5921 Open source projects that are alternatives of or similar to Pydata Book

scikit-learn wrappers for Python fastText.

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Tutorial covering Open Source tools for Source Separation.

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🏆 A ranked list of awesome Jupyter Notebook, Hub and Lab projects (extensions, kernels, tools). Updated weekly.

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Traffic Graph Convolutional Recurrent Neural Network

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Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).

Some code examples gathered during my Month of Julia.

edaviz - Python library for Exploratory Data Analysis and Visualization in Jupyter Notebook or Jupyter Lab

Simplified implementations of deep learning related works

A toolkit to learn how to model and interpret regulatory sequence data using deep learning.

A Pytorch Computer Vision template to quick start your next project! 🚀🚀

Keras implementation of the paper "3D MRI brain tumor segmentation using autoencoder regularization" by Myronenko A. (https://arxiv.org/abs/1810.11654).

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

What scientific programmers must know about CPUs and RAM to write fast code.

The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

A Python Matplotlib, Numpy library to manage wind data, draw windrose (also known as a polar rose plot), draw probability density function and fit Weibull distribution

Machine Learning notebooks for refreshing concepts.

Materials for GWU DNSC 6279 and DNSC 6290.

Simple plotting library that wraps Matplotlib and integrated with DataFrames

Pytorch implementation of set transformer

This is the code for "Neural DIfferential Equations" By Siraj Raval on Youtube

CardIO is a library for data science research of heart signals

A fast and lightweight AutoML library.

Official mirror of https://gitlab.com/lfortran/lfortran. Please submit pull requests (PR) there. Any PR sent here will be closed automatically.

A clear, concise, simple yet powerful and efficient API for deep learning.

Coding the Machine Learning Tutorial for Learning to Learn

This is an LSTM stock prediction using Tensorflow with Keras on top.

Compared performance of KNN, SVM, BPNN, CNN, Transfer Learning (retrain on Inception v3) on image classification problem. CNN is implemented with TensorFlow

An automatic beatmap generator using Tensorflow / Deep Learning.

Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets

Pytorch implementation of HighRes-net, a neural network for multi-frame super-resolution, trained and tested on the European Space Agency’s Kelvin competition.

Ciência de Dados aplicada à pandemia do novo coronavírus.

Temporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series

Data and code behind the articles and graphics at FiveThirtyEight

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

General Assembly's Data Science course in Washington, DC

Tensorflow notes

Statistical Rethinking (2nd ed.) with NumPyro

虾神的Python示例代码库

Weakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).