This Twitter thread started with a user named Jason Liu (@jxnlco) asking for recommendations on resources for building a foundation in machine learning, specifically deep learning. The thread received numerous responses, suggesting various courses, books, and online materials. A few advertisements related to land management and book writing also appeared in the thread.
Courses:
- fast.ai: Recommended by multiple users, particularly for practical deep learning. https://t.co/oV6KBk7Sms
- DeepLearning.AI Specializations: Praised for their comprehensive coverage and industry relevance. @DeepLearningAI
- Stanford Courses: Several Stanford courses were mentioned, specifically:
- CS231n (Convolutional Neural Networks for Visual Recognition)
- CS189 (Introduction to Machine Learning)
- CS280 (Computer Vision)
- CS288 (Reinforcement Learning)
- Marqo's Fine-Tuning Embedding Models Course: A course focused on using and fine-tuning embedding models. https://t.co/UW1juAkMZO
Books and Papers:
- "Designing Machine Learning Systems": Recommended for a general overview of the entire ML process.
- "Understanding Deep Learning" (UDL Book): https://udlbook.github.io/udlbook/
- Transformer Paper: The original research paper on transformers.
- Annotated Transformer: A more accessible version of the transformer paper.
- GPT 1, 2, and 3 Papers: Research papers detailing the development of the Generative Pre-trained Transformer models.
- Anthropic Chat Assistant and RL Papers: Papers related to Anthropic's chat assistant and reinforcement learning.
- RLHF and InstructGPT Papers: Research papers on Reinforcement Learning from Human Feedback and InstructGPT.
Other:
- Karpathy's Neural Nets Zero to Hero (YouTube): Recommended as a starting point for learning about neural networks. https://youtu.be/kCc8FmEb1nY?si=NwdajzMCg_jTlr0I
- StatQuest with Josh Starmer (YouTube): Offers tutorials on statistics, machine learning, and data science. https://youtube.com/@statquest?si=1gv-dyFdR8yBwenv
- Hugging Face LORA and PEFT: Tools for efficient fine-tuning of large language models.
- Build a Large Language Model (From Scratch) (Manning): https://t.co/vFavPajva0
Note: The inclusion of advertisements in the thread (Maven.com, LandGate, Manuscripts.com) is not related to the core topic and has been excluded from the resources section.