Stage 4: Deep Learning and Neural Networks
Build deep intelligence and unlock the infinite possibilities of AI
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Andrej Karpathy provides an in-depth explanation of large language model (LLM) technology, covering the complete training stack from fundamental neural networks to advanced models like GPT and Llama, including cutting-edge techniques such as RLHF (Reinforcement Learning from Human Feedback).
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Stanford CS336 course, systematically explaining how to build a large language model from scratch, covering the complete process from data processing, Transformer architecture, model training, GPU optimization, parallel computing to RLHF alignment.
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A complete tutorial on building large language models from scratch, implementing the GPT architecture step-by-step with PyTorch, including the entire process of pre-training, fine-tuning, and deployment.
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The third edition of the definitive deep learning textbook by Keras creator François Chollet, covering the latest technologies such as generative AI, large language models, and diffusion models. Available for free online reading.
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A large model programming practice tutorial developed by Shanghai Jiao Tong University, covering a complete set of learning resources on 11 core topics including fine-tuning deployment, prompt engineering, model editing, and security techniques.
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A visualized learning resource for large language model algorithms, containing 100+ original illustrated explanations, systematically covering LLM, reinforcement learning, fine-tuning, and alignment techniques.
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A comprehensive textbook on graph representation learning, covering the theory and practice of node embeddings, graph neural networks, and graph generative models.
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An open-source deep learning textbook that combines theory, code, and practice, supports multi-framework implementations, and provides a complete deep learning learning path from beginner to advanced.
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A free diffusion model course provided by Hugging Face, covering theoretical foundations, practical applications, and model training from scratch. It is suitable for developers with deep learning experience to learn image and audio generation techniques.
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Hugging Face's free large language model and natural language processing course, covering the complete technology stack of Transformers, data processing, model fine-tuning, and more.
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A guide to core concepts in machine learning and AI by Sebastian Raschka, covering topics such as deep learning, computer vision, natural language processing, production deployment, and model evaluation through 30 key questions and answers.
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A complete TensorFlow tutorial series by Hvass Laboratories, featuring 30 tutorials covering topics from basic linear models to advanced deep learning techniques, with over 3.87 million total views.
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An authoritative deep learning textbook written by the three giants of deep learning, covering a complete knowledge system from theoretical foundations to practical applications.
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A series of in-depth learning and neural network video tutorials produced by 3Blue1Brown, which explains core concepts such as neural networks, gradient descent, and backpropagation in an easy-to-understand manner through unique visualization.
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A deep learning tutorial series by 3Blue1Brown, explaining core concepts such as neural networks, gradient descent, backpropagation, and Transformers through beautiful visual animations. It's a high-quality resource for getting started with deep learning.
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A beginner's guide to PyTorch deep learning, including complete tutorials and practical projects, with all code tested and verified.