王晨 (wangchenctg)

wangchenctg

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王晨's repositories

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test

test

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practical-pytorch

PyTorch tutorials demonstrating modern techniques with readable code

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tf-stanford-tutorials

This repository contains code examples for the course CS 20SI: TensorFlow for Deep Learning Research.

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tensorflow-shakespeare

Neural machine translation between the writings of Shakespeare and modern English using TensorFlow

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DocumentClassification

This code implements a simple CNN model for document classification with tensorflow.

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Deep-Learning-Tricks

Enumerate diverse machine learning training tricks.

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chatbot-retrieval

Dual LSTM Encoder for Dialog Response Generation

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pytorch-rl

Deep Reinforcement Learning with pytorch & visdom

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pytorch-playground

Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

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seq2seq

Sequence to Sequence Learning with Keras

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tensorflow-white-paper-notes

Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation

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pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

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the-incredible-pytorch

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

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examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

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char-rnn-tensorflow

Multi-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow

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tensorflow-seq2seq-tutorials

Dynamic seq2seq in TensorFlow, step by step

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cnn-text-classification-tf

Convolutional Neural Network for Text Classification in Tensorflow

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seq2seq-pytorch-example

A toy example of sequence to sequence (seq2seq) model with pytorch.

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tf_seq2seq_chatbot

[unmaintained]

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conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

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insuranceQA-cnn-lstm

tensorflow and theano cnn code for insurance QA(question Answer matching)

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cs231n_2016_homework

Assignment 1 of Stanford CS231n 2016 winter course. Modified for Python 3.

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