Framework Adaptation
We have added PyTorch implementations up to Chapter 11 (Optimization Algorithms). Chapter 1--7 and Chapter 11 have also been adapted to TensorFlow.
Towards v1.0
The following chapters have been significantly improved for v1.0:
- Linear Neural Networks
- Multilayer Perceptrons
- Deep Learning Computation
- Convolutional Neural Networks
- Modern Convolutional Neural Networks
- Recurrent Neural Networks
Finalized chapters are being translated into Chinese (d2l-zh v2)
Other Improvements
- Fixed issues of not showing all the equation numbers in the HTML and PDF
- Consistently used f-string
- Revised overfitting experiments
- Fixed implementation errors for weight decay experiments
- Improved layer index style
- Revised "breaking the symmetry"
- Revised descriptions of covariate and label shift
- Fixed mathematical errors in covariate shift correction
- Added true risk, empirical risk, and (weighted) empirical risk minimization
- Improved variable naming style for matrices and tensors
- Improved consistency of mathematical notation for tensors of order two or higher
- Improved mathematical descriptions of convolution
- Revised descriptions of cross-correlation
- Added feature maps and receptive fields
- Revised mathematical descriptions of batch normalization
- Added more details to Markov models
- Fixed implementations of k-step-ahead predictions in sequence modeling
- Fixed mathematical descriptions in language modeling
- Improved the
d2l.VocabAPI - Fixed mathematical descriptions and figure illustrations for deep RNNs
- Added BLEU
- Improved machine translation application results
- Improved the animation plot function in the all the training loops