chainer - A flexible framework of neural networks for deep learning

  •        191

Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference. For more details of Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter. The stable version of current Chainer is separated in here: v3.



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  •    Python

ChainerCV is a collection of tools to train and run neural networks for computer vision tasks using Chainer. You can find the documentation here.

hebel - GPU-Accelerated Deep Learning Library in Python

  •    Python

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Arraymancer - A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU, OpenCL and embedded devices

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  •    Jupyter

Chainer-based Python implementation of Transformer, an attention-based seq2seq model without convolution and recurrence. If you want to see the architecture, please see See "Attention Is All You Need", Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, arxiv, 2017.

PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

  •    Python

PyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, Deep Neural Networks built on a tape-based autograd system. You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed.

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  •    Python

ChainerRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. ChainerRL is tested with Python 2.7+ and 3.5.1+. For other requirements, see requirements.txt.

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Machine-Learning-Tutorials - machine learning and deep learning tutorials, articles and other resources


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