GitHub – andresvourakis/data-scientist-handbook: This is a repo with links to everything you'd ever want to learn about data science https://
bit.ly/3N6nZvo
#AI #MachineLearning #DeepLearning #LLMs #DataScience
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Comprehensive Data Science Learning Resource Handbook
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Fastest Way to Deploy a Web App with OpenAI API
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fastest way to deploy a web app with openai api:
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Fastest Way to Deploy a Web App With OpenAI API
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fastest way to deploy a web app with openai api:
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Memory functionality for agents in LangGraph explained
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I wrote some thoughts on memory for agents! We released a bunch of new functionality for memory in LangGraph, and in doing so we thought hard about what memory actually means, and was is useful today Some highlights Memory is application specific The best memory today
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ML Engineering Open Book: Comprehensive Machine Learning Resource
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GitHub – stas00/ml-engineering: Machine Learning Engineering Open Book https://
bit.ly/3SP0qtv
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Fine-tune Llama-3-8b for SQL Code Generation with Small Models
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#CodeGen is a popular use case for LLMs, but you don't need a huge or proprietary model to get good performance. Check out this #tutorial — sample data + notebook included — to learn how to fine-tune a small open-source model (#SLM) LLama-3-8b for SQL generation, achieving a
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Meta Releases SpiritLM Research Model with Code and Weights
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(5/6) Learn more & access Meta's research paper, model weights, and code: Research Paper: https://
arxiv.org/abs/2402.05755 Blog & Overview: https://
ai.meta.com/blog/fair-news
-segment-anything-2-1-meta-spirit-lm-layer-skip-salsa-lingua/?utm_source=twitter&utm_medium=organic_social&utm_content=thread&utm_campaign=fair
… GitHub Repository & Demo: https://
github.com/facebookresear
ch/spiritlm
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Research paper, model weights and code details released
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More details, including links to the research paper, model weights and code
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Rust 1.82 Achieves Parity for aarch64-apple-darwin Target
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“This new milestone puts the aarch64-apple-darwin target on par with the 64-bit ARM Linux and the X86 macOS, Linux, and Windows targets.” https://
blog.rust-lang.org/2024/10/17/Rus
t-1.82.0.html
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Simple Convergence Proof of Adam and Adagrad Optimizers
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A Simple Convergence Proof of Adam and Adagrad https://
bit.ly/3zwWvvZ
#AI #MachineLearning #DeepLearning #LLMs #DataScience