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Deep Learning · Theory
Deep Learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville · 2016
🆓 FreeAdvanced★★★★★800 pagesISBN 978-0262035613
The definitive textbook on deep learning theory, co-authored by three pioneers including the inventor of GANs.
Why you should read this
The "Bible" of deep learning. Required reading for anyone doing serious research in neural networks and AI.
The "Bible" of deep learning. Required reading for anyone doing serious research in neural networks and AI.
Key Topics
Chapters (10)
1Introduction8 key concepts
AI history, deep learning motivation
2Linear Algebra14 key concepts
Vectors, matrices, eigendecomposition
3Probability & Information Theory12 key concepts
Random variables, distributions, entropy
4Numerical Computation8 key concepts
Overflow, gradient-based optimization
5ML Basics16 key concepts
Capacity, bias-variance, MLE, Bayesian
6Deep Feedforward Networks18 key concepts
Architecture, activation functions, backprop
7Regularization14 key concepts
L1/L2, dropout, data augmentation, early stopping
8Optimization16 key concepts
SGD, Adam, learning rate schedules, batch normalization
9CNNs14 key concepts
Convolution, pooling, architectures
10Sequence Modeling (RNNs)16 key concepts
LSTM, GRU, encoder-decoder, attention
Real-World Applications
- Self-driving cars
- Language translation
- Drug discovery
- Generative art
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