meichenchu Goto Github PK

convs2s

Implementation of the ConvS2S architecture using TensorFlow. Also includes the BiConvS2S for bidirectional sequence-to-sequence generation

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

deeplearningwithpython

Machine Learning and Data Science study group starting Sep'2018

graph-neural-network-review

interpretable-ml-book

Book about interpretable machine learning

keras_deep_clustering

How to do Unsupervised Clustering with Keras

leeml-notes

李宏毅《机器学习》笔记，在线阅读地址：https://datawhalechina.github.io/leeml-notes

numpy-ml

Machine learning, in numpy

python-deep-learning-cookbook

Python Deep Learning Cookbook, published by Packt

python-machine-learning-book-2nd-edition

The "Python Machine Learning (2nd edition)" book code repository and info resource

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

themlbook

The Python code to reproduce the illustrations from The Hundred-Page Machine Learning Book.

timeseries_seq2seq

This repo aims to be a useful collection of notebooks/code for understanding and implementing seq2seq neural networks for time series forecasting. Networks are constructed with keras/tensorflow.

tutorial-arima-w-jeffrey-yau

ut_class

PyLadies Taiwan - Unit Test in Python for Beginner