From @swapnakpanda Stanford’s FREE Machine Learning Courses: CS221 – Artificial Intelligence
CS229 – Machine Learning
CS230 – Deep Learning
CS234 – Reinforcement Learning
CS224N – NLP with Deep Learning Links below…
MACHINE LEARNING
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Stanford’s Free Machine Learning Courses Available Online
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Comprehensive Python Machine Learning Book Third Edition 772 Pages
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Learn Python for #MachineLearning comprehensively in this massive 772-page book:
=> #Python Machine Learning (3rd Ed.)
=> http://
amzn.to/42eUuhB by @rasbt ————
#DataScience #ML #AI #NeuralNetworks #DeepLearning #DataScientist #ReinforcementLearning -
Autonomous Agents: Decision-Making and Task Decomposition Insights
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random thoughts on autonomous agents – breaks down to (1) figuring out what to do, and (2) figuring out how to do it
– we need/want insight into what’s being done, how, and why
– tasks are a human interpretable unit for agents that can break down into sub tasks and flow up to -
Building Neo4j-Powered Applications with LLMs and Haystack
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New from @PacktDataML >> "Building Neo4j-Powered Applications with LLMs: Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI" Available at https://
amzn.to/4l9lLKO -
Data Quality Tradeoffs in SOTA Model Training Datasets
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I'm not 100% sure about that. As an example I was just browsing through the DCLM-baseline datamix (which is ~SOTA) and it is *terrible*. Compared to what I could in principle imagine. Major concessions are made in data quality to gather enough data quantity.
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The Quest for Ultimate High-Quality Pretraining Data for LLMs
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Mildly obsessed with what the "highest grade" pretraining data stream looks like for LLM training, if 100% of the focus was on quality, putting aside any quantity considerations. Guessing something textbook-like content, in markdown? Or possibly samples from a really giant model?
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Neural Conversational Model Paper Turns 10 Years Old
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"A Neural Conversational Model" is 10 years old, w/ @quocleix . TL;DR you can train a chatbot with a large neural network (~500M params!). Samples This paper was received with mixed reviews, but I'm glad all the critics are now riding the LLM wave https://
arxiv.org/abs/1506.05869 -
Property Inference Advances in Machine Learning Research
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yeah, agree that that's a nice relaxation of this problem. this work focuses on property inference:
