6116 Open source projects that are alternatives of or similar to Boltzmann Machines

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

CmdStanR: the R interface to CmdStan

Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"

PyTorch implementation of "Weight Uncertainty in Neural Networks"

500,000 multimodal short video data and baseline models. 50万条多模态短视频数据集和基线模型（TensorFlow2.0）。

Make a sweet giant triangle confusogram (GTC) plot

Using neural network to build expressive hierarchical distribution; A variational method to accurately estimate posterior uncertainty; A fast and general method for Bayesian inference. (ICML 2018)

GPstuff - Gaussian process models for Bayesian analysis

Bayesian Data Analysis demos for Python

DGMs for NLP. A roadmap.

Understanding normalizing flows

Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code

Bayesian methods in deep learning Summer School

Seminars DeepBayes Summer School 2018

Statistical Rethinking (2nd ed.) with NumPyro

A R/Shiny app for interactive RNN tensorflow models

Regression with Summary Statistics.

Domain-Adversarial Neural Network in Tensorflow

Scalable inference for a generative model of astronomical images

Statistical Rethinking (2nd Ed) with Tensorflow Probability

Gathers Machine learning models using pure Numpy to cover feed-forward, RNN, CNN, clustering, MCMC, timeseries, tree-based, and so much more!

A repository to keep track of all the code that I end up writing for my blog posts.

Automatic Reparameterisation of Probabilistic Programs

Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..

Collection of probabilistic models and inference algorithms

Introduction to generative adversarial networks, with code to accompany the O'Reilly tutorial on GANs

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

Project for Digital Image Processing

Practice your pandas skills!

TensorFlow implementation of an arbitrary order Factorization Machine

《深入浅出图神经网络：GNN原理解析》配套代码

Swift for TensorFlow

A small library for automatically adjustment of text position in matplotlib plots to minimize overlaps.

Notes, examples, and Python demos for the textbook "Machine Learning Refined" (published by Cambridge University Press).

The Hitchhiker's Guide to Data Science for Social Good

Learn Julia via interactive tutorials!

35岁程序员退路之量化投资学习笔记

RStan, the R interface to Stan

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

Examples of matplotlib codes and plots

Learning numerical computing with notebooks for all ages.

记录Learning from data一书中的习题解答

How to Predict Stock Prices Easily - Intro to Deep Learning #7 by Siraj Raval on Youtube

Keras version of Realtime Multi-Person Pose Estimation project

Collection of tools and code examples to demonstrate best practices in using Amazon EC2 Spot Instances.

鉴于我没有时间继续写这个东西，这个项目暂时废止

Visualize and explore NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2).

Udemy üzerindeki Python kurslarında kullanılan materyaller

Code for finetuning AlexNet in TensorFlow >= 1.2rc0

Pytorch easy-to-follow Capsule Network tutorial

pytorch implementation of openpose including Hand and Body Pose Estimation.

3D U-Net model for volumetric semantic segmentation written in pytorch

ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

Lecture notes and code for Machine Learning practical course on CMC MSU

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

Solutions of Reinforcement Learning, An Introduction

Implementing Siamese networks with a contrastive loss for similarity learning