Name: Zhechao
Type: User
Company: Treasure Box Capital
Bio: Amazon Applied Scientist II, MIT Alum, Quant ||
More than EQ or IQ is positive energy, the belief that you will succeed in whatever you set out to achieve
Location: Seattle
Zhechao's Projects
Review of Algorithms and Data Structures for Olympics in Informatics
Experimental Code for Computational Statistics
📚 Computer Science Books 计算机技术类书籍 PDF
Store some of the frequently used data science related code for quick reference.
Examine two questions and determine whether they are duplicate or not.
GRU4Rec is the original Theano implementation of the algorithm in "Session-based Recommendations with Recurrent Neural Networks" paper, published at ICLR 2016 and its follow-up "Recurrent Neural Networks with Top-k Gains for Session-based Recommendations". The code is optimized for execution on the GPU.
An other implementation of GRU4REC using PyTorch
Sentence embeddings (InferSent) and training code for NLI.
part of my notes (Chinese) and my code implementation of pytorch official tutorial and examples
Linkedin爬虫,根据公司名字抓取员工的linkedin信息
Work from class 15.095 Machine Learning Under a Modern Optimization Lens at MIT taught by Dimitris Bertsimas and Martin Copenhaver
PyTorch tutorials demonstrating modern techniques with readable code
Python implementation of Mazumder and Hastie's softImpute for matrix completion
Seamless operability between C++11 and Python
Python version of Reinforcement Learning Tree
pytorch tutorial for beginners
A very simple generative adversarial network (GAN) in PyTorch
The code to compile Resume Book of SSAC 2019
Reinforcement Learning for Supply Chain Management
Pytorch Implementation of Textgenrnn
Pytorch Implementation of Textgenrnn
Twitter Sentiment Analyzer
Repository to reproduce results of "Word2vec applied to Recommendation: Hyperparameters Matter" by H. Caselles-Dupré, F. Lesaint and J. Royo-Letelier. The paper will be published on the 12th ACM Conference on Recommender Systems, Vancouver, Canada, 2nd-7th October 2018
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"