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

Videos of deep learning optimizers moving on 3D problem-landscapes

This repository shows a sample example to build, manage and orchestrate Machine Learning workflows using Amazon Sagemaker and Apache Airflow.

Code release for Hu et al. Segmentation from Natural Language Expressions. in ECCV, 2016

Faster-RCNN based on Densecap(deprecated)

Book sample (AI Machine-learning Deep-learning)

公众号“无痛入门音乐科技”开源代码

Breast Cancer Classification using CNN and transfer learning

Code for reproducing the results from the paper Few Shot Text Classification with a Human in the Loop

Place for resources used during the Mordor Detection hackathon event featuring APT29 ATT&CK evals datasets

“Hands-On Machine Learning with Scikit-Learn and TensorFlow” Excerpt From: Aurélien Géron. “Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.” iBooks.

GitHub Action for repo2docker

A community run, 5-day PyTorch Deep Learning Bootcamp

Game Theory and Python, a workshop investigating repeated games using the prisoner's dilemma

机器学习实战

机器学习与深度学习算法示例

自然语言处理NLP在中文文本上的一些应用，如文本分类、情感分析、命名实体识别等

Code for my EMNLP 2018 paper "SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach"

A lightweight book theme built off of the pydata sphinx theme

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

Discovering Interpretable GAN Controls [NeurIPS 2020]

Experiments with Deep Learning

An LSTM Autoencoder for rare event classification

Multi-purpose tool for sound design and music production implemented in TensorFlow.

Repository for reading Pascal VOC data in Python, rather than requiring MATLAB to read the XML files.

capsule networks that achieves outstanding performance on the German traffic sign dataset

Distributed deep learning on Hadoop and Spark clusters.

DEPRECATED: please see individual lesson repositories for current material.

Atom notebook

https://plot.ly/dash/gallery/live-wind-data/

Gamified tutorial for the QISKit quantum SDK

p5_VehicleDetection_Unet

Deliverable: This Jupyter notebook will help aspiring data scientists learn and practice the necessary python code needed for many data science projects.

MAGNet: Multi-agents control using Graph Neural Networks

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.

Generative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation

Automagically remove trends from time-series data

Repository which contains various scripts and work with various basketball statistics

Designing Deep neural network architectures using topologies from the world of Complex Networks/network Science

PyTorch tutorials A to Z

Original implementation of the paper "SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery" by Shion Honda et al.

Course materials for: Introduction to Data Science and Programming

Código para el curso "Aprende Data Science y Machine Learning con Python"

A collection of code examples as well as presentations for training purposes

This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.

100+ python programming exercise problem discussed ,explained and solved in different ways

Implementation of the paper - Generative Recurrent Networks for De Novo Drug Design.

Koç University deep learning framework.

FCOS: Fully Convolutional One-Stage Object Detection.

[BA project] Dynamic Pricing Optimization for Airbnb listing to optimize yearly profit for host. Use Clustering for competitive analysis, kNN regression for demand forecasting, and find dynamic optimal price with Optimization model.

AIND Term 2 -- VUI Capstone Project

Exploring applications of deep learning to the world of wine

Models and Pipelines for the Spark NLP library

基于李笑来的那本自学是一门手艺的书，然后里面有自己修改的痕迹，以及更多的资料。