Stage 4: Deep Learning and Neural Networks
Build deep intelligence and unlock the infinite possibilities of AI
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A beginner's guide to PyTorch deep learning, including complete tutorials and practical projects, with all code tested and verified.
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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 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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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 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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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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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 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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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 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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A visualized learning resource for large language model algorithms, containing 100+ original illustrated explanations, systematically covering LLM, reinforcement learning, fine-tuning, and alignment techniques.