6485 Open source projects that are alternatives of or similar to Csa Inpainting

Python tutorials for introduction to machine learning

Apache Spark (PySpark) Practice on Real Data

Multi-Graph Convolutional Neural Networks

High-speed simulator of convolutional spiking neural networks with at most one spike per neuron.

Example of "biological" learning for MNIST

Lecture slides, Jupyter notebooks, and other material from the LSSTC Data Science Fellowship Program

OpenCV in Python for lane line and vehicle detection/tracking in autonomous cars

RadIO is a library for data science research of computed tomography imaging

Various Notebooks for Lernapparat.de

This trading-gym is the first trading for agent to train with episode of short term trading itself.

Code for NeurIPS 2019 paper: "Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes"

Ropsten public testnet PoW chain

Intro to deep learning for medical imaging lesson, by MD.ai

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Go binding for TensorFlow Lite

A beginner-friendly project to help you in open-source contributions. Made specifically for contributions in HACKTOBERFEST 2020! Hello World Programs and Algorithms! Please leave a star ⭐ to support this project! ✨

An easy to use blogging platform, with enhanced support for Jupyter Notebooks.

Materials for PyCon 2017 presentation on optimizing Pandas code

Build a CNN network to predict 3D bounding box of car from 2D image.

Python for Finance Cookbook, published by Packt

Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

线性代数笔记

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection.

Materials for a short course on convex optimization.

LargitData Course Material

w-net: a convolutional neural network architecture for the self-supervised learning of depthmap from pairs of stereo images.

This is a tutorial for PyTorch Geometric on the YooChoose dataset

python tutorial

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Data, Benchmarks, and methods submitted to the M5 forecasting competition

Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs (CVPRW 2020)

Tutorial: Web scraping in Python with Beautiful Soup

🎄 Virtual DOM for Python

Library extending Jupyter notebooks to integrate with Apache TinkerPop and RDF SPARQL.

Run any standard Python code quality tool on a Jupyter Notebook

Code for a workshop on statistical interference using computational methods in Python.

A collection of tools to collect and download various data.

A centralized repository to report scikit-learn model performance across a variety of parameter settings and data sets.

Reference deployment of JupyterHub and nbgrader on a single server

Traffic Sign Detection. Code for the paper entitled "Evaluation of deep neural networks for traffic sign detection systems".

Python code for the fast bilateral solver

just some code snippet

基于深度学习的CTR预估，从FM推演各深度学习CTR预估模型（附代码）

Auto-ReID and Other Person Re-Identification Projects

Code snippets from Kite blog posts

A Repository of Notebooks for the Python Lecture Site

Open source ultrasound processing modules and building blocks

Çeşitli kütüphaneler kullanılarak Türkçe kod açıklamalarıyla TEMEL SEVİYEDE pratik derin öğrenme uygulamaları.

Getting start with PySpark and MLlib

Image Captions Generation with Spatial and Channel-wise Attention

Visualizations based on best open science practices.

Python/Keras implementation of integrated gradients presented in "Axiomatic Attribution for Deep Networks" for explaining any model defined in Keras framework.

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models