This is a repository for all workshop related materials.

VIP cheatsheets for Stanford's CS 230 Deep Learning

A complete computer science study plan to become a software engineer.

A PyTorch implementation of the Transformer model in "Attention is All You Need".

Class-Balanced Loss Based on Effective Number of Samples. CVPR 2019

Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"

全中文注释.(The loss function of retinanet based on pytorch).(You can use it on one-stage detection task or classifical task, to solve data imbalance influence).用于one-stage目标检测算法,提升检测效果.你也可以在分类任务中使用该损失函…

Geometric Computer Vision Library for Spatial AI

Focal Loss of multi-classification in tensorflow

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

Udacity C++ Nanodegree Program

Four Courses Specialization Tensorflow in practise Specialization

MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

Source code for Deep Fundamental Factor Models, https://arxiv.org/abs/1903.07677

GT CSE6250 Big Data Analytics for Healthcare - Deep Learning Lab Sessions

A machine learning deployment project on detecting plagiarism from text

Udacity Data Science Nanodegree Program

Winning solution to the Avito CTR competition

2nd place solution for Avazu click-through rate prediction competition

Code for the Kaggle Ensembling Guide Article on MLWave

A searchable compilation of Kaggle past solutions

Codes for Kaggle Competitions

Interview = 简历指南 + 算法题 + 八股文 + 源码分析

1st Place Solution for CrowdFlower Product Search Results Relevance Competition on Kaggle.

A collection of Kaggle solutions. Not very polished.

A collection of notebook to learn the Applied Predictive Modeling using Python.

Jupyter notebooks for summarizing and reproducing the textbook "The Elements of Statistical Learning" 2/E by Hastie, Tibshirani, and Friedman