james liao's repositories
awesome-deep-learning-and-machine-learning-questions
【不定期更新中】收集整理的一些网站中（如知乎、Quora、Reddit、Stack Exchange等）与深度学习、机器学习、强化学习、数据科学相关的有价值的问题
awesome-deep-learning-music
List of articles related to deep learning applied to music
awesome-deep-learning-papers
The most cited deep learning papers
deep-learning-coursera
Deep Learning Specialization by Andrew Ng on Coursera.
Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
GBDT_Simple_Tutorial
python实现GBDT的回归、二分类以及多分类，将算法流程详情进行展示解读并可视化，庖丁解牛地理解GBDT。Gradient Boosting Decision Trees regression, dichotomy and multi-classification are realized based on python, and the details of algorithm flow are displayed, interpreted and visualized to help readers better understand Gradient Boosting Decision
Grokking-Deep-Learning
this repository accompanies my forthcoming book "Grokking Deep Learning"
How-to-learn-Deep-Learning
A top-down, practical guide to learn AI, Deep learning and Machine Learning.
instagram-scraper
Scrapes an instagram user's photos and videos
introtodeeplearning_labs
Lab Materials for MIT 6.S191: Introduction to Deep Learning
leetcode-questions-by-company
A list of LeetCode questions with the corresponding companies
machine-learning-interview
机器学习面试题总结
magenta
Magenta: Music and Art Generation with Machine Intelligence
makerting_data_analysis
Give a marketing analysis to the sales department based on the sales data.
music-audio-tagging-at-scale-models
Tensorflow implementation of the models used in "End-to-end learning for music audio tagging at scale"
Neural-Style-Transfer
Keras Implementation of Neural Style Transfer from the paper "A Neural Algorithm of Artistic Style" (http://arxiv.org/abs/1508.06576) in Keras 2.0+
RecSys2018
1'st Place Approach by Layer6 AI to the 2018 ACM RecSys Challenge
tetris_game
A Tetris Game with AI
the-elements-of-statistical-learning-notebooks
Jupyter notebooks for summarizing and reproducing the textbook "The Elements of Statistical Learning" 2/E by Hastie, Tibshirani, and Friedman