Top 5939 jupyter-notebook open source projects

A very lightweight Python wrapper to the Philips Hue API

Experiments, algorithms and reports in machine learning reseach.

Machine Learning Tutorials in Python

Code for Siraj Raval's Coding Challenges!

中文文本错别字检测以及自动纠错 / Autochecker & autocorrecter for chinese

Survival analsyis and time-to-failure predictive modeling using Weibull distributions and Recurrent Neural Networks in Keras

Principles of Machine Learning Python

Deep learning infrastructure for bioinformatics

复杂网络研究资源整理和基础知识学习

Material for the tutorial: "Deep Diving into GANs: from theory to production"

COMS W4995 Applied Machine Learning - Spring 20

Python is a multi-paradigm programming language. Meaning, it supports different programming approach. One of the popular approach to solve a programming problem is by creating objects. This is known as Object-Oriented Programming (OOP).

努力成为一名合格有水平的AI算法工程师

This Jupyter Notebook will help you downloading Coursera videos, subtitles and quizzes (but not answering the quiz). It will automatically download and convert vtt subtitle files into srt. All resources downloaded are numbered according to their sequence.

This is the code for "LSTM Networks - The Math of Intelligence (Week 8)" By Siraj Raval on Youtube

北京邮电大学“网络搜索原理”课程资料(2019)

Utilities for preprocessing text for deep learning with Keras

MLSS 2018 Madrid lecture materials on Generative Adversarial Networks (GANs)

Progressive training of GANs with Mutual Information Penalty

quizzes/assignments for mathematics for machine learning specialization on coursera

a sklearn wrapper for Google's BERT model

Deep learning course CE7454, 2019

A curated list of Machine Learning and Deep Learning tutorials in Jupyter Notebook format ready to run in Google Colaboratory

Azure Machine Learning Lab Notebooks

A practical feature engineering handbook

Yolov5 real time smoke detection system

A framework for hydrodynamics explorations and prototyping

免费学代码系列：小白python入门、数据分析data analyst、机器学习machine learning、深度学习deep learning、kaggle实战

Project which implements extraction of data from scatter plots

网易云课堂终于官方发布了吴恩达经过授权的汉化课程-“”深度学习专项课程“”，这是自己做的一些笔记以及代码。下为网易云学习链接

吴恩达机器学习coursera课程，学习代码(2017年秋) The Stanford Coursera course on MachineLearning with Andrew Ng

top 1% solution to toxic comment classification challenge on Kaggle.

Previous version of eeg-notebooks

A CNN Variational Autoencoder (CNN-VAE) implemented in PyTorch

This notebook shows how to implement LibFM in Keras and how it was used in the Talking Data competition on Kaggle.

Kotlin API for Lets-Plot - an open-source plotting library for statistical data.

A simple NLP library allows profiling datasets with one or more text columns. When given a dataset and a column name containing text data, NLP Profiler will return either high-level insights or low-level/granular statistical information about the text in that column.

Juliabox continues to run, but this codebase is no longer current.

SegNet model implemented using keras framework

Quantitative Interview Preparation Guide, updated version here ==>

Mask R-CNN for Pulmonary Nodules Diagnosis, using TensorFlow 天池医疗AI大赛：Mask R-CNN肺部结节智能检测（Segmentation + Classification）

This is the official repository for the Recommender Systems course at Politecnico di Milano.

The BIGGAN based Anime generation implemented with tensorflow. All training data has been open sourced.

This is Andrew NG Coursera Handwritten Notes.

Data preparation and loader for AMASS

该存储库包含由deeplearning.ai提供的相关课程的个人的笔记和实现代码。

Run M Files from Python - GNU Octave to Python bridge

This repository contains Ipython notebooks of assignments and tutorials used in the course introduction to data science in python, part of Applied Data Science using Python Specialization from University of Michigan offered by Coursera

A wrapper of the COCOeval class for extended keypoint error estimation analysis.

InfiniteBoost: building infinite ensembles with gradient descent

Hands-On Machine Learning with Scikit-Learn & TensorFlow (O'Reilly)

코딩셰프의 3분 딥러닝, 케라스맛

A set of tutorials for torchtext

Machine Learning for Finance, published by Packt

Methods with examples for Feature Selection during Pre-processing in Machine Learning.

1st place solution for the Kuaishou Active-user Forecast competition