1669 Open source projects that are alternatives of or similar to deep-blueberry

Lets Learn AI

This is an open sourced book on deep learning.

In this repo I will try to gather all of the projects related to data science with clean datasets and high accuracy models to solve real world problems.

[ WSDM '19 ] Sequential Variational Autoencoders for Collaborative Filtering

This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch

【不定期更新】收集整理的一些网站中（如知乎、Quora、Reddit、Stack Exchange等）与深度学习、机器学习、强化学习、数据科学相关的有价值的问题

TensorFlow and Deep Learning Tutorials

A curated list of almost all machine learning algorithms and deep learning algorithms grouped by category.

Machine Learning notebooks for refreshing concepts.

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

Simple (but often Strong) Baselines for POMDPs in PyTorch - ICML 2022

PyTorch tutorial for learners

Code for the paper: Benhenda, M. 2017. ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? arXiv preprint arXiv:1708.08227.

A tensorflow implementation of "Generating Sentences from a Continuous Space"

Deep learning codes and projects using Python

Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch

CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning

Top 100 trending deep learning repositories sorted by the number of stars gained on a specific day.

PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Learning (GAIL). Fast Fisher vector product TRPO.

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

Deep Reinforcement Learning based Trading Agent for Bitcoin

PyTorch Implementation of the RDPG (Recurrent Deterministic Policy Gradient)

Assessing Generative Models via Precision and Recall (official repository)

Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

"Learning Machine Learning" Course, Bogotá, Colombia 2019 #LML2019

Cloud-native Financial Reinforcement Learning

A tutorial on how to implement models for part-of-speech tagging using PyTorch and TorchText.

videos, lectures, blogs for Deep Learning

深度学习与PyTorch入门实战视频教程 配套源代码和PPT

Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network

iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press

A curated list of dedicated resources and applications

This is just a collection of projects that made during my DEEPLEARNING NANODEGREE by UDACITY

RAD: Reinforcement Learning with Augmented Data

Code base for solving Markov Decision Processes and Reinforcement Learning problems using Recurrent Convolutional Neural Networks.

A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

Official implementation of the paper "Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning Algorithms": https://arxiv.org/abs/1909.01779 To appear at the next NeurIPS2019 DRL-Workshop

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

bitcoin prediction algorithms

Data Science & Machine Learning projects and tutorials in python from beginner to advanced level.

Collection of lectures and lab lectures on machine learning and deep learning. Lab practices in Python and TensorFlow.

PyTorch implementation of Deterministic Generative Adversarial Imitation Learning (GAIL) for Off Policy learning

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

🤖 Implements of Reinforcement Learning algorithms.

Various cognitive api for machine learning, vision, language intent alalysis. Covers traditional as well as deep learning model design and training.

Top 200 deep learning Github repositories sorted by the number of stars.

The aim of this repository is to show a baseline model for text classification by implementing a LSTM-based model coded in PyTorch. In order to provide a better understanding of the model, it will be used a Tweets dataset provided by Kaggle.

🍃 Using a Neural Network to improve web accessibility in JavaScript.

A Python toolkit for Reservoir Computing and Echo State Network experimentation based on pyTorch. EchoTorch is the only Python module available to easily create Deep Reservoir Computing models.

Recurrent and multi-process PyTorch implementation of deep reinforcement Actor-Critic algorithms A2C and PPO

A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

The Cutest Deep Learning Framework which is also a wonderful Declarative Language

Deep & Classical Reinforcement Learning + Machine Learning Examples in Python

Deep Learning using Rectified Linear Units (ReLU)

PyTorch/Tensorflow solutions for Stanford's CS231n: "CNNs for Visual Recognition"

Get started with Machine Learning in TensorFlow with a selection of good reads and implemented examples!

Python package containing all custom layers used in Neural Networks (Compatible with PyTorch, TensorFlow and MegEngine)

Remove watermark automatically(Just can use for fixed position watermark till now). 自动水印消除算法的实现（目前只支持固定水印位置）。

Automate swing trading using deep reinforcement learning. The deep deterministic policy gradient-based neural network model trains to choose an action to sell, buy, or hold the stocks to maximize the gain in asset value. The paper also acknowledges the need for a system that predicts the trend in stock value to work along with the reinforcement …