6182 Open source projects that are alternatives of or similar to 2017 Ccf Bdci Enterprise

Some awesome AI related books and pdfs for learning and downloading, also apply some playground models for learning

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

2017-CCF-BDCI-让AI当法官(初赛)：7th/415 (Top 1.68%)

Introduction to Data Science

H2O.ai Machine Learning Interpretability Resources

Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.

Python implementation of frequent subgraph mining algorithm gSpan. Directed graphs are supported.

open source for wechat-official-account (ID: PyDataLab)

Scraping statistics, predicting NBA player performance with neural networks and boosting algorithms, and optimising lineups for Draft Kings with genetic algorithm. Capstone Project for Machine Learning Engineer Nanodegree by Udacity.

Python library for reading and writing well data using Log ASCII Standard (LAS) files

IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018

Analyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.

👨🏽‍🏫You can learn about what data science is and why it's important in today's modern world. Are you interested in data science?🔋

Code of the IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018 [read-only repository]

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

The "Python Machine Learning (1st edition)" book code repository and info resource

A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

Materials for GWU DNSC 6279 and DNSC 6290.

Machine learning fundamentals lesson in interactive notebooks

Materials for workshops on text mining, machine learning, and data visualization

用户贷款风险预测

A book about machine learning, statistics, and data mining for heliophysics

2018年全国大学生计算机应用能力大赛之住房月租金预测第一名代码

Tensorflow Object Detector

Korean Language Model을 이용한 심리상담 대화 언어 모델.

A simple visual tracking interface using Python

Embed strange attractors using a regularizer for autoencoders

NIPS 2017: ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

The Biggest Scala Cheat-Sheet.

Code for the paper "Controlling Style and Semantics in Weakly-Supervised Image Generation", ECCV 2020

Source code of our TIP 2018 paper "Object-Part Attention Model for Fine-grained Image Classification"

Kaggle Toxic Comments Challenge

基于ltp的简单评论观点抽取模块

Road to Machine Learning

Mediterranean Machine Learning school tutorials

Pangeo Example Notebooks

This is the code for "Quantum Machine Learning LIVE" By Siraj Raval on Youtube

http://zeroviscosity.com/category/d3-js-step-by-step

Experimenting with and teaching probabilistic programming

Neural reparameterization improves structural optimization

Slides and materials for most of my talks by year

Peregrine: Fast Genome Assembler Using SHIMMER Index

Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker

MIMIC Code Repository: Code shared by the research community for the MIMIC-III database

Export Spotify playlists using the Web API. Analyze them in the Jupyter notebook.

A chatbot built in python using Selenium module.

Augmented environments with RL

Contains the code and deck for the presentation on Applying Deep Transfer Learning for NLP in Analytics Vidhya's DataHack Summit 2019

Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)

Scalable Graph Neural Networks with Deep Graph Library

ISMIR 2020 Tutorial for Metric Learning in MIR

Improving Convolutional Networks via Attention Transfer (ICLR 2017)

Exercises from 'Introduction to Statistical Learning with Applications in R' written in Python.

A middle-to-high level algorithm book designed with coding interview at heart!