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Keras 深度学习框架相关资源(MD版)

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Keras 深度学习框架相关资源

这里是基于Keras的相关资源(来自于作者François Chollet,本人翻译追加了部分中文资源),包括教程和源码、第三方库、应用项目等的链接。如果您有高质量的资源,欢迎分享。

Keras 快速入门

  • keras.io - Keras 文档(中文:http://keras-cn.readthedocs.io)
  • Getting started with the Sequential model
  • Getting started with the functional API
  • Keras FAQ

Keras 教程

  • Quick start: the Iris dataset in Keras and scikit-learn
  • Using pre-trained word embeddings in a Keras model
  • Building powerful image classification models using very little data
  • Building Autoencoders in Keras
  • A complete guide to using Keras as part of a TensorFlow workflow
  • Introduction to Keras, from University of Waterloo: video - slides
  • Introduction to Deep Learning with Keras, from CERN: video - slides
  • Installing Keras for deep learning
  • Develop Your First Neural Network in Python With Keras Step-By-Step
  • Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras
  • Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras
  • Keras video tutorials from Dan Van Boxel
  • Keras Deep Learning Tutorial for Kaggle 2nd Annual Data Science Bowl
  • Collection of tutorials setting up DNNs with Keras

代码例程

文本处理

  • Reuters topic classification
  • LSTM on the IMDB dataset (text sentiment classification)
  • Bidirectional LSTM on the IMDB dataset
  • 1D CNN on the IMDB dataset
  • 1D CNN-LSTM on the IMDB dataset
  • LSTM-based network on the bAbI dataset
  • Memory network on the bAbI dataset (reading comprehension question answering)
  • Sequence to sequence learning for performing additions of strings of digits
  • LSTM text generation
  • Using pre-trained word embeddings
  • Monolingual and Multilingual Image Captioning
  • FastText on the IMDB dataset
  • Structurally constrained recurrent nets text generation
  • Character-level convolutional neural nets for text classification

影像处理

  • Simple CNN on MNIST
  • Simple CNN on CIFAR10 with data augmentation
  • Inception v3
  • VGG 16 (with pre-trained weights)
  • VGG 19 (with pre-trained weights)
  • ResNet 50 (with pre-trained weights): 1 - 2
  • FractalNet
  • AlexNet, VGG 16, VGG 19, and class heatmap visualization
  • Visual-Semantic Embedding
  • Variational Autoencoder: with deconvolutions - with upsampling
  • Visual question answering
  • Deep Networks with Stochastic Depth
  • Smile detection with a CNN
  • VGG-CAM
  • t-SNE of image CNN fc7 activations
  • VGG16 Deconvolution network
  • Wide Residual Networks (with pre-trained weights): 1 - 2
  • Ultrasound nerve segmentation
  • DeepMask object segmentation

可视化应用

  • Real-time style transfer
  • Style transfer: 1 - 2
  • Image analogies: Generate image analogies using neural matching and blending.
  • Visualizing the filters learned by a CNN
  • Deep dreams
  • GAN / DCGAN: 1 - 2 - 3

强化学习

  • DQN
  • FlappyBird DQN
  • async-RL: Tensorflow + Keras + OpenAI Gym implementation of 1-step Q Learning from "Asynchronous Methods for Deep Reinforcement Learning"
  • keras-rl: A library for state-of-the-art reinforcement learning. Integrates with OpenAI Gym and implements DQN, double DQN, Continuous DQN, and DDPG.

混合架构蓝皮书

  • Stateful LSTM
  • Siamese network
  • Pretraining on a different dataset
  • Neural programmer-interpreter

第三方支持库

  • Elephas: Distributed Deep Learning with Keras & Spark
  • Hyperas: Hyperparameter optimization
  • Hera: in-browser metrics dashboard for Keras models
  • Kerlym: reinforcement learning with Keras and OpenAI Gym
  • Qlearning4K: reinforcement learning add-on for Keras
  • seq2seq: Sequence to Sequence Learning with Keras
  • Seya: Keras extras
  • Keras Language Modeling: Language modeling tools for Keras

基于Keras的项目

  • RocAlphaGo: An independent, student-led replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"
  • DeepJazz: Deep learning driven jazz generation using Keras
  • dataset-sts: Semantic Text Similarity Dataset Hub
  • snli-entailment: Independent implementation of attention model for textual entailment from the paper "Reasoning about Entailment with Neural Attention".
  • Headline generator: independent implementation of Generating News Headlines with Recurrent Neural Networks

转载于:https://my.oschina.net/u/2306127/blog/747091

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