5921 Open source projects that are alternatives of or similar to Slow_disentanglement

Find the solution of machine learning competitions I am participating.

Curso sobre Aprendizaje Profundo por Refuerzo en ECI 2019

《深度学习原理与实践》相关代码——source code of the book <deep learning in action>

Repo for Pierian Data course on Udemey

Remote photoplethysmography measures heart rate of a person without any contact, from their video. This is an implementation of rPPG called as Plane Orthogonal-to-Skin (POS) as described in the IEEE paper - "Algorithmic Principles of Remote PPG," W. Wang, A. C. den Brinker, S. Stuijk and G. de Haan.

Python Helper library for Jupyter Notebooks

Experiments for Jeremy Howard's deep learning courses

Network Analysis for Financial Markets

The source repository is at https://github.com/full-stack-deep-learning/fsdl-text-recognizer

Utilities for processing the xView 2018 dataset (i.e., xview1)

Bayesian Regression Models in Pyro

Notebooks and files for the Python for Journalists course on Datajournalism.com

Code examples from the book, Python 101 by Michael Driscoll

My solution to the book A Collection of Data Science Take-Home Challenges

A simple optical illusion in python

Thailand Election'62 Candidate Information Browsing Website

Face Recognition using Neural Networks implemented using Keras

Course Materials Big Data and Automated Content Analysis

Inspired by word2vec-pride-vis the replacement of words of Russian most valuable novels text with closest word2vec model words. By Boris Orekhov

Covid19-forecast

Computer vision sabbatical study materials

Code for 'The Grand Atari Challenge dataset' paper

基于YOLO-lite的web实时人脸检测，tfjs人脸检测，目标检测

🔥 Introductory PyTorch tutorials with OReilly Media.

My own solutions for Stanford CS231n (2017) assignments

Hyperbolic Visual Embedding Learning for Zero-Shot Recognition (CVPR 2020)

使用keras搭建seq2seq完成中英文翻译

Toolbox repository for Tiger team

These are praat scripts I use in my research, implemented in parselmouth for python for use in binder

DEEP-LEARNING-practical-introduction-with-Keras

This is the 1st place solution for AV Intel scene classification challenge

🐍 Python visualization tools for openPMD files

A set of examples for using open source tools on IBM i

A system for assigning and grading notebooks

Introduction to root_numpy, pandas, Keras in simple Deep Learning application at CERN

This easy-to-use library splits images and its bounding box annotations into tiles, both into specific sizes and into any arbitrary number of equal parts. It can also resize them, both by specific sizes and by a resizing/scaling factor.

Adversarial Workshop code and presentation

This toolbox is support material for the book on CNN (http://www.convolution.network).

Multimodal speech recognition using lipreading (with CNNs) and audio (using LSTMs). Sensor fusion is done with an attention network.

本项目是TensorFlow2.0学习笔记，主要参考官方文档，此外也添加个人许多个人使用心得体会等内容，本项目所有笔记也发布在博客园等平台，希望对你有所帮助。

Content for Meetups for DATA & AI - Microsoft DFW

Procedural terrain generation for video games has been traditionally been done with smartly designed but handcrafted algorithms that generate heightmaps. We propose a first step toward the learning and synthesis of these using recent advances in deep generative modelling with openly available satellite imagery from NASA.

Another attempt to use Deep-Learning in the financial markets

Learn to build a basic machine learning model from scratch with this repo and tutorial series.

keras를 이용한 딥러닝 기초 학습

Quilt is a self-organizing data hub for S3

🎓 Educational application of Hidden Markov Model to Autonomous Driving 🚕🚙🚗

From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Neural Networks (DDCNN)

Urban sound source tagging from an aggregation of four second noisy audio clips via 1D and 2D CNN (Xception)

An analysis of different intelligent control techniques (evolutionary alg., reinf. learn., neural networks) applied to robotic manipulators.

Compute homographies with deep networks instead of feature matching and RANSAC.

The Foundations of Computer Science XSeries, offered by the M.I.T. Department of Electrical Engineering and Computer Science, is a sequence of courses that introduce key concepts of computer science and computational thinking. Students apply these concepts and build their engineering skills by completing software and hardware design problems. Additionally, students test their understanding by taking a series of exams.

🍣 Implementations of fundamental algorithms and data structures. Happy Hacktoberfest!

DL course co-developed by YSDA, HSE and Skoltech

Few of the essential image augmentation techniques.

Sources for some videos