- #Fix broken links d2l code
- #Fix broken links d2l free
Strictly follow the naming conventions for the IPython Notebooks and the subsections.Īlso, if you think there's any section that requires more/better explanation, please use the issue tracker to We will assign that issue to you (if no one has been assigned earlier). Before starting out with the notebook, open an issue with the name of the notebook in order to contribute for the same.
#Fix broken links d2l free
Please feel free to open a Pull Request to contribute a notebook in PyTorch for the rest of the chapters.
14.14 Dog Breed Identification (ImageNet Dogs) on Kaggle. 14.13 Image Classification (CIFAR-10) on Kaggle. 14.11 Fully Convolutional Networks (FCN). 14.9 Semantic Segmentation and Data Sets. 
14.7 Single Shot Multibox Detection (SSD). 14.6 Object Detection Data Set (Pikachu). 14.3 Object Detection and Bounding Boxes. 12.5 Mini-batch Stochastic Gradient Descent. 11.2 Sequence to Sequence with Attention Mechanism. 10.11 Bidirectional Recurrent Neural Networks. 10.6 Concise Implementation of Recurrent Neural Networks. 10.5 Implementation of Recurrent Neural Networks from Scratch. 9.7 Densely Connected Networks (DenseNet). 9.4 Networks with Parallel Concatenations (GoogLeNet). 9.1 Deep Convolutional Neural Networks (AlexNet). 8.6 Convolutional Neural Networks (LeNet). 6.8 Numerical Stability and Initialization. 6.7 Forward Propagation Backward Propagation and Computational Graphs. 6.4 Model Selection Underfitting and Overfitting. 6.3 Concise Implementation of Multilayer Perceptron. 6.2 Implementation of Multilayer Perceptron from Scratch. 5.7 Concise Implementation of Softmax Regression. 5.6 Implementation of Softmax Regression from Scratch. 
5.5 Image Classification Data (Fashion-MNIST). 5.3 Concise Implementation of Linear Regression. 5.2 Linear Regression Implementation from Scratch. We suggest cloning the repo or using nbviewer to view the notebooks. Note: Some ipynb notebooks may not be rendered perfectly in Github. #Fix broken links d2l code
We have made an effort to modify the book and convert the MXnet code snippets into PyTorch. Smola and all the community contributors. This project is adapted from the original Dive Into Deep Learning book by Aston Zhang, Zachary C. UPDATE: Please see the orignal repo for the complete PyTorch port.
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