📚 A practical approach to machine learning.
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Updated
Oct 18, 2019 - 29 commits
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📚 A practical approach to machine learning.
The fastai deep learning library, plus lessons and tutorials
🤗 Transformers: State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.
PyTorch Tutorial for Deep Learning Researchers
Clone a voice in 5 seconds to generate arbitrary speech in real-time
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
pytorch handbook是一本开源的书籍,目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门,其中包含的Pytorch教程全部通过测试保证可以成功运行
Train a simple NER tagger for Swedish trained for instance over this dataset.
For this task, we need to adapt the NLPTaskDataFetcher for the appropriate Swedish dataset and train a simple model using Swedish word embeddings. How to train a model is [illustrated here](https://github.com/zalandoresearch/flair/blob/master/resources/docs/TUTORIAL_TRAI
If you use the min_count parameter of the Vocabulary, but you specify a namespace that does not exist, the vocabulary creation will just silently proceed. It'd be great if it could error in this case, perhaps by popping off the namespaces in min_count and erroring if any are left at the end of vocab creation (would probably go at the end of https://github.com/allenai/allennlp/blob/master/all
Support for storing large tensor values in external files was introduced in #678, but AFAICT is undocumented.
This is a pretty important feature, functionally, but it's also important for end users who may not realise that they need to move around more than just the *.onnx file.
I would suggest it should be documented in IR.md, and perhaps there are other locations from which it could be s
Open MMLab Detection Toolbox and Benchmark
Visualizer for neural network, deep learning and machine learning models
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
tensorboard for pytorch (and chainer, mxnet, numpy, ...)
Originated as a forum post
Let's add a tutorial demonstrating how to serve trained models in C++ using torch.jit.save() and torch::jit::load(). I believe there is some subtlety requiring a torch.jit.trace() to wrap poutine.trace and poutine.replay logic for SVI mod
Geometric Deep Learning Extension Library for PyTorch
Set up deep learning environment in a single command line.
Natural Language Processing Tutorial for Deep Learning Researchers
A list of popular github projects related to deep learning
A faster pytorch implementation of faster r-cnn
Official PyTorch Implementation of StarGAN - CVPR 2018
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
Synthesizing and manipulating 2048x1024 images with conditional GANs
Describe the bug
This bug is reported in #2618, where in the sample of code adding or remove .squeeze(1) make the gradient None or not.
Support should be added for this function in autograd.
Environment:
Framework: (TensorFlow, Keras)
Framework version:
tensorflow 1.14.0
tensorflow-estimator 1.14.0
tensorflow-serving-api 1.14.0
Keras 2.2.4
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.0
Horovod version:
horovod 0.18.1
MPI version:
(tensorflow_p36) ubuntu@ip-172-31-38-183:~$ mpirun --version
mpirun (Open MPI) 4.0.1
CUDA version:
CUDA Version 10.0.130
NCCL version