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10-steps-to-become-a-data-scientist

📢 Ready to learn! you will learn 10 skills as data scientist:📚 Machine Learning, Deep Learning, Data Cleaning, EDA, Learn Python, Learn python packages such as Numpy, Pandas, Seaborn, Matplotlib, Plotly, Tensorfolw, Theano...., Linear Algebra, Big Data, Analysis Tools and solve some real problems such as predict house prices.

100-days-of-ml-code

100-Days-Of-ML-Code中文版

106-facial-landmark-detection

12306_code_server

该仓库用于构建自托管的12306验证码识别服务器

300

《唐诗三百首》数据

3d_garment_tryon_system

3D虚拟试衣系统

3ddfa

The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.

3ddfa_v2

The official PyTorch implementation of Towards Fast, Accurate and Stable 3D Dense Face Alignment, ECCV 2020.

3dmm-fitting

Fit 3DMM to front and side face images simultaneously.

3dmm-fitting-pytorch

A 3DMM fitting framework using Pytorch.

3dmm_cnn

Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network

81-facial-landmarks-detector

Detects 81 points on face , using openCV , Python and Dlib. In real-time

8cc

A Small C Compiler

a-pytorch-tutorial-to-image-captioning

Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning

a-recsys

A Tensorflow based implicit recommender system

abseil-cpp

Abseil Common Libraries (C++)

accelerate

🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

action-recognition-visual-attention

Action recognition using soft attention based deep recurrent neural networks

acwj

A Compiler Writing Journey

ad-papers

Papers on Computational Advertising

adabound

An optimizer that trains as fast as Adam and as good as SGD.

adamatting

a pytorch implementation of ICCV 2019 paper "Disentangled Image Matting"

adanet

Fast and flexible AutoML with learning guarantees.

ads-recsys-datasets

This repository collects some datasets for Ads & RecSys uses, and provide easy-to-use hdf5 iterative access.

adv_fin_ml_exercises

Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

advanced-deep-learning-with-keras

Advanced Deep Learning with Keras, published by Packt

advanced-deep-trading

Mostly experiments based on "Advances in financial machine learning" book

advances-in-financial-machine-learning

Notes on Advances in Financial Machine Learning

adversarial-recommender-systems-survey

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 74 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.

adversarial_examples

Code for the NIPS2017 competition on adversarial examples