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d2l-en Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
book computer-vision data-science deep-learning gaussian-processes hyperparameter-optimization +12
Last updated over 1 year ago
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New October 23, 2020
Release v0.15.0 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.Vocab API
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