UP主： 封面： 简介：https://www.oreilly.com/library/view/deep-learning-with/9781633436589/Oreilly - Deep Learning with Python, Third Edition, Video Edition 2025-09
视频选集 001. Chapter 1. What is deep learning 002. Chapter 1. Artificial intelligence 003. Chapter 1. Machine learning 004. Chapter 1. Learning rules and representations from data 005. Chapter 1. The deep in deep learning 006. Chapter 1. Understanding how deep learning works, in three figures 007. Chapter 1. Understanding how deep learning works, in three figures 008. Chapter 1. The age of generative AI 009. Chapter 1. What deep learning has achieved so far 010. Chapter 1. Beware of the short-term hype 011. Chapter 1. Summer can turn to winter 012. Chapter 1. The promise of AI 013. Chapter 2. The mathematical building blocks of neural networks 014. Chapter 2. Data representations for neural networks 015. Chapter 2. The gears of neural networks - Tensor operations 016. Chapter 2. The engine of neural networks - Gradient-based optimization 017. Chapter 2. Looking back at our first example 018. Chapter 2. Summary 019. Chapter 3. Introduction to TensorFlow, PyTorch, JAX, and Keras 020. Chapter 3. How these frameworks relate to each other 021. Chapter 3. Introduction to TensorFlow 022. Chapter 3. Introduction to PyTorch 023. Chapter 3. Introduction to JAX 024. Chapter 3. Introduction to Keras 025. Chapter 3. Summary 026. Chapter 4. Classification and regression 027. Chapter 4. Classifying newswires - A multiclass classification example 028. Chapter 4. Predicting house prices - A regression example 029. Chapter 4. Summary 030. Chapter 5. Fundamentals of machine learning 031. Chapter 5. Evaluating machine-learning models 032. Chapter 5. Improving model fit 033. Chapter 5. Improving generalization 034. Chapter 5. Summary 035. Chapter 6. The universal workflow of machine learning 036. Chapter 6. Developing a model 037. Chapter 6. Deploying your model 038. Chapter 6. Summary 039. Chapter 7. A deep dive on Keras 040. Chapter 7. Different ways to build Keras models 041. Chapter 7. Using built-in training and evaluation loops 042. Chapter 7. Writing your own training and evaluation loops 043. Chapter 7. Summary 044. Chapter 8. Image classification 045. Chapter 8. Training a ConvNet from scratch on a small dataset 046. Chapter 8. Using a pretrained model 047. Chapter 8. Summary 048. Chapter 9. ConvNet architecture patterns 049. Chapter 9. Residual connections 050. Chapter 9. Batch normalization 051. Chapter 9. Depthwise separable convolutions 052. Chapter 9. Putting it together - A mini Xception-like model 053. Chapter 9. Beyond convolution - Vision Transformers 054. Chapter 9. Summary 055. Chapter 10. Interpreting what ConvNets learn 056. Chapter 10. Visualizing ConvNet filters 057. Chapter 10. Visualizing heatmaps of class activation 058. Chapter 10. Visualizing the latent space of a ConvNet 059. Chapter 10. Summary 060. Chapter 11. Image segmentation 061. Chapter 11. Training a segmentation model from scratch 062. Chapter 11. Using a pretrained segmentation model 063. Chapter 11. Summary 064. Chapter 12. Object detection 065. Chapter 12. Training a YOLO model from scratch 066. Chapter 12. Using a pretrained RetinaNet detector 067. Chapter 12. Summary 068. Chapter 13. Timeseries forecasting 069. Chapter 13. A temperature forecasting example 070. Chapter 13. Recurrent neural networks 071. Chapter 13. Going even further 072. Chapter 13. Summary 073. Chapter 14. Text classification 074. Chapter 14. Preparing text data 075. Chapter 14. Sets vs. sequences 076. Chapter 14. Set models 077. Chapter 14. Sequence models 078. Chapter 14. Summary 079. Chapter 15. Language models and the Transformer 080. Chapter 15. Sequence-to-sequence learning 081. Chapter 15. The Transformer architecture 082. Chapter 15. Classification with a pretrained Transformer 083. Chapter 15. What makes the Transformer effective 084. Chapter 15. Summary 085. Chapter 16. Text generation 086. Chapter 16. Training a mini-GPT 087. Chapter 16. Using a pretrained LLM 088. Chapter 16. Going further with LLMs 089. Chapter 16. Where are LLMs heading next 090. Chapter 16. Summary 091. Chapter 17. Image generation 092. Chapter 17. Diffusion models 093. Chapter 17. Text-to-image models 094. Chapter 17. Summary 095. Chapter 18. Best practices for the real world 096. Chapter 18. Scaling up model training with multiple devices 097. Chapter 18. Speeding up training and inference with lower-precision computa 098. Chapter 18. Summary 099. Chapter 19. The future of AI 100. Chapter 19. Scale isn t all you need
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