Reinforcement

Explore resources related to reinforcement to help implement AI solutions for your business.

What Reinforcement Learning Method Should I Use for Poker AI with LLMs?

Hey everyone, I’m working on a poker AI project, where I’m training a large language model (LLM) to predict poker actions from given game states (check, call, bet, raise, etc.). My end goal is to create a model that can play poker at a high level, primarily by self-play and opponent modeling. However, I’m running into some challenges that I hope you can help me with! Here's the situation: Training Method: I’m using supervised fine-tuning (SFT) on real poker hand history data to initially teach the LLM how to predict poker actions from game states. This means that the model learns from examples of past games, predicting the actions that players took in various situations. Self-Play Setup: I plan to eventually move to self-play, where the LLM will play against itself (or other types of models that I create to simulate different play styles). I’ll use these self-play sessions to improve the model over time. Opponent Pool: I’m creating 6 types of poker players (Loose Aggressive, Loose Passive, Tight Aggressive, Tight Passive, Maniac, and Nit), each trained at 5 different skill levels (Novice, Beg\*nner, Intermediate, Advanced, Expert). This gives me a decent range of opponent behavior for training. The problem: Here’s the catch: The LLM I’m using only outputs discrete actions (e.g., bet 3BB, raise to 10BB, etc.) with no access to the probabilities of actions, so I can't directly use methods like policy gradients or Q-learning that rely on action probabilities or continuous action spaces. This makes applying traditional RL methods a bit tricky. My question: Given that I don't have access to action probabilities, what RL method or strategy should I pursue to improve my model? Specifically, I’m looking for a way to: Incorporate self-play with reward-based learning. Refine the model through reinforcement learning, without the need for continuous probabilities. Ensure the model doesn’t just overfit to its own prior behavior but learns to adapt and exploit different strategies in poker. I’ve considered a few approaches like reward-weighted supervised fine-tuning or using simpler RL techniques like Monte Carlo updates, but I’m not sure which would work best with the LLM setup I have. I've also considered Q-learning or Deep Q-learning. Any advice or suggestions on which RL approach I should take given my situation would be greatly appreciated! Yes I used AI to write this queston. But it captures everything I want to say, and I suck at writing.

#reddit #reinforcement #learning

[P] Building an Reinforcement Learning Agent to play The Legend of Zelda

DarkAutumn•This week

[P] Building an Reinforcement Learning Agent to play The Legend of Zelda

A year go I started trying to use PPO to play the original Legend of Zelda, and I was able to train a model to beat the first boss after a few months of work. I wanted to share the project just for show and tell. I'd love to hear feedback and suggestions as this is just a hobby project. I don't do this for a living. The code for that lives in the original-design branch of my Triforce repo. I'm currently tinkering with new designs so the main branch is much less stable. Here's a video of the agent beating the first dungeon, which was trained with 5,000,000+ steps. At 38 seconds, you can see it learned that it's invulnerable at the screen edge, and it exploits that to avoid damage from a projectile. At 53 seconds it steps up to avoid damage from an unblockable projectile, even though it takes a -0.06 penalty for moving the wrong way (taking damage would be a larger penalty.) At 55 seconds it walks towards the rock projectile to block it. And so on, lots of little things the model does is easy to miss if you don't know the game inside and out. As a TLDR, here's an early version of my new (single) model. This doesn't make it quite as far, but if you watch closely it's combat is already far better, and is only trained on 320,000 steps (~6% of the steps the first model was trained on). This is pretty far along from my very first model. Original Design I got the original project working using stable-baselines's PPO and default neural network (Shared NatureCNN, I believe). SB was great to get started but ultimately stifling. In the new version of the project I've implemented PPO from scratch with torch with my own simple neural network similar to stable-baseline's default. I'm playing with all kinds of changes and designs now that I have more flexibility and control. Here is my rough original design: Overall Strategy My first pass through this project was basically "imagine playing Zelda with your older sibling telling you where to go and what to do". I give the model an objective vector which points to where I want it to go on the screen (as a bird flies, the agent still had to learn path finding to avoid damage and navigate around the map). This includes either point at the nearest enemy I want it to kill or a NSEW vector if it's supposed to move to the next room. Due a few limitations with stable-baselines (especially around action masking), I ended up training unique models for traversing the overworld vs the dungeon (since they have entirely different tilesets). I also trained a different model for when we have sword beams vs not. In the video above you can see what model is being used onscreen. In my current project I've removed this objective vector as it felt too much like cheating. Instead I give it a one-hot encoded objective (move north to the next room, pickup items, kill enemies, etc). So far it's working quite well without that crutch. The new project also does a much better job of combat even without multiple models to handle beams vs not. Observation/Action Space Image - The standard neural network had a really tough time being fed the entire screen. No amount of training seemed to help. I solved this by creating a viewport around Link that keeps him centered. This REALLY helped the model learn. I also had absolutely zero success with stacking frames to give Link a way to see enemy/projectile movement. The model simply never trained with stable-baselines when I implemented frame stacking and I never figured out why. I just added it to my current neural network and it seems to be working... Though my early experiments show that giving it 3 frames (skipping two in between, so frames curr, curr-3, curr-6) doesn't really give us that much better performance. It might if I took away some of the vectors. We'll see. Vectors - Since the model cannot see beyond its little viewport, I gave the model a vector to the closest item, enemy, and projectile onscreen. This made it so the model can shoot enemies across the room outside of its viewport. My new model gives it multiple enemies/items/projectiles and I plan to try to use an attention mechanism as part of the network to see if I can just feed it all of that data. Information - It also gets a couple of one-off datapoints like whether it currently has sword beams. The new model also gives it a "source" room (to help better understand dungeons where we have to backtrack), and a one-hot encoded objective. Action Space My original project just has a few actions, 4 for moving in the cardinal directions and 4 for attacking in each direction (I also added bombs but never spent any time training it). I had an idea to use masking to help speed up training. I.E. if link bumps into a wall, don't let him move in that direction again until he moves elsewhere, as the model would often spend an entire memory buffer running headlong straight into a wall before an update...better to do it once and get a huge negative penalty which is essentially the same result but faster. Unfortunately SB made it really annoying architecturally to pass that info down to the policy layer. I could have hacked it together, but eventually I just reimplemented PPO and my own neural network so I could properly mask actions in the new version. For example, when we start training a fresh model, it cannot attack when there aren't enemies on screen and I can disallow it from leaving certain areas. The new model actually understands splitting swinging the sword short range vs firing sword beams as two different actions, though I haven't yet had a chance to fully train with the split yet. Frameskip/Cooldowns - In the game I don't use a fixed frame skip for actions. Instead I use the internal ram state of game to know when Link is animation locked or not and only allow the agent to take actions when it's actually possible to give meaningful input to the game. This greatly sped up training. We also force movement to be between tiles on the game map. This means that when the agent decides to move it loses control for longer than a player would...a player can make more split second decisions. This made it easier to implement movement rewards though and might be something to clean up in the future. Other interesting details Pathfinding - To facilitate rewards, the original version of this project used A* to pathfind from link to what he should be doing. Here's a video of it in action. This information wasn't giving to the model directly but instead the agent would only be given the rewards if it exactly followed that path or the transposed version of it. It would also pathfind around enemies and not walk through them. This was a nightmare though. The corner cases were significant, and pushing Link towards enemies but not into them was really tricky. The new verison just uses a wavefront algorithm. I calculate a wave from the tiles we want to get to outwards, then make sure we are following the gradient. Also calculating the A* around enemies every frame (even with caching) was super slow. Wavefront was faster, especially because I give the new model no special rewards for walking around enemies...faster to compute and it has to learn from taking damage or not. Either way, the both the old and new models successfully learned how to pathfind around danger and obstacles, with or without the cheaty objective vector. Rewards - I programmed very dense rewards in both the old and new model. At basically every step, the model is getting rewarded or punished for something. I actually have some ideas I can't wait to try out to make the rewards more sparse. Or maybe we start with dense rewards for the first training, then fine-tune the model with sparser rewards. We'll see. Predicting the Future - Speaking of rewards. One interesting wrinkle is that the agent can do a lot of things that will eventually deal damage but not on that frame. For example, when Link sets a bomb it takes several seconds before it explodes, killing things. This can be a massive reward or penalty since he spent an extremely valuable resource, but may have done massive damage. PPO and other RL propagates rewards backwards, of course, but that spike in reward could land on a weird frame where we took damage or moved in the wrong direction. I probably could have just not solved that problem and let it shake out over time, but instead I used the fact that we are in an emulator to just see what the outcome of every decision is. When planting a bomb, shooting sword beams, etc, we let the game run forward until impact, then rewind time and reward the agent appropriately, continuing on from when we first paused. This greatly speeds up training, even if it's expensive to do this savestate, play forward, restore state. Neural Networks - When I first started this project (knowing very little about ML and RL), I thought most of my time would be tuning the shape of the neural network that we are using. In reality, the default provided by stable-baselines and my eventual reimplemnentation has been enough to make massive progress. Now that I have a solid codebase though, I really want to revisit this. I'd like to see if trying CoordConvs and similar networks might make the viewport unncessary. Less interesting details/thoughts Hyperparameters - Setting the entropy coefficinet way lower helped a TON in training stable models. My new PPO implementation is way less stable than stable-baselines (ha, imagine that), but still converges most of the time. Infinite Rewards - As with all reinforcement learning, if you give some way for the model to get infinite rewards, it will do just that and nothing else. I spent days, or maybe weeks tweaking reward functions to just get it to train and not find a spot on the wall it could hump for infinite rewards. Even just neutral rewards, like +0.5 moving forward and -0.5 for moving backwards, would often result in a model that just stepped left, then right infinitely. There has to be a real reward or punishment (non-neutral) for forward progress. Debugging Rewards - In fact, building a rewards debugger was the only way I made progress in this project. If you are tackling something this big, do that very early. Stable-Retro is pretty great - Couldn't be happier with the clean design for implementing emulation for AI. Torch is Awesome - My early versions heavily used numpy and relied on stable-baselines, with its multiproc parallelization support. It worked great. Moving the project over to torch was night and day though. It gave me so much more flexibility, instant multithreading for matrix operations. I have a pretty beefy computer and I'm almost at the same steps per second as 20 proc stable-retro/numpy. Future Ideas This has already gone on too long. I have some ideas for future projects, but maybe I'll just make them another post when I actually do them. Special Thanks A special thanks to Brad Flaugher for help with the early version of this, Fiskbit from the Zelda1 speedrunning community for help pulling apart the raw assembly to build this thing, and MatPoliquin for maintaining Stable-Retro. Happy to answer any questions, really I just love nerding out about this stuff.

#reddit #building #reinforcement

[N] 20 hours of new lectures on Deep Learning and Reinforcement Learning with lots of examples

cwkx•This week

[N] 20 hours of new lectures on Deep Learning and Reinforcement Learning with lots of examples

If anyone's interested in a Deep Learning and Reinforcement Learning series, I uploaded 20 hours of lectures on YouTube yesterday. Compared to other lectures, I think this gives quite a broad/compact overview of the fields with lots of minimal examples to build on. Here are the links: Deep Learning (playlist) The first five lectures are more theoretical, the second half is more applied. Lecture 1: Introduction. (slides, video) Lecture 2: Mathematical principles and backpropagation. (slides, colab, video) Lecture 3: PyTorch programming: coding session*. (colab1, colab2, video) - minor issues with audio, but it fixes itself later. Lecture 4: Designing models to generalise. (slides, video) Lecture 5: Generative models. (slides, desmos, colab, video) Lecture 6: Adversarial models. (slides, colab1, colab2, colab3, colab4, video) Lecture 7: Energy-based models. (slides, colab, video) Lecture 8: Sequential models: by* u/samb-t. (slides, colab1, colab2, video) Lecture 9: Flow models and implicit networks. (slides, SIREN, GON, video) Lecture 10: Meta and manifold learning. (slides, interview, video) Reinforcement Learning (playlist) This is based on David Silver's course but targeting younger students within a shorter 50min format (missing the advanced derivations) + more examples and Colab code. Lecture 1: Foundations. (slides, video) Lecture 2: Markov decision processes. (slides, colab, video) Lecture 3: OpenAI gym. (video) Lecture 4: Dynamic programming. (slides, colab, video) Lecture 5: Monte Carlo methods. (slides, colab, video) Lecture 6: Temporal-difference methods. (slides, colab, video) Lecture 7: Function approximation. (slides, code, video) Lecture 8: Policy gradient methods. (slides, code, theory, video) Lecture 9: Model-based methods. (slides, video) Lecture 10: Extended methods. (slides, atari, video)

#reddit #hours #lectures

[D] What is your honest experience with reinforcement learning?

Starks-Technology•This week

[D] What is your honest experience with reinforcement learning?

In my personal experience, SOTA RL algorithms simply don't work. I've tried working with reinforcement learning for over 5 years. I remember when Alpha Go defeated the world famous Go player, Lee Sedol, and everybody thought RL would take the ML community by storm. Yet, outside of toy problems, I've personally never found a practical use-case of RL. What is your experience with it? Aside from Ad recommendation systems and RLHF, are there legitimate use-cases of RL? Or, was it all hype? Edit: I know a lot about AI. I built NexusTrade, an AI-Powered automated investing tool that lets non-technical users create, update, and deploy their trading strategies. I’m not an idiot nor a noob; RL is just ridiculously hard. Edit 2: Since my comments are being downvoted, here is a link to my article that better describes my position. It's not that I don't understand RL. I released my open-source code and wrote a paper on it. It's the fact that it's EXTREMELY difficult to understand. Other deep learning algorithms like CNNs (including ResNets), RNNs (including GRUs and LSTMs), Transformers, and GANs are not hard to understand. These algorithms work and have practical use-cases outside of the lab. Traditional SOTA RL algorithms like PPO, DDPG, and TD3 are just very hard. You need to do a bunch of research to even implement a toy problem. In contrast, the decision transformer is something anybody can implement, and it seems to match or surpass the SOTA. You don't need two networks battling each other. You don't have to go through hell to debug your network. It just naturally learns the best set of actions in an auto-regressive manner. I also didn't mean to come off as arrogant or imply that RL is not worth learning. I just haven't seen any real-world, practical use-cases of it. I simply wanted to start a discussion, not claim that I know everything. Edit 3: There's a shockingly number of people calling me an idiot for not fully understanding RL. You guys are wayyy too comfortable calling people you disagree with names. News-flash, not everybody has a PhD in ML. My undergraduate degree is in biology. I self-taught myself the high-level maths to understand ML. I'm very passionate about the field; I just have VERY disappointing experiences with RL. Funny enough, there are very few people refuting my actual points. To summarize: Lack of real-world applications Extremely complex and inaccessible to 99% of the population Much harder than traditional DL algorithms like CNNs, RNNs, and GANs Sample inefficiency and instability Difficult to debug Better alternatives, such as the Decision Transformer Are these not legitimate criticisms? Is the purpose of this sub not to have discussions related to Machine Learning? To the few commenters that aren't calling me an idiot...thank you! Remember, it costs you nothing to be nice! Edit 4: Lots of people seem to agree that RL is over-hyped. Unfortunately those comments are downvoted. To clear up some things: We've invested HEAVILY into reinforcement learning. All we got from this investment is a robot that can be super-human at (some) video games. AlphaFold did not use any reinforcement learning. SpaceX doesn't either. I concede that it can be useful for robotics, but still argue that it's use-cases outside the lab are extremely limited. If you're stumbling on this thread and curious about an RL alternative, check out the Decision Transformer. It can be used in any situation that a traditional RL algorithm can be used. Final Edit: To those who contributed more recently, thank you for the thoughtful discussion! From what I learned, model-based models like Dreamer and IRIS MIGHT have a future. But everybody who has actually used model-free models like DDPG unanimously agree that they suck and don’t work.

#reddit #your #honest

[R] Reinforcement Learning for Sequential Decision and Optimal Control

isfjzzz•This week

[R] Reinforcement Learning for Sequential Decision and Optimal Control

Since early 21st century, artificial intelligence (AI) has been reshaping almost all areas of human society, which has high potential to spark the fourth industrial revolution. Notable examples can be found in the sector of road transportation, where AI has drastically changed automobile design and traffic management. Many new technologies, such as driver assistance, autonomous driving, and cloud-based cooperation, are emerging at an unbelievable speed. These new technologies have the potential to significantly improve driving ability, reduce traffic accidents, and relieve urban congestion. As one of the most important AI branches, reinforcement learning (RL) has attracted increasing attention in recent years. RL is an interdisciplinary field of trial-and-error learning and optimal control, which promises to provide optimal solutions for decision-making or control in large-scale and complex dynamic processes. One of its most conspicuous successes is AlphaGo from Google DeepMind, which has beaten the highest-level professional human player. The underlying key technology is the so-called deep reinforcement learning, which equips AlphaGo with amazing self-evolution ability and high playing intelligence. Despite a few successes, the application of RL is still in its infancy because most RL algorithms are rather difficult to comprehend and implement. RL connects deeply with statistical learning and convex optimization, and involves a wide range of new concepts and theories. As a beginner, one must undergo a long and tedious learning process to become an RL master. Without fully understanding those underlying principles, it is very difficult for new users to make proper adjustments to achieve the best application performance.  https://preview.redd.it/tggt6o3o481c1.jpg?width=248&format=pjpg&auto=webp&s=75e2b58ac8da9273f2511a4fe37ef508d86a6e96 Reference： Shengbo Eben Li, Reinforcement Learning for Sequential Decision and Optimal Control. Springer Verlag, Singapore, 2023 Website of e-book: https://link.springer.com/book/10.1007/978-981-19-7784-8  QR code to Springer Book contents This book aims to provide a systematic introduction to fundamental RL theories, mainstream RL algorithms and typical RL applications for researchers and engineers. The main topics include Markov decision processes, Monte Carlo learning, temporal difference learning, RL with function approximation, policy gradient method, approximate dynamic programming, deep reinforcement learning, etc. Chapter 1 provides an overview of RL, including its history, famous scholars, successful examples and up-to-date challenges. Chapter 2 discusses the basis of RL, including main concepts and terminologies, Bellman’s optimality condition, and general problem formulation. Chapter 3 introduces Monte Carlo learning methods for model-free RL, including on-policy/off-policy methods and importance sampling technique. Chapter 4 introduces temporal difference learning methods for model-free RL, including Sarsa, Q-learning, and expected Sarsa. Chapter 5 introduces stochastic dynamic programming (DP), i.e., model-based RL with tabular representation, including value iteration DP, policy iteration DP and their convergence mechanisms. Chapter 6 introduces how to approximate policy and value functions in indirect RL methods as well as the associated actor-critic architecture. Chapter 7 derives different kinds of direct policy gradients, including likelihood ratio gradient, natural policy gradient and a few advanced variants. Chapter 8 introduces infinite-horizon ADP, finite-horizon ADP and its connection with model predictive control. Chapter 9 discusses how to handle state constraints and its connection with feasibility and safety, as well as the newly proposed actor-critic-scenery learning architecture. Chapter 10 is devoted to deep reinforcement learning, including how to train artificial neural networks and typical deep RL algorithms such as DQN, DDPG, TD3, TRPO, PPO, SAC, and DSAC. Chapter 11 provides various RL topics,including robust RL, POMDP, multi-agent RL, meta-RL, inverse RL, offline RL, major RL libraries and platforms. Author information: Shengbo Eben Li is currently a professor at Tsinghua University in the interdisciplinary field of autonomous driving and artificial intelligence. Before joining Tsinghua University, he has worked at Stanford University, University of Michigan, and UC Berkeley. His active research interests include intelligent vehicles and driver assistance, deep reinforcement learning, optimal control and estimation, etc. He has published more than 130 peer-reviewed papers in top-tier international journals and conferences. He is the recipient of best paper awards (finalists) of IEEE ITSC, ICCAS, IEEE ICUS, IEEE IV, L4DC, etc. He has received a number of important academic honors, including National Award for Technological Invention of China (2013), National Award for Progress in Sci & Tech of China (2018), Distinguished Young Scholar of Beijing NSF (2018), Youth Sci & Tech Innovation Leader from MOST China (2020), etc. He also serves as Board of Governor of IEEE ITS Society, Senior AE of IEEE OJ ITS, and AEs of IEEE ITSM, IEEE Trans ITS, Automotive Innovation, etc.

#reddit #reinforcement #learning

Starks-Technology•This week

[D] What is your honest experience with reinforcement learning?

#reddit #your #honest

Starks-Technology•This week

[D] What is your honest experience with reinforcement learning?

#reddit #your #honest

[D] What kind of reinforcement learning algorithm would you recommend for training AI for a strategy game like Risk or Axis&Allies?

anti-hero•This week

[D] What kind of reinforcement learning algorithm would you recommend for training AI for a strategy game like Risk or Axis&Allies?

The challenge being multiple stages of the game (buy, move/attack, place...) and dice rolls.

#reddit #kind #reinforcement

Solo technical founders, has not having a business cofounder even screwed you up? "I will not promote"

-1

JustZed32•This week

Solo technical founders, has not having a business cofounder even screwed you up? "I will not promote"

I’m creating an AI startup; a reinforcement learning model designed to do a certain thing in a big field. I’ve basically developed code for a prototype (I hope to start training in two weeks) I do believe YC advice for taking a cofounder, but risk of getting a bad one is too high. In my life I also haven't met people with my grit, at least from my countries, so I'll have to "date down", I guess. At least in technical sense. So, has not having a business cofounder actually screwed anyone up badly? (Looking at people who are doing ventures with $20m+ potential/real ARR,)

#reddit #solo #technical

TechPrimo•This week

How I Started Learning Machine Learning

Hello, everyone. As promised, I'll write a longer post about how I entered the world of ML, hoping it will help someone shape their path. I'll include links to all the useful materials I used alongside the story, which you can use for learning. I like to call myself an AI Research Scientist who enjoys exploring new AI trends, delving deeper into understanding their background, and applying them to real products. This way, I try to connect science and entrepreneurship because I believe everything that starts as scientific research ends up "on the shelves" as a product that solves a specific user problem. I began my journey in ML in 2016 when it wasn't such a popular field. Everyone had heard of it, but few were applying it. I have several years of development experience and want to try my hand at ML. The first problem I encountered was where to start - whether to learn mathematics, statistics, or something else. That's when I came across a name and a course that completely changed my career. Let's start You guessed it. It was Professor Andrew Ng and his globally popular Machine Learning course available on Coursera (I still have the certificate, hehe). This was also my first official online course ever. Since that course no longer exists as it's been replaced by a new one, I recommend you check out: Machine Learning (Stanford CS229) Machine Learning Specialization These two courses start from the basics of ML and all the necessary calculus you need to know. Many always ask questions like whether to learn linear algebra, statistics, or probability, but you don't need to know everything in depth. This knowledge helps if you're a scientist developing a new architecture, but as an engineer, not really. You need to know some basics to understand, such as how the backpropagation algorithm works. I know that Machine Learning (Stanford CS229) is a very long and arduous course, but it's the right start if you want to be really good at ML. In my time, I filled two thick notebooks by hand while taking the course mentioned above. TensorFlow and Keras After the course, I didn't know how to apply my knowledge because I hadn't learned specifically how to code things. Then, I was looking for ways to learn how to code it. That's when I came across a popular framework called Keras, now part of TensorFlow. I started with a new course and acquiring practical knowledge: Deep Learning Specialization Deep Learning by Ian Goodfellow Machine Learning Yearning by Andrew Ng These resources above were my next step. I must admit that I learned the most from that course and from the book Deep Learning by Ian Goodfellow because I like reading books (although this one is quite difficult to read). Learn by coding To avoid just learning, I went through various GitHub repositories that I manually retyped and learned that way. It may be an old-fashioned technique, but it helped me a lot. Now, most of those repositories don't exist, so I'll share some that I found to be good: Really good Jupyter notebooks that can teach you the basics of TensorFlow Another good repo for learning TF and Keras Master the challenge After mastering the basics in terms of programming in TF/Keras, I wanted to try solving some real problems. There's no better place for that challenge than Kaggle and the popular Titanic dataset. Here, you can really find a bunch of materials and simple examples of ML applications. Here are some of my favorites: Titanic - Machine Learning from Disaster Home Credit Default Risk House Prices - Advanced Regression Techniques Two Sigma: Using News to Predict Stock Movements I then decided to further develop my career in the direction of applying ML to the stock market, first using predictions on time series and then using natural language processing. I've remained in this field until today and will defend my doctoral dissertation soon. How to deploy models To continue, before I move on to the topic of specialization, we need to address the topic of deployment. Now that we've learned how to make some basic models in Keras and how to use them, there are many ways and services, but I'll only mention what I use today. For all my ML models, whether simple regression models or complex GPT models, I use FastAPI. It's a straightforward framework, and you can quickly create API endpoints. I'll share a few older and useful tutorials for beginners: AI as an API tutorial series A step-by-step guide Productizing an ML Model with FastAPI and Cloud Run Personally, I've deployed on various cloud providers, of which I would highlight GCP and AWS because they have everything needed for model deployment, and if you know how to use them, they can be quite cheap. Chose your specialization The next step in developing my career, besides choosing finance as the primary area, was my specialization in the field of NLP. This happened in early 2020 when I started working with models based on the Transformer architecture. The first model I worked with was BERT, and the first tasks were related to classifications. My recommendations are to master the Transformer architecture well because 99% of today's LLM models are based on it. Here are some resources: The legendary paper "Attention Is All You Need" Hugging Face Course on Transformers Illustrated Guide to Transformers - Step by Step Explanation Good repository How large language models work, a visual intro to transformers After spending years using encoder-based Transformer models, I started learning GPT models. Good open-source models like Llama 2 then appear. Then, I started fine-tuning these models using the excellent Unsloth library: How to Finetune Llama-3 and Export to Ollama Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth After that, I focused on studying various RAG techniques and developing Agent AI systems. This is now called AI engineering, and, as far as I can see, it has become quite popular. So I'll write more about that in another post, but here I'll leave what I consider to be the three most famous representatives, i.e., their tutorials: LangChain tutorial LangGraph tutorial CrewAI examples Here I am today Thanks to the knowledge I've generated over all these years in the field of ML, I've developed and worked on numerous projects. The most significant publicly available project is developing an agent AI system for well-being support, which I turned into a mobile application. Also, my entire doctoral dissertation is related to applying ML to the stock market in combination with the development of GPT models and reinforcement learning (more on that in a separate post). After long 6 years, I've completed my dissertation, and now I'm just waiting for its defense. I'll share everything I'm working on for the dissertation publicly on the project, and in tutorials I'm preparing to write. If you're interested in these topics, I announce that I'll soon start with activities of publishing content on Medium and a blog, but I'll share all of that here on Reddit as well. Now that I've gathered years of experience and knowledge in this field, I'd like to share it with others and help as much as possible. If you have any questions, feel free to ask them, and I'll try to answer all of them. Thank you for reading.

#reddit #started #learning

Are Genetic Algorithms Still Relevant in 2025?

Baby-Boss0506•This week

Are Genetic Algorithms Still Relevant in 2025?

Hey everyone, I was first introduced to Genetic Algorithms (GAs) during an Introduction to AI course at university, and I recently started reading "Genetic Algorithms in Search, Optimization, and Machine Learning" by David E. Goldberg. While I see that GAs have been historically used in optimization problems, AI, and even bioinformatics, I’m wondering about their practical relevance today. With advancements in deep learning, reinforcement learning, and modern optimization techniques, are they still widely used in research and industry?I’d love to hear from experts and practitioners: In which domains are Genetic Algorithms still useful today? Have they been replaced by more efficient approaches? If so, what are the main alternatives? Beyond Goldberg’s book, what are the best modern resources (books, papers, courses) to deeply understand and implement them in real-world applications? I’m currently working on a hands-on GA project with a friend, and we want to focus on something meaningful rather than just a toy example.

#reddit #genetic #algorithms

TOP ML University Courses for beginners (FREE)

Attitude_Alone•This week

TOP ML University Courses for beginners (FREE)

UC Berkeley UC Berkeley Full Stack Playlist - YouTube Stanford University STEFANO ERMON's Deep Generative Model classes - (Highly Recommended) Stanford CS234: Reinforcement Learning Spring 2024 - Playlist 1 Stanford CS234: Reinforcement Learning Spring 2024 - Playlist 2 Stanford XCS224U: Natural Language Understanding Spring 2023 - Playlist Stanford CS229M: Machine Learning Theory Fall 2021 - Playlist Stanford CS25 - Transformers United ML with Graphs MIT MIT 6.S191: Introduction to Deep Learning Harvard NLP Harvard NLP Code - Official Website CU Boulder AI BY HAND Cornell Tech ML Models UMICH DL for CV

#reddit #university #courses

TechPrimo•This week

How I Started Learning Machine Learning

#reddit #started #learning

Strategies for training AI players on asymmetric games?

computing_professor•This week

Strategies for training AI players on asymmetric games?

I'm familiar with things like self-play in training a player for games like Chess, Checkers, and many other games where move sets and goals are the same for both players - e.g. eliminate the other player's pieces, prevent the other player from moving (like in Amazons, for example), etc. But what about training a computer player for a game where one side has a different goal than the other? Thing like Maker-Breaker Games. I imagine I would want to train a strong Maker and then train a strong Breaker. So it seems like like an application of Reinforcement Learning and more like a Generative Adversarial Network. Does anyone have any reading on these types of problems? Thanks. Additionally, I'm looking to build a home computer with some funds I came into recently to help me train models (I can't depend on cloud services since the money is only available for a short time), and my budget is pretty big for a home system. Where should I go looking for something that is relatively quiet, usable, doesn't raise my electricity bill too high, and is still powerful enough to train models for years to come? I've looked into /r/buildapcforme but folks seem more focused on gaming over there. And I'm pretty hardware ignorant.

#reddit #strategies #training

ZeroToHeroML: Beginner-Friendly ML & AI Course (Free)

DizD•This week

ZeroToHeroML: Beginner-Friendly ML & AI Course (Free)

Hey r/learnmachinelearning! A friend of mine, who's been a software developer at Sony for 10 years, recently expressed interest in learning Machine Learning (ML) and Artificial Intelligence (AI). Leveraging my background in ML and neural computation (learned at UCSD) to create a beginner-friendly course guiding him through the basics and into more complex projects. Foundational Concepts: Predicting House Prices (Regression): Master regression techniques to forecast housing prices based on various factors. Iris Flower Species Prediction (Classification): Learn classification algorithms by predicting flower species using the famous Iris dataset. Overcoming Overfitting: Explore methods to prevent models from overfitting and enhance their generalizability. In Progress: Customer Segmentation (Unsupervised Learning): Delve into unsupervised learning to group customers based on purchase history or demographics (valuable for targeted marketing campaigns). Deep Learning for Image Recognition: Implement Convolutional Neural Networks (CNNs) to build models that recognize objects or scenes in images. Natural Language Processing Sentiment Analysis: Analyze the sentiment (positive, negative, or neutral) expressed in text data (e.g., reviews, social media posts) using NLP techniques. Introduction to Reinforcement Learning: Get acquainted with the fundamentals of reinforcement learning by creating an agent that learns to navigate a maze. Want to Learn or Contribute? I thought I'd share ZeroToHeroML here so others who want to learn ML/AI or know someone who does can benefit from this free resource!  Fork the repo: https://github.com/DilrajS/ZeroToHeroML Share with others interested in ML/AI! Pull requests welcome (help the community grow!). All help is appriciated! Let's conquer ML/AI together!

#reddit #zerotoheroml:#beginner-friendly

Teaching an AI to Play Mario: A Learning Journey

CivilLifeguard189•This week

Teaching an AI to Play Mario: A Learning Journey

TLDR: I've always wanted to learn reinforcement learning, but the notation and concepts often seemed overwhelming (and scary). So, \~3 months ago, I set myself a challenge: Train an AI to Speedrun Mario Watch the progression here: https://youtu.be/OQitI066aI0  Full Story: Three months ago, I stared at the dense forest of Reinforcement Learning (RL) papers and felt like Mario facing Bowser for the first time: unequipped and overwhelmingly outmatched. The notation seemed like hieroglyphics, and terms like "policy gradients" felt like they belonged in a sci-fi novel, not a beginner's project. But RL always seemed so cool, and I was really determined to achieve my goal. So, I started with the Sutton & Barto RL textbook, learning things like the Multi-Armed Bandit problem and MDPs working my way up to Actor-Critic methods. That book is literal gold & I highly recommend you work through it (even though it can be tough at times). I tried everything from random courses online to books on amazon & this textbook has been by far the most clear and effective way to learn RL. The biggest issue with the textbook is you learn a lot of theory, but don't learn implementation. So, I would go through a chapter a week & set aside Friday + the weekend to actually implement what I learned (usually by watching youtube tutorials & looking at Github Repos). Eventually, while searching for practical resources for implementing PPO, I stumbled upon a GitHub repository that literally trained an AI to play Mario. Rather than just cloning and running the code, I took a deeper approach. I aimed to understand the repository thoroughly, ensuring each line of code made sense in the context of what I had studied. But of course, this wasn't easy. One of the biggest issues was my hardware limitation. I was working on an old Mac. So, I started using Google Collab, but that had its own problems (session timeouts & limited GPU access). Ultimately, I found AWS Sagemaker to be pretty good.  After rewriting the code, I felt confident it would work because I understood every aspect of it. So, I trained the AI to play Mario across a variety of different levels (took a long time and a lot of trial and error with the learning rate). It feels amazing seeing your theoretical knowledge translate into tangible results & this project gave me a big confidence boost.  Anyways I made a video showing off the results (Note that I simplified the technical parts for it to reach a wider audience): https://youtu.be/OQitI066aI0  Feel free to drop any questions or feedback, I'm more than happy to help or chat about my experiences. I hope my journey can inspire some of you who might be feeling overwhelmed with the idea of diving into reinforcement learning or any other area of AI. Remember, the hardest part is often taking the first step. Once you start, the momentum will carry you forward. Thank you for reading my super long post and sharing in my little success story! 🚀🕹️🎮

#reddit #teaching #play

Best AI/ML course for Beginners to advanced - recommendations?

Afreen19•This week

Best AI/ML course for Beginners to advanced - recommendations?

Hey everyone, I’m looking for some solid AI/ML courses that cover everything from the basics to advanced topics. I want a structured learning path that helps me understand fundamental concepts like linear regression, neural networks, and deep learning, all the way to advanced topics like transformers, reinforcement learning, and real-world applications. Ideally, the course(s) should: • Be beginner-friendly but progress to advanced topics • Have practical, hands-on projects • Cover both theory and implementation (Python, TensorFlow, PyTorch, etc.) • Be well-structured and up to date I’m open to free and paid options (Coursera, Udemy, YouTube, etc.). What are some of the best courses you’d recommend? Thanks in advance!

#reddit #best #ai/ml

Seeking Guidance on Implementing LLM and Generative AI in a New Role.

NoConsideration4473•This week

Seeking Guidance on Implementing LLM and Generative AI in a New Role.

Hi everyone, I recently got a job that involves implementing LLM and Generative AI. During the interview, we discussed topics like Reinforcement Learning, Deep Learning, and Machine Learning. I’m finishing my MSc in Computer Science, where I’ve worked on projects involving simple Deep Learning implementations (data extraction, cleaning some MLP). Essentially, I will have access to servers for computing and a laptop, and I’ll be the sole programmer responsible for establishing and demonstrating value. I am looking for resources and maybe some examples of how to do that so far I have recommended: Hugging Face - Documentation Intro to Large Language Models by Andrej Karpathy I also plan to attend local meetups about LLM and PyTorch. I’m looking for practical advice on learning these technologies while developing them, particularly regarding development workflows, cloud backups, and coding standards, as I will be working independently. I know if I can achieve something easier and faster with a simple Machine Learning model I will do that. Any help would be greatly appreciated.

#reddit #seeking #guidance

JuergenSchmidhuber•This week

I am Jürgen Schmidhuber, AMA!

Hello /r/machinelearning, I am Jürgen Schmidhuber (pronounce: You_again Shmidhoobuh) and I will be here to answer your questions on 4th March 2015, 10 AM EST. You can post questions in this thread in the meantime. Below you can find a short introduction about me from my website (you can read more about my lab’s work at people.idsia.ch/~juergen/). Edits since 9th March: Still working on the long tail of more recent questions hidden further down in this thread ... Edit of 6th March: I'll keep answering questions today and in the next few days - please bear with my sluggish responses. Edit of 5th March 4pm (= 10pm Swiss time): Enough for today - I'll be back tomorrow. Edit of 5th March 4am: Thank you for great questions - I am online again, to answer more of them! Since age 15 or so, Jürgen Schmidhuber's main scientific ambition has been to build an optimal scientist through self-improving Artificial Intelligence (AI), then retire. He has pioneered self-improving general problem solvers since 1987, and Deep Learning Neural Networks (NNs) since 1991. The recurrent NNs (RNNs) developed by his research groups at the Swiss AI Lab IDSIA (USI & SUPSI) & TU Munich were the first RNNs to win official international contests. They recently helped to improve connected handwriting recognition, speech recognition, machine translation, optical character recognition, image caption generation, and are now in use at Google, Microsoft, IBM, Baidu, and many other companies. IDSIA's Deep Learners were also the first to win object detection and image segmentation contests, and achieved the world's first superhuman visual classification results, winning nine international competitions in machine learning & pattern recognition (more than any other team). They also were the first to learn control policies directly from high-dimensional sensory input using reinforcement learning. His research group also established the field of mathematically rigorous universal AI and optimal universal problem solvers. His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He also generalized algorithmic information theory and the many-worlds theory of physics, and introduced the concept of Low-Complexity Art, the information age's extreme form of minimal art. Since 2009 he has been member of the European Academy of Sciences and Arts. He has published 333 peer-reviewed papers, earned seven best paper/best video awards, and is recipient of the 2013 Helmholtz Award of the International Neural Networks Society.

#reddit #jürgen #schmidhuber,

actbsh•This week

[D] Advanced courses update

EDIT Jan 2021 : I am still updating the list as of Jan, 2021 and will most probably continue to do so for foreseeable future. So, please feel free to message me any courses you find interesting that fit here. - - We have a PhD level or Advanced courses thread in the sidebar but it's three year old now. There were two other 7-8 month old threads (1, 2) but they don't have many quality responses either. So, can we have a new one here? To reiterate - CS231n, CS229, ones from Udemy etc are not advanced. Advanced ML/DL/RL, attempts at building theory of DL, optimization theory, advanced applications etc are some examples of what I believe should belong here, much like the original sidebar post. You can also suggest (new) categories for the courses you share. :) - - Here are some courses we've found so far. ML >> Learning Discrete Latent Structure - sta4273/csc2547 Spring'18 Learning to Search - csc2547 Fall'19 Scalable and Flexible Models of Uncertainty - csc2541 Fundamentals of Machine Learning Over Networks - ep3260 Machine Learning on Graphs - cs224w, videos Mining Massive Data Sets - cs246 Interactive Learning - cse599 Machine Learning for Sequential Decision Making Under Uncertainty - ee290s/cs194 Probabilistic Graphical Methods - 10-708 Introduction to Causal Inference ML >> Theory Statistical Machine Learning - 10-702/36-702 with videos, 2016 videos Statistical Learning Theory - cs229T/stats231 Stanford Autumn'18-19 Statistical Learning Theory - cs281b /stat241b UC Berkeley, Spring'14 Statistical Learning Theory - csc2532 Uni of Toronto, Spring'20 ML >> Bayesian Bayesian Data Analysis Bayesian Methods Research Group, Moscow, Bayesian Methods in ML - spring2020, fall2020 Deep Learning and Bayesian Methods - summer school, videos available for 2019 version ML >> Systems and Operations Stanford MLSys Seminar Series Visual Computing Systems- cs348v - Another systems course that discusses hardware from a persepective of visual computing but is relevant to ML as well Advanced Machine Learning Systems - cs6787 - lecture 9 and onwards discuss hardware side of things Machine Learning Systems Design - cs329S Topics in Deployable ML - 6.S979 Machine Learning in Production / AI Engineering (17-445/17-645/17-745/11-695) AutoML - Automated Machine Learning DL >> Deep Unsupervised Learning - cs294 Deep Multi-task and Meta learning - cs330 Topics in Deep Learning - stat991 UPenn/Wharton most chapters start with introductory topics and dig into advanced ones towards the end. Deep Generative Models - cs236 Deep Geometric Learning of Big Data and Applications Deep Implicit Layers - NeurIPS 2020 tutorial DL >> Theory Topics course on Mathematics of Deep Learning - CSCI-GA 3033 Topics Course on Deep Learning - stat212b Analyses of Deep Learning - stats385, videos from 2017 version Mathematics of Deep Learning Geometry of Deep Learning RL >> Meta-Learning - ICML 2019 Tutorial , Metalearning: Applications to Data Mining - google books link Deep Multi-Task and Meta Learning - cs330, videos Deep Reinforcement Learning - cs285 Advanced robotics - cs287 Reinforcement Learning - cs234, videos for 2019 run Reinforcement Learning Summer School 2019: Bandits, RL & Deep RL Optimization >> Convex Optimization I - ee364a, has quite recent videos too. Convex Optimization II - ee364b, 2008 videos Convex Optimization and Approximation - ee227c Convex Optimization - ee227bt Variational Methods for Computer Vision Advanced Optimization and Randomized Algorithms - 10-801, videos Optimization Methods for Machine Learning and Engineering - Karlsruhe Institute of Technology Applications >> Computer Vision Computational Video Manipulation - cs448v Advanced Topics in ML: Modeling and Segmentation of Multivariate Mixed Data TUM AI Guest lecture series - many influential researchers in DL, vision, graphics talk about latest advances and their latest works. Advanced Deep Learning for Computer Vision - TUM ADL4CV Detection, Segmentation and Tracking - TUM CV3DST Guest lectures at TUM Dynamic Vision and Learning group Vision Seminar at MIT Autonomous Vision Group, Talk@Tübingen Seminar Applications >> Natural Language Processing Natural Language Processing with Deep Learning - cs224n ( not sure if it belongs here, people working in NLP can help me out) Neural networks for NLP - cs11-747 Natural Language Understanding - cs224u, video Applications >> 3D Graphics Non-Euclidean Methods in Machine Learning - cs468, 2020 Machine Learning for 3D Data - cs468, spring 2017 Data-Driven Shape Analysis - cs468, 2014 Geometric Deep Learning - Not a course but the website links a few tutorials on Geometric DL Deep Learning for Computer Graphics - SIGGRAPH 2019 Machine Learning for Machine Vision as Inverse Graphics - csc2547 Winter'20 Machine Learning Meets Geometry, winter 2020; Machine Learning for 3D Data, winter 2018 Edit: Upon suggestion, categorized the courses. There might be some misclassifications as I'm not trained on this task ;). Added some good ones from older (linked above) discussions.

#reddit #advanced #courses

[N] OpenAI's new language model gpt-3.5-turbo-instruct can defeat chess engine Fairy-Stockfish 14 at level 5

Wiskkey•This week

[N] OpenAI's new language model gpt-3.5-turbo-instruct can defeat chess engine Fairy-Stockfish 14 at level 5

This Twitter thread (Nitter alternative for those who aren't logged into Twitter and want to see the full thread) claims that OpenAI's new language model gpt-3.5-turbo-instruct can "readily" beat Lichess Stockfish level 4 (Lichess Stockfish level and its rating) and has a chess rating of "around 1800 Elo." This tweet shows the style of prompts that are being used to get these results with the new language model. I used website parrotchess\[dot\]com (discovered here) (EDIT: parrotchess doesn't exist anymore, as of March 7, 2024) to play multiple games of chess purportedly pitting this new language model vs. various levels at website Lichess, which supposedly uses Fairy-Stockfish 14 according to the Lichess user interface. My current results for all completed games: The language model is 5-0 vs. Fairy-Stockfish 14 level 5 (game 1, game 2, game 3, game 4, game 5), and 2-5 vs. Fairy-Stockfish 14 level 6 (game 1, game 2, game 3, game 4, game 5, game 6, game 7). Not included in the tally are games that I had to abort because the parrotchess user interface stalled (5 instances), because I accidentally copied a move incorrectly in the parrotchess user interface (numerous instances), or because the parrotchess user interface doesn't allow the promotion of a pawn to anything other than queen (1 instance). Update: There could have been up to 5 additional losses - the number of times the parrotchess user interface stalled - that would have been recorded in this tally if this language model resignation bug hadn't been present. Also, the quality of play of some online chess bots can perhaps vary depending on the speed of the user's hardware. The following is a screenshot from parrotchess showing the end state of the first game vs. Fairy-Stockfish 14 level 5: https://preview.redd.it/4ahi32xgjmpb1.jpg?width=432&format=pjpg&auto=webp&s=7fbb68371ca4257bed15ab2828fab58047f194a4 The game results in this paragraph are from using parrotchess after the forementioned resignation bug was fixed. The language model is 0-1 vs. Fairy-Stockfish level 7 (game 1), and 0-1 vs. Fairy-Stockfish 14 level 8 (game 1). There is one known scenario (Nitter alternative) in which the new language model purportedly generated an illegal move using language model sampling temperature of 0. Previous purported illegal moves that the parrotchess developer examined turned out (Nitter alternative) to be due to parrotchess bugs. There are several other ways to play chess against the new language model if you have access to the OpenAI API. The first way is to use the OpenAI Playground as shown in this video. The second way is chess web app gptchess\[dot\]vercel\[dot\]app (discovered in this Twitter thread / Nitter thread). Third, another person modified that chess web app to additionally allow various levels of the Stockfish chess engine to autoplay, resulting in chess web app chessgpt-stockfish\[dot\]vercel\[dot\]app (discovered in this tweet). Results from other people: a) Results from hundreds of games in blog post Debunking the Chessboard: Confronting GPTs Against Chess Engines to Estimate Elo Ratings and Assess Legal Move Abilities. b) Results from 150 games: GPT-3.5-instruct beats GPT-4 at chess and is a \~1800 ELO chess player. Results of 150 games of GPT-3.5 vs stockfish and 30 of GPT-3.5 vs GPT-4. Post #2. The developer later noted that due to bugs the legal move rate was actually above 99.9%. It should also be noted that these results didn't use a language model sampling temperature of 0, which I believe could have induced illegal moves. c) Chess bot gpt35-turbo-instruct at website Lichess. d) Chess bot konaz at website Lichess. From blog post Playing chess with large language models: Computers have been better than humans at chess for at least the last 25 years. And for the past five years, deep learning models have been better than the best humans. But until this week, in order to be good at chess, a machine learning model had to be explicitly designed to play games: it had to be told explicitly that there was an 8x8 board, that there were different pieces, how each of them moved, and what the goal of the game was. Then it had to be trained with reinforcement learning agaist itself. And then it would win. This all changed on Monday, when OpenAI released GPT-3.5-turbo-instruct, an instruction-tuned language model that was designed to just write English text, but that people on the internet quickly discovered can play chess at, roughly, the level of skilled human players. Post Chess as a case study in hidden capabilities in ChatGPT from last month covers a different prompting style used for the older chat-based GPT 3.5 Turbo language model. If I recall correctly from my tests with ChatGPT-3.5, using that prompt style with the older language model can defeat Stockfish level 2 at Lichess, but I haven't been successful in using it to beat Stockfish level 3. In my tests, both the quality of play and frequency of illegal attempted moves seems to be better with the new prompt style with the new language model compared to the older prompt style with the older language model. Related article: Large Language Model: world models or surface statistics? P.S. Since some people claim that language model gpt-3.5-turbo-instruct is always playing moves memorized from the training dataset, I searched for data on the uniqueness of chess positions. From this video, we see that for a certain game dataset there were 763,331,945 chess positions encountered in an unknown number of games without removing duplicate chess positions, 597,725,848 different chess positions reached, and 582,337,984 different chess positions that were reached only once. Therefore, for that game dataset the probability that a chess position in a game was reached only once is 582337984 / 763331945 = 76.3%. For the larger dataset cited in that video, there are approximately (506,000,000 - 200,000) games in the dataset (per this paper), and 21,553,382,902 different game positions encountered. Each game in the larger dataset added a mean of approximately 21,553,382,902 / (506,000,000 - 200,000) = 42.6 different chess positions to the dataset. For this different dataset of \~12 million games, \~390 million different chess positions were encountered. Each game in this different dataset added a mean of approximately (390 million / 12 million) = 32.5 different chess positions to the dataset. From the aforementioned numbers, we can conclude that a strategy of playing only moves memorized from a game dataset would fare poorly because there are not rarely new chess games that have chess positions that are not present in the game dataset.

#reddit #openai's #language

[R] "o3 achieves a gold medal at the 2024 IOI and obtains a Codeforces rating on par with elite human competitors"

we_are_mammals•This week

[R] "o3 achieves a gold medal at the 2024 IOI and obtains a Codeforces rating on par with elite human competitors"

Competitive Programming with Large Reasoning Models OpenAI We show that reinforcement learning applied to large language models (LLMs) significantly boosts performance on complex coding and reasoning tasks. Additionally, we compare two general-purpose reasoning models - OpenAI o1 and an early checkpoint of o3 - with a domain-specific system, o1-ioi, which uses hand-engineered inference strategies designed for competing in the 2024 International Olympiad in Informatics (IOI). We competed live at IOI 2024 with o1-ioi and, using hand-crafted test-time strategies, placed in the 49th percentile. Under relaxed competition constraints, o1-ioi achieved a gold medal. However, when evaluating later models such as o3, we find that o3 achieves gold without hand-crafted domain-specific strategies or relaxed constraints. Our findings show that although specialized pipelines such as o1-ioi yield solid improvements, the scaled-up, general-purpose o3 model surpasses those results without relying on hand-crafted inference heuristics. Notably, o3 achieves a gold medal at the 2024 IOI and obtains a Codeforces rating on par with elite human competitors. Overall, these results indicate that scaling general-purpose reinforcement learning, rather than relying on domain-specific techniques, offers a robust path toward state-of-the-art AI in reasoning domains, such as competitive programming. https://arxiv.org/abs/2502.06807

#reddit #achieves #gold

milaworld•This week

[N] Stable-Baselines v2.0.0 Released

Has anyone tried to use Stable-Baselines? How does it compare to the official Baselines from OpenAI in your experience? https://github.com/hill-a/stable-baselines From the repo: Stable Baselines Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. You can read a detailed presentation of Stable Baselines in the Medium article. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details. Main differences with OpenAI Baselines This toolset is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups: Unified structure for all algorithms PEP8 compliant (unified code style) Documented functions and classes More tests & more code coverage

#reddit #stable-baselines #v2.0.0

[D] What are some good advanced platforms?

SemperZero•This week

[D] What are some good advanced platforms?

Hey. I'm 27 and I think I got most of the basics for ML. I'm very good at math, I understand statistics and probability quite deep, worked on research projects by myself, for which I had to build models on my own. Not really complex, but still requiring creativity and a good understanding of basic concepts. I will soon start a data science job at a FAANG company and I want to further improve my skills and use their resources to the fullest, but I'm not really sure where to go from here in terms of learning. Could you help me with some more advanced materials/forums for ML research/place with good papers/place with good articles? I'd also like to study the very best and see the way they code and explain advanced concepts (like Andrej Karpathy) where can I find them?? is there a Twitch for challenger level AI researchers streaming live processes? Or videos showing the entire project flow (how they do data visualizations, mining, choosing models, tuning, etc) like top digital artists show the highlights or the entire speed-up of their painting processes? Here's a list all of my projects to get a general idea of my level and where I'm at: calculating the distance between hundreds of 42.000 feature objects (containing categorical, strings, numbers, hashes, booleans as variables) and then clustering. with some vector processing and a neural network implemented from scratch in C some models like ARIMA (together with linear regression) combining a FFT with a neural network for a 42d wave classification T-SNE to split dataset into 2d grids -> Kullback–Leibler on grids for distance -> DBSCAN/KMEANS for clustering genetic algorithms for hyperparameter optimizations and reinforcement learning (neuro evolution) DBSCAN -> Levenberg-Marquardt for polynomial coefficients-> neural network predicting the coefficients based on different parameters playing with instance segmentation and some algorithms to synchronize a color and a depth camera simulations/statistics/probabilities for video games a lot of visualizations and data mining for patterns As you can see there is no LLM/ Generative AI/ Computer Vision stuff, which I would like to get into. I'm also not 100% sure what else would be nice to learn in general. I know most of the basic procedures for training, balancing datasets, avoid overfit, computing error plots, comparing models, etc and I'm familiar with most of math (not insanely advanced) used in ML. I didn't read many papers, but holy ... most of them are so unreadable and filled with pompous nonsense that 99% of the effort is de-obfuscating the bs and reading for so long just to figure out how the input is encoded, what's the output, and what's the model. Where can I find good, readable, structured papers which are actually on point? I'm from Eastern Europe and most of my learning has been done by my self after high school, the education quality is close to zero in the universities here and I never had any mentors at the jobs I worked. There's no research in this country, and getting to work on these projects was insanely hard, some of them being done in my free time or for free just to get experience... Fortunately after a lot of hard work I got into FAANG, and I hope things will be better here. Most of what I've learned has been from very fragmented places on the internet, and now I'm looking for centralized places and communities of top quality content. TL;DR: sorry for the long rambling. had to order my thoughts and figure what i actually want: Looking for top tier AI researchers showcasing their work processes, places with clear papers/articles, tips for someone who's no longer a very beginner, and other communities like this.

#reddit #some #good

[P] RLHF Learning to Summarize: Implementation by CarperAI with trlX

Hyper1on•This week

[P] RLHF Learning to Summarize: Implementation by CarperAI with trlX

Hi, "Learning to summarize from human feedback" is a 2020 paper by OpenAI demonstrating how to use reinforcement learning with human feedback (RLHF) to fine-tune a language model to produce higher quality summaries of news articles and Reddit posts than is possible with supervised fine-tuning. Now, CarperAI has demonstrated how to use their library trlX to implement this work, by applying RLHF to the summarization dataset released by OpenAI and fine-tuning GPT-J-6B. Read the full report here, with a code walkthrough: https://wandb.ai/carperai/summarize_RLHF/reports/Implementing-RLHF-Learning-to-Summarize-with-trlX--VmlldzozMzAwODM2 trlX library here: https://github.com/CarperAI/trlx Twitter thread here: https://twitter.com/carperai/status/1613645352514768897

#reddit #rlhf #learning

[N] TheSequence Scope: When it comes to machine learning, size matters: Microsoft's DeepSpeed framework, which can train a model with up to a trillion parameters

Kseniase•This week

[N] TheSequence Scope: When it comes to machine learning, size matters: Microsoft's DeepSpeed framework, which can train a model with up to a trillion parameters

Hi there! Offering to your attention the latest edition of a weekly ML-newsletter that focusing on three things: impactful ML research papers, cool ML tech solutions, and ML use cases supported by investors. Please, see it below. Reddit is a new thing for me, and I've been struggling a bit with it, so please don't judge me too harsh for this promotion. This weekly digest is free and I hope you'd find the format convenient for you. Your feedback is very appreciated, and please feel free to sign up if you like it. 📝 Editorial The recent emergence of pre-trained language models and transformer architectures pushed the creation of larger and larger machine learning models. Google’s BERT presented attention mechanism and transformer architecture possibilities as the “next big thing” in ML, and the numbers seem surreal. OpenAI’s GPT-2 set a record by processing 1.5 billion parameters, followed by Microsoft’s Turing-NLG, which processed 17 billion parameters just to see the new GPT-3 processing an astonishing 175 billion parameters. To not feel complacent, just this week Microsoft announced a new release of its DeepSpeed framework (which powers Turing-NLG), which can train a model with up to a trillion parameters. That sounds insane but it really isn’t. What we are seeing is a consequence of several factors. First, computation power and parallelization techniques have evolved to a point where it is relatively easy to train machine learning models in large clusters of machines. Second and most importantly, in the current state of machine learning, larger models have regularly outperformed smaller and more specialized models. Knowledge reusability methods like transfer learning are still in very nascent stages. As a result, it’s really hard to build small models that can operate in uncertain environments. Furthermore, as models like GPT-3 and Turing-NLG have shown, there is some unexplainable magic that happens after models go past a certain size. Many of the immediate machine learning problems might be solved by scaling the current generation of neural network architectures. Plain and simple, when it comes to machine learning, size matters. We would love to hear your opinions about the debate between broader-larger vs. smaller and more specialized models. Leave a comment Now, to the most important developments in the AI industry this week 🔎 ML Research GPT-3 Falls Short in Machine Comprehension Proposed by researchers from a few major American universities, a 57-task test to measure models’ ability to reason poses challenges even for sophisticated models like GPT-3 ->read more in the original paper Better Text Summarization OpenAI published a paper showing a reinforcement learning with human feedback technique that can surpass supervised models ->read more on OpenAI blog Reinforcement Learning with Offline Datasets Researchers from the Berkeley AI Research (BAIR) Lab published a paper unveiling a method that uses offline datasets to improve reinforcement learning models->read more on BAIR blog 🤖 Cool AI Tech Releases New Version of DeepSpeed Microsoft open-sourced a new version of DeepSpeed, an open-source library for parallelizable training that can scale up to models with 1 trillion parameters->read more on Microsoft Research blog 💸 Money in AI AI-powered customer experience management platform Sprinklr has raised $200 million (kudos to our subscribers from Sprinklr 👏). Sprinklr's “AI listening processing” solution allows companies to get structured and meaningful sentiments and insights from unstructured customer data that comes from public conversations on different websites and social platforms. Xometry, an on-demand industrial parts marketplace, raises $75 million in Series E funding. The company provides a digital way of creating the right combination of buyers and manufacturers. Another example of AI implementation into matching two sides for a deal. Real estate tech company Orchard raises $69 million in its recent funding round. Orchard aims to digitize the whole real estate market, by developing a solution that combines machine learning and rapid human assistance to smooth the search, match the right deal, and simplify buying and selling relationships. Cybersecurity startup Pcysys raised $25 million in its funding round. Pcysys’ platform, which doesn’t require installation or network reconfiguration, uses algorithms to scan and “ethically” attack enterprise networks. Robotics farming company Iron Ox raised $20 million in a funding round. The system of farming robots is still semi-autonomous, the company’s goal is to become fully autonomous. Insurtech company Descartes Underwriting raised $18.5 million. The company applies AI and machine learning technologies to climate risk predicting and insurance underwriting. Legaltech startup ThoughtRiver raised $10 million in its Series A round. Its AI solution applied to contract pre-screening aims to boost operational efficiency. Medtech startup Skin Analytics raised $5.1 million in Series A funding. Skin Analytics has developed a clinically validated AI system that can identify not only the important skin cancers but also precancerous lesions that can be treated, as well as a range of lesions that are benign. Amazon, along with several government organizations and three other industry partners, helped fund the National Science Foundation, a high-priority AI research initiative. The amount of funding is not disclosed. The content of TheSequence is written by Jesus Rodriguez, one of the most-read contributors to KDNuggets and TDS. You can check his Medium here.

#reddit #thesequence #scope:

[P] Improve AI 8.0: Free Contextual Multi-Armed Bandit Platform for Scoring, Ranking & Decisions

gogogadgetlegz•This week

[P] Improve AI 8.0: Free Contextual Multi-Armed Bandit Platform for Scoring, Ranking & Decisions

Improve AI 8.0 - Contextual Multi-Armed Bandit Platform for Scoring, Ranking & Decisions Full announcement post at: https://improve.ai/2023/06/08/contextual-bandit.html We’re thrilled to introduce Improve AI 8.0, a modern, free, production-ready contextual multi-armed bandit platform that quickly scores and ranks items using intuitive reward-based training. Multi-armed bandits and contextual bandits are corner-stone machine learning algorithms that power a myriad of applications including recommendation systems, personalization, query re-ranking, automated decisions, and multi-variate optimization. With version 8, we’ve fully delivered on our original vision - providing a high performance, simple to use, low cost contextual multi-armed bandit platform. Key features of v8.0 include: Simplified APIs 90% more memory efficient XGBoost models The reward tracker & trainer is now free for most uses On-device scoring, ranking, and decisions for iOS and Android apps Native Swift SDK that can rank or score any Encodable Ranked Value Encoding* for accurate scoring of String properties Compact hash tables for reduced model sizes when encoding large numbers of string values Balanced exploration vs exploitation using Thompson Sampling Simple APIs With Swift, Python, or Java, create a list of JSON encodable items and simply call Ranker.rank(items). For instance, in an iOS bedtime story app, you may have a list of Story objects: struct Story: Codable { var title: String var author: String var pageCount: Int } To obtain a ranked list of stories, use just one line of code: let rankedStories = try Ranker(modelUrl).rank(stories) The expected best story will be the first element in the ranked list: let bestStory = rankedStories.first Simple Training Easily train your rankers using reinforcement learning. First, track when an item is used: let tracker = RewardTracker("stories", trackUrl) let rewardId = tracker.track(story, from: rankedStories) Later, if a positive outcome occurs, provide a reward: if (purchased) { tracker.addReward(profit, rewardId) } Reinforcement learning uses positive rewards for favorable outcomes (a “carrot”) and negative rewards for undesirable outcomes (a “stick”). By assigning rewards based on business metrics, such as revenue or conversions, the system optimizes these metrics over time. Contextual Ranking & Scoring Improve AI turns XGBoost into a contextual multi-armed bandit, meaning that context is considered when making ranking or scoring decisions. Often, the choice of the best variant depends on the context that the decision is made within. Let’s take the example of greetings for different times of the day: greetings = ["Good Morning", "Good Afternoon", "Good Evening", "Buenos Días", "Buenas Tardes", "Buenas Noches"] rank() also considers the context of each decision. The context can be any JSON-encodable data structure. ranked = ranker.rank(items=greetings, context={ "day_time": 12.0, "language": "en" }) greeting = ranked[0] Trained with appropriate rewards, Improve AI would learn from scratch which greeting is best for each time of day and language. XGBoost Model Improvements Improve AI v8.0 is 90%+ more memory efficient for most use cases. Feature hashing has been replaced with a feature encoding approach that only uses a single feature per item property, substantially improving both training performance as well as ranking / scoring. Ranked Value Encoding Ranked Value Encoding is our novel approach to encoding string values in a manner that is extremely space efficient, accurate, and helps approximate Thompson Sampling for balanced exploration vs exploitation. The concept of Ranked Value Encoding is similar to commonly used Target Value Encoding for encoding string or categorical features. With Target Value Encoding, each string or categorical feature is replaced with the mean of the target values for that string or category. Target Value Encoding tends to provide good results for regression. However, multi-armed bandits are less concerned with the absolute accuracy of the scores and more concerned with the relative scores between items. Since we don’t need the exact target value, we can simply store the relative ranking of the string values, which saves space in the resulting model, increasing performance and lowering distribution costs. Compact String Encoding In conjunction with Ranked Value Encoding, rather than store entire strings, which could be arbitrarily long, Improve AI v8 models only store compact string hashes, resulting in only \~4 bytes per string for typical models. Proven Performance Improve AI is a production ready implementation of a contextual multi-armed bandit algorithm, honed through years of iterative development. By merging Thompson Sampling with XGBoost, it provides a learning system that is both fast and flexible. Thompson Sampling maintains equilibrium between exploring novel possibilities and capitalizing on established options, while XGBoost ensures cost-effective, high-performance training for updated models. Get Started Today Improve AI is available now for Python, Swift, and Java. Check out the Quick-Start Guide for more information. Thank you for your efforts to improve the world a little bit today.

#reddit #improve #8.0:

Watched 8 hours of MrBeast's content. Here are 7 psychological strategies he's used to get 34 billion views

Positive-Bison5023•This week

Watched 8 hours of MrBeast's content. Here are 7 psychological strategies he's used to get 34 billion views

MrBeast can fill giant stadiums and launch 8-figure candy companies on demand. He’s unbelievably popular. Recently, I listened to the brilliant marketer Phill Agnew (from The Nudge podcast) being interviewed on the Creator Science podcast. The episode focused on how MrBeast’s near-academic understanding of audience psychology is the key to his success. Better than anyone, MrBeast knows how to get you: \- Click on his content (increase his click-through rate) \- Get you to stick around (increase his retention rate) He gets you to click by using irresistible thumbnails and headlines. I watched 8 hours of his content. To build upon Phil Agnew’s work, I made a list of 7 psychological effects and biases he’s consistently used to write headlines that get clicked into oblivion. Even the most aggressively “anti-clickbait” purists out there would benefit from learning the psychology of why people choose to click on some content over others. Ultimately, if you don’t get the click, it really doesn’t matter how good your content is. Novelty Effect MrBeast Headline: “I Put 100 Million Orbeez In My Friend's Backyard” MrBeast often presents something so out of the ordinary that they have no choice but to click and find out more. That’s the “novelty effect” at play. Our brain’s reward system is engaged when we encounter something new. You’ll notice that the headline examples you see in this list are extreme. MrBeast takes things to the extreme. You don’t have to. Here’s your takeaway: Consider breaking the reader/viewer’s scrolling pattern by adding some novelty to your headlines. How? Here are two ways: Find the unique angle in your content Find an unusual character in your content Examples: “How Moonlight Walks Skyrocketed My Productivity”. “Meet the Artist Who Paints With Wine and Chocolate.” Headlines like these catch the eye without requiring 100 million Orbeez. Costly Signaling MrBeast Headline: "Last To Leave $800,000 Island Keeps It" Here’s the 3-step click-through process at play here: MrBeast lets you know he’s invested a very significant amount of time and money into his content. This signals to whoever reads the headline that it's probably valuable and worth their time. They click to find out more. Costly signaling is all amount showcasing what you’ve invested into the content. The higher the stakes, the more valuable the content will seem. In this example, the $800,000 island he’s giving away just screams “This is worth your time!” Again, they don’t need to be this extreme. Here are two examples with a little more subtlety: “I built a full-scale botanical garden in my backyard”. “I used only vintage cookware from the 1800s for a week”. Not too extreme, but not too subtle either. Numerical Precision MrBeast knows that using precise numbers in headlines just work. Almost all of his most popular videos use headlines that contain a specific number. “Going Through The Same Drive Thru 1,000 Times" “$456,000 Squid Game In Real Life!” Yes, these headlines also use costly signaling. But there’s more to it than that. Precise numbers are tangible. They catch our eye, pique our curiosity, and add a sense of authenticity. “The concreteness effect”: Specific, concrete information is more likely to be remembered than abstract, intangible information. “I went through the same drive thru 1000 times” is more impactful than “I went through the same drive thru countless times”. Contrast MrBeast Headline: "$1 vs $1,000,000 Hotel Room!" Our brains are drawn to stark contrasts and MrBeast knows it. His headlines often pit two extremes against each other. It instantly creates a mental image of both scenarios. You’re not just curious about what a $1,000,000 hotel room looks like. You’re also wondering how it could possibly compare to a $1 room. Was the difference wildly significant? Was it actually not as significant as you’d think? It increases the audience’s \curiosity gap\ enough to get them to click and find out more. Here are a few ways you could use contrast in your headlines effectively: Transformational Content: "From $200 to a $100M Empire - How A Small Town Accountant Took On Silicon Valley" Here you’re contrasting different states or conditions of a single subject. Transformation stories and before-and-after scenarios. You’ve got the added benefit of people being drawn to aspirational/inspirational stories. Direct Comparison “Local Diner Vs Gourmet Bistro - Where Does The Best Comfort Food Lie?” Nostalgia MrBeast Headline: "I Built Willy Wonka's Chocolate Factory!" Nostalgia is a longing for the past. It’s often triggered by sensory stimuli - smells, songs, images, etc. It can feel comforting and positive, but sometimes bittersweet. Nostalgia can provide emotional comfort, identity reinforcement, and even social connection. People are drawn to it and MrBeast has it down to a tee. He created a fantasy world most people on this planet came across at some point in their childhood. While the headline does play on costly signaling here as well, nostalgia does help to clinch the click and get the view. Subtle examples of nostalgia at play: “How this \[old school cartoon\] is shaping new age animation”. “\[Your favorite childhood books\] are getting major movie deals”. Morbid Curiosity MrBeast Headline: "Surviving 24 Hours Straight In The Bermuda Triangle" People are drawn to the macabre and the dangerous. Morbid curiosity explains why you’re drawn to situations that are disturbing, frightening, or gruesome. It’s that tension between wanting to avoid harm and the irresistible desire to know about it. It’s a peculiar aspect of human psychology and viral content marketers take full advantage of it. The Bermuda Triangle is practically synonymous with danger. The headline suggests a pretty extreme encounter with it, so we click to find out more. FOMO And Urgency MrBeast Headline: "Last To Leave $800,000 Island Keeps It" “FOMO”: the worry that others may be having fulfilling experiences that you’re absent from. Marketers leverage FOMO to drive immediate action - clicking, subscribing, purchasing, etc. The action is driven by the notion that delay could result in missing out on an exciting opportunity or event. You could argue that MrBeast uses FOMO and urgency in all of his headlines. They work under the notion that a delay in clicking could result in missing out on an exciting opportunity or event. MrBeast’s time-sensitive challenge, exclusive opportunities, and high-stakes competitions all generate a sense of urgency. People feel compelled to watch immediately for fear of missing out on the outcome or being left behind in conversations about the content. Creators, writers, and marketers can tap into FOMO with their headlines without being so extreme. “The Hidden Parisian Cafe To Visit Before The Crowds Do” “How \[Tech Innovation\] Will Soon Change \[Industry\] For Good” (Yep, FOMO and urgency are primarily responsible for the proliferation of AI-related headlines these days). Why This All Matters If you don’t have content you need people to consume, it probably doesn’t! But if any aspect of your online business would benefit from people clicking on things more, it probably does. “Yes, because we all need more clickbait in this world - \eye-roll emoji\” - Disgruntled Redditor I never really understood this comment but I seem to get it pretty often. My stance is this: If the content delivers what the headline promises, it shouldn’t be labeled clickbait. I wouldn’t call MrBeast’s content clickbait. The fact is that linguistic techniques can be used to drive people to consume some content over others. You don’t need to take things to the extremes that MrBeast does to make use of his headline techniques. If content doesn’t get clicked, it won’t be read, viewed, or listened to - no matter how brilliant the content might be. While “clickbait” content isn’t a good thing, we can all learn a thing or two from how they generate attention in an increasingly noisy digital world.

#reddit #watched #hours

ChatGPT Full Course For 2025 | ChatGPT Tutorial For Beginnners | ChatGPT Course | Simplilearn

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Simplilearn•Mar 28, 2025

ChatGPT Full Course For 2025 | ChatGPT Tutorial For Beginnners | ChatGPT Course | Simplilearn

🔥Purdue - Applied Generative AI Specialization - https://www.simplilearn.com/applied-ai-course?utmcampaign=C4lBsBlloL0&utmmedium=Lives&utm_source=Youtube 🔥Professional Certificate Program in Generative AI and Machine Learning - IITG (India Only) - https://www.simplilearn.com/iitg-generative-ai-machine-learning-program?utmcampaign=C4lBsBlloL0&utmmedium=Lives&utm_source=Youtube 🔥Advanced Executive Program In Applied Generative AI - https://www.simplilearn.com/applied-generative-ai-course?utmcampaign=C4lBsBlloL0&utmmedium=Lives&utm_source=Youtube This ChatGPT Full Course 2025 by Simplilearn provides a comprehensive learning journey, starting with an introduction to ChatGPT and Generative AI, followed by insights into AI job opportunities and a comparison between ChatGPT 4.0 and 4.0 Turbo. The tutorial covers prompt engineering techniques, machine learning fundamentals, and running Llama models privately. Learners will explore ChatGPT-powered application development, its role in programming, and Excel automation. The course also dives into blogging, PowerPoint automation, customer support, and finance applications. Advanced topics like RAG vs. Prompt Tuning, prompt injection, and LangChain are included, along with discussions on OpenAI's latest innovations, including Sora and Strawberry. By the end, participants will gain a strong understanding of ChatGPT’s capabilities and monetization strategies. 🚀 Following are the topics covered in the ChatGPT Full Course 2025: 00:00:00 - Introduction to ChatGPT Full Course 2025 00:09:26 - What is ChatGPT 00:10:11 - What is Gen AI 00:26:29 - How to get Job in AI 00:27:06 - ChatGPT 40 vs ChatGPT 4 01:03:14 - Chatgpt analyse 02:13:57 - Prompt Engineering Tutorial 03:10:34 - What is Machine Learning 04:07:06 - Machine Learning Tutorial 04:08:13 - Run Lama Privately 04:23:50 - Search GPT 04:25:31 - Build App Using ChatGPT 06:31:11 - ChatGPT for Programming 06:46:08 - Prompt Formulae Chatgpt 07:58:38 - Automate Excel using Chatgpt 08:00:06 - Blogging with ChatGpt 08:27:25 - Powerpoint using Chatgpt 08:28:31 - Rag Vs Prompt Tuning 09:37:43 - Chatgpt for Customer Support 11:11:06 - ChatGPT for finance 11:17:38 - Prompt injection 11:18:38 - How to Earn Money using ChatGPT 11:41:46 - Open AI Strawberry 11:52:42 - Openai sora 11:54:57 - Langchain 12:22:19 - Open ai chatgpt o1 model ✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH ⏩ Check out the Artificial Intelligence training videos: https://youtube.com/playlist?list=PLEiEAq2VkUULa5aOQmO_al2VVmhC-eqeI #gpt #chatgpt #chatgptforbeginners #chatgptcourse #genai #generativeai #artificialintelligence #ai #machinelearning #llm #simplilearn #2025 ➡️ About Professional Certificate Program in Generative AI and Machine Learning Dive into the future of AI with our Generative AI & Machine Learning course, in collaboration with E&ICT Academy, IIT Guwahati. Learn tools like ChatGPT, OpenAI, Hugging Face, Python, and more. Join masterclasses led by IITG faculty, engage in hands-on projects, and earn Executive Alumni Status. Key Features: ✅ Program completion certificate from E&ICT Academy, IIT Guwahati ✅ Curriculum delivered in live virtual classes by seasoned industry experts ✅ Exposure to the latest AI advancements, such as generative AI, LLMs, and prompt engineering ✅ Interactive live-virtual masterclasses delivered by esteemed IIT Guwahati faculty ✅ Opportunity to earn an 'Executive Alumni Status' from E&ICT Academy, IIT Guwahati ✅ Eligibility for a campus immersion program organized at IIT Guwahati ✅ Exclusive hackathons and “ask-me-anything” sessions by IBM ✅ Certificates for IBM courses and industry masterclasses by IBM experts ✅ Practical learning through 25+ hands-on projects and 3 industry-oriented capstone projects ✅ Access to a wide array of AI tools such as ChatGPT, Hugging Face, DALL-E 2, Midjourney and more ✅ Simplilearn's JobAssist helps you get noticed by top hiring companies Skills Covered: ✅ Generative AI ✅ Prompt Engineering ✅ Chatbot Development ✅ Supervised and Unsupervised Learning ✅ Model Training and Optimization ✅ Model Evaluation and Validation ✅ Ensemble Methods ✅ Deep Learning ✅ Natural Language Processing ✅ Computer Vision ✅ Reinforcement Learning ✅ Machine Learning Algorithms ✅ Speech Recognition ✅ Statistics Learning Path: ✅ Program Induction ✅ Programming Fundamentals ✅ Python for Data Science (IBM) ✅ Applied Data Science with Python ✅ Machine Learning ✅ Deep Learning with TensorFlow (IBM) ✅ Deep Learning Specialization ✅ Essentials of Generative AI, Prompt Engineering & ChatGPT ✅ Advanced Generative AI ✅ Capstone Electives: ✅ ADL & Computer Vision ✅ NLP and Speech Recognition ✅ Reinforcement Learning ✅ Academic Masterclass ✅ Industry Masterclass 👉 Learn More At: https://www.simplilearn.com/iitg-generative-ai-machine-learning-program?utmcampaign=C4lBsBlloL0&utmmedium=Lives&utm_source=Youtube

Reinforcement

What Reinforcement Learning Method Should I Use for Poker AI with LLMs?

[P] Building an Reinforcement Learning Agent to play The Legend of Zelda

[N] 20 hours of new lectures on Deep Learning and Reinforcement Learning with lots of examples

[D] What is your honest experience with reinforcement learning?

[R] Reinforcement Learning for Sequential Decision and Optimal Control

[D] What is your honest experience with reinforcement learning?

[D] What is your honest experience with reinforcement learning?

[D] What kind of reinforcement learning algorithm would you recommend for training AI for a strategy game like Risk or Axis&Allies?

Solo technical founders, has not having a business cofounder even screwed you up? "I will not promote"

How I Started Learning Machine Learning

Are Genetic Algorithms Still Relevant in 2025?

TOP ML University Courses for beginners (FREE)

How I Started Learning Machine Learning

Strategies for training AI players on asymmetric games?

ZeroToHeroML: Beginner-Friendly ML & AI Course (Free)

Teaching an AI to Play Mario: A Learning Journey

Best AI/ML course for Beginners to advanced - recommendations?

Seeking Guidance on Implementing LLM and Generative AI in a New Role.

I am Jürgen Schmidhuber, AMA!

[D] Advanced courses update

[N] OpenAI's new language model gpt-3.5-turbo-instruct can defeat chess engine Fairy-Stockfish 14 at level 5

[R] "o3 achieves a gold medal at the 2024 IOI and obtains a Codeforces rating on par with elite human competitors"

[N] Stable-Baselines v2.0.0 Released

[D] What are some good advanced platforms?

[P] RLHF Learning to Summarize: Implementation by CarperAI with trlX

[N] TheSequence Scope: When it comes to machine learning, size matters: Microsoft's DeepSpeed framework, which can train a model with up to a trillion parameters

[P] Improve AI 8.0: Free Contextual Multi-Armed Bandit Platform for Scoring, Ranking & Decisions

Watched 8 hours of MrBeast's content. Here are 7 psychological strategies he's used to get 34 billion views

ChatGPT Full Course For 2025 | ChatGPT Tutorial For Beginnners | ChatGPT Course | Simplilearn

ARENA_2.0

AITreasureBox

h2o-llmstudio

Production-Level-Deep-Learning

practicalAI-cn

info8006-introduction-to-ai

awesome-ai-in-finance

awesome-quantum-machine-learning

aima-java

AI-PhD-S24

deep-rts

ai50

teach-AI-in-business

flappy-es

ai-learning-roadmap

ai_primer