6583 Open source projects that are alternatives of or similar to Hamiltonian Nn

A framework for blind denoising with self-supervision.

Reference deployment of JupyterHub and nbgrader on a single server

Implementing of the "A Neural Algorithm of Artistic Style"

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）

[ECCV'2020] STTN: Learning Joint Spatial-Temporal Transformations for Video Inpainting

A Repository of Notebooks for the Python Lecture Site

Tensorflow notes

Visualizations based on best open science practices.

CARTO Python package for data scientists

mobilev2-yolov5s剪枝、蒸馏，支持ncnn，tensorRT部署。ultra-light but better performence！

Deep Learning / NLP tutorial for Chatbot Developers

Project containig related material for my TensorFlow articles

Paper: CVPR2018, Learning Rich Features for Image Manipulation Detection

Pytorch version of Realtime Multi-Person Pose Estimation project

Keras Implementation of The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation by (Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, Yoshua Bengio)

Some code examples gathered during my Month of Julia.

A seq2seq model that can correct spelling mistakes.

A web frontend for scheduling Jupyter notebook reports

Detecting and counting small objects - Analysis, review and application to counting

Simplified implementations of deep learning related works

爬虫+数据分析实战项目

Example Machine Learning Scripts for Numerai's Tournament

Code for training py-faster-rcnn and py-R-FCN on multiple GPUs in caffe

Representing research papers as vectors / latent representations.

A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.

FaceNet for face recognition using pytorch

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

A simpler version of the self-attention layer from SAGAN, and some image classification results.

[tutorial]A functional, Data Science focused introduction to Python

🚀 FREE AI Resources - 🎓 Courses, 👷 Jobs, 📝 Blogs, 🔬 AI Research, and many more - for everyone!

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

Tools to participate in the ActivityNet Challenge 2016 (NIPSW 2016)

Open Bandit Pipeline: a python library for bandit algorithms and off-policy evaluation

PyData Cookbook Project

Python Bootcamp docs and lectures (UC Berkeley)

TeachOpenCADD: a teaching platform for computer-aided drug design (CADD) using open source packages and data

Implementation of a seq2seq model for summarization of textual data. Demonstrated on amazon reviews, github issues and news articles.

统计学习方法训练营课程作业及答案，视频笔记在线阅读地址：https://relph1119.github.io/statistical-learning-method-camp

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

My personal notes, presentations, and notebooks on everything Deep Learning.

Tools for using a variational auto-encoder for latent image encoding and generation.

Convolutional Neural Network for Multi-label Multi-instance Relation Extraction in Tensorflow

Machine Learning Model for Sport Predictions (Football, Basketball, Baseball, Hockey, Soccer & Tennis)

Transfer learning for music classification and regression tasks

Deep learning for MIR

Simple plotting library that wraps Matplotlib and integrated with DataFrames

Fast and memory-efficient clustering

A collection of some of the neat math and physics tricks that I've collected over the last few years.

CardIO is a library for data science research of heart signals

Implemented digit detector in natural scene using resnet50 and Yolo-v2. I used SVHN as the training set, and implemented it using tensorflow and keras.

Russian language models for spaCy

An example attention network with simple dataset.

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

PyTorch implementation of VQ-VAE by Aäron van den Oord et al.

Multi-hop knowledge graph reasoning learned via policy gradient with reward shaping and action dropout

Coding the Machine Learning Tutorial for Learning to Learn

Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments