(It's always nice to see people that condense a huge topic like DL into ~80 pages document and share it with the community) Deep Learning and Computational Physics – Lecture Notes: https://
arxiv.org/abs/2301.00942
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Deep Learning and Computational Physics Lecture Notes Shared
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Deep Learning and Computational Physics Lecture Notes from USC
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Deep Learning and Computational Physics – Lecture Notes, University of South California Great & concise notes on various fundamental topics in deep learning. The notes got a nice structure. Starts from the very basics, gradually to some DL architectures. https://
arxiv.org/abs/2301.00942
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Learning from Preferences: RLHF, Policy Gradients, and Dagger
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Finally, when learning from preferences, one learns an F(x,y) that enables one to rank and select or do policy gradients (e.g. PPO) as in most RLHF. When the interface allows for corrections (e.g. rewriting the response in a chat agent), then we are in the domain of Dagger.
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Dagger Imitation Learning: Human Feedback for Agent Training
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Imitation with Dagger: In counterfactual learning F is typically the identity. The agent acting with policy p(y|x) determines the x’s as in RL, but humans (or other agents) provide corrections in the form of y’s. The new data is used for retraining.
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Self-Training: Filtering Functions and Model Ranking Systems
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Self-training: F(x,y) is a filtering/ranking function, eg., what we call a reward/return. The input x may be chosen by humans, but the model generates the y’s and F ranks and selects for further rounds of self-training. F can be explicit or implicit (human in the loop as in RLHF)
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Supervised Learning: Function Identity and Human-Labeled Data
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Supervised learning: F = I (identity), and x and y are produced by humans. E.g. x is images taken by humans and y are corresponding labels. E.g. 2, x is text and y is the next text token.
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AirByte Loader enables data integration for LangChain applications
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As the final days of #chatyourdata challenge approach, we have one last document loader to enable you… AirByte Loader @AirbyteHQ has 100s of data connectors, and now you can easily use them to load data into a format you can use in LangChain Links
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LangChain Tools for Data Loading and AI Chat Applications
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AirByte Loader: https://
langchain.readthedocs.io/en/latest/modu
les/document_loaders/examples/airbyte_json.html
… ChatVectorDBChain Notebook: https://
langchain.readthedocs.io/en/latest/modu
les/chains/combine_docs_examples/chat_vector_db.html
… Chat-Your-Data Challenge: https://
blog.langchain.dev/chat-your-data
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QA Over Docs: Diagrams Explaining RAG Architecture
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Some great diagrams here explaining how qa over docs works
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Microsoft’s BioGPT biomedical text-generation model goes viral
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The implementation of Microsoft's biomedical text-generation model #BioGPT is going viral on Github
V/ @alphasignalai https://
buff.ly/3RSTqe7
#AI #MachineLearning #DeepLearning @kirkdborne @Khulood_Almani @AkwyZ @Victoryabro @Fabriziobustama @MaiaGabunia @amalmerzouk