wesszumino Goto Github PK

probability

Probabilistic reasoning and statistical analysis in TensorFlow

pyquil

A Python library for quantum programming using Quil.

pytorch-balanced-batch

A pytorch dataset sampler for always sampling balanced batches.

pytorch-hessian-eigenthings

Efficient PyTorch Hessian eigendecomposition tools!

qabom

Simple demo of logic used in https://arxiv.org/abs/1712.05304

quantumflow

QuantumFlow: A Quantum Algorithms Development Toolkit

quantumkatas

Programming exercises for learning Q# and quantum computing

random-matrix-theory-for-pedestrians

ray

A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

recommenders

Recommender Systems

reference-qvm

A reference implementation for a quantum virtual machine in Python

reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

restricted-boltzmann-machine

Restricted Boltzmann Machine for collaborative filtering of movies.

rmt

routing

searching-for-activation-functions

NIPS Implementation challenge 2017 for "Searching for activation functions" paper by Google Brain

semantics-aligned-representation-learning-for-person-re-identification

This is an implementation of AAAI'20 paper "Semantics-Aligned Representation Learning for Person Re-identification". We leverages dense semantics to address both the spatial misalignment and semantics misalignment challenges in person re-identification.

siamdw

Deeper and Wider Siamese Networks for Real-Time Visual Tracking

spm-tracker

This is the implementation of CVPR'19 paper "SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking"

stat-mech-van

Code for Solving Statistical Mechanics using Variational Autoregressive Networks.

statistical-mechanics

Jupyter Notebooks of Leonard Susskind's Statistical Mechanics lectures

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

substochastic-sat

Substochastic Monte Carlo applied to MAX-SAT

sumy

Module for automatic summarization of text documents and HTML pages.

swa

Stochastic Weight Averaging in PyTorch

tensorflow

Computation using data flow graphs for scalable machine learning

tensorflow-1x-deep-learning-cookbook

TensorFlow 1.x Deep Learning Cookbook, published by Packt

tensorflow-dataset-tutorial

Notebook for my medium article about how to use Dataset API in TensorFlow

tensorflow-examples

TensorFlow Tutorial and Examples for Beginners with Latest APIs