ZenHub is the only project management tool that integrates natively within GitHub’s user interface. Developers stay in an environment they love and Project Managers get total visibility into the development process. He built the entire FSD architecture – in less time and with fewer people than Google, Apple, and god knows who else. He’s likely saved more lives than most doctors at this point… But yes, I also feel this is an important development and that this should be an ongoing way of teaching people things.

IIRC OpenAI used a million times more data to train GPT3 than karpathy used in this video, so a naive estimate would be that it would take about 20 billion times more compute. This is could be a significant overestimate since Karpathy probably used each bit of the training set more times than openAI did. So, it’s really five steps, but very smart people have devoted their lives to figuring out each one of those. We’re lucky to live in a time where we can stand on those shoulders, but it can take quite the leap to get up there in the first place. At a glance, the five steps are simple.

It may be best to leave LaTeX-PDF previews to dedicated LaTeX previewers that don’t involve Pandoc. These early results are encouraging, and we look forward to sharing more soon, but sensibleness and specificity aren’t the only qualities we’re looking for in models like LaMDA. We’re also exploring dimensions like “interestingness,” by assessing whether responses are insightful, unexpected or witty. Being Google, we also care a lot about factuality , and are investigating ways to ensure LaMDA’s responses aren’t just compelling but correct.

I think that’s perfectly fine and in the spirit of HN. With your feature set, you’re not really targeting researchers, even if you think you do. For the research papers which you write in LaTeX you should have a look at MonsterWriter. There’s such a myriad of e-learning platforms in Germany and I guess it’s the same for most countries. During my bachelor thesis I added Pandoc as a renderer to an Atom markdown preview extension.

It may cost a few million to train in GPU costs, but if what you say is that important, surely there are folks here who will donate it for the public good? If there are not, then they either don’t consider it important or are just virtue iceland beer brands signaling. Or the effort is actually hard to implement. That being said there are a number of companies who have successfully created large language models like GPT3, so it’s definitely not impossible when you have the resources.

The website is based on the software library OpenAI Baselines, which is an implementation of RL algorithms in Python with PyTorch and TensorFlow. The library includes implementations of popular RL algorithms such as DQN, PPO, A2C, and TRPO. The website provides detailed instructions and code examples on how to use the library to train RL agents and run experiments. RL has been applied to a wide range of domains, including robotics, natural language processing, and finance. In the gaming industry, RL has been used to develop advanced game-playing agents, such as the AlphaGo algorithm that defeated a human champion in the board game Go. In the healthcare industry, RL has been used to optimize treatment plans for patients with chronic diseases, such as diabetes.

This post by neptune.ai provides an overview of the popular tools and libraries used in RL with Python to help readers decide which tools are best suited for their specific use case. It covers a variety of popular RL libraries such as TensorFlow, PyTorch, and OpenAI Baselines, as well as other tools such as OpenAI Gym, and RL Toolbox. The post also covers other topics such as visualization tools, model management tools and experiment tracking tools which are useful for RL.

These techniques are used to improve the performance of reinforcement learning algorithms and make them more efficient. The book is intended for readers with some experience in machine learning and deep learning, but no prior experience with reinforcement learning is required. The authors provide a comprehensive and accessible introduction to the field, making it an ideal choice for both beginners and experienced practitioners. The book also delves into advanced topics such as planning under uncertainty, safe reinforcement learning, and the use of decision-making methods in real-world applications. The author explains the concepts in a clear and concise manner, with the help of examples and exercises to help the reader understand and apply the material. Photo from Spinning Up in Deep RL official website by OpenAI— Spinning Up in Deep RL is developed and maintained by OpenAI.