人工智能资料库：第46辑（20170419）
作者：chen_h
微信号 & QQ：862251340
微信公众号：coderpai
1.【博客】5 Machine Learning Projects You Can No Longer Overlook, April
简介：
It’s time for yet another installment of “5 Machine Learning Projects You Can No Longer Overlook” – the modest quest of bringing formidable, lesser-known machine learning projects to a few additional sets of eyes – this time for April 2017. Previous lists have included both general purpose and specialized machine learning and deep learning libraries, along with auxiliary support, data cleaning, and automation tools.
2.【代码】Chatting with a Deep learning brain
简介：
This is a demo of chatting with a Deep learning chatbot trained through Neuralconvo, a Torch library that implements Sequence to Sequence Learning with Neural Networks (seq2seq), reproducing the results in the Neural Conversational Model paper (aka the Google chatbot).
原文链接：http://neuralconvo.huggingface.co/
代码链接：https://github.com/macournoyer/neuralconvo
3.【代码】Neural Complete
简介：
A neural network trained to help writing neural network code using autocomplete
原文链接：https://github.com/kootenpv/neural_complete
4.【博客】A news-analysis NeuralNet learns from a language NeuralNet
简介：
A common way to solve a complex computing task is to chain together specialized components. In data-science this is the pipeline approach. Each component mostly treats the other components as I/O black-boxes. As developers we potentially have the full picture but the system does not.
With Neural Network what happens between I and O is often too interesting to be ignored. One Neural Network can leverage the way another Neural Network processes its inputs.
5.【代码】Spatial Generative Adversarial Networks: Tensorflow
简介：
SGANs can generate sample textures of arbitrary size that look strikingly similar - but not exactly the same - compared to a single (or several) source image(s).
- SGANs can be thought of as a convolutional roll-out of Radford et al.’s deep convolutional generative adversarial networks for texture synthesis
- the fully convolutional nature allows for real-time generation of high resolution images
- the method can fuse multiple source images and is highly scalable w.r.t. to output texture size and allows for generation of tiled textures
原文链接：https://github.com/cryptexis/sgan-tensorflow