5921 Open source projects that are alternatives of or similar to Ai Projects

Installations for Data Science. Anaconda, RStudio, Spark, TensorFlow, AWS (Amazon Web Services).

Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.

Source code accompanying: BigQuery: The Definitive Guide by Lakshmanan & Tigani to be published by O'Reilly Media

전 세계의 멋진 케라스 문서 및 튜토리얼을 한글화하여 케라스x코리아를 널리널리 이롭게합니다.

My attempt at reproducing the paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

A crash course in six episodes for software developers who want to become machine learning practitioners.

AWS Retail Demo Store is a sample retail web application and workshop platform demonstrating how AWS infrastructure and services can be used to build compelling customer experiences for eCommerce, retail, and digital marketing use-cases

A python package that includes many methods for decoding neural activity

Ever wondered how to code your Neural Network using NumPy, with no frameworks involved?

Weakly Supervised Segmentation with Tensorflow. Implements instance segmentation as described in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).

A PyTorch implementation of PointRend: Image Segmentation as Rendering

Python solutions to solve practical business problems.

Code for our paper "Hamiltonian Neural Networks"

《统计学习方法》（第一版）习题解答，在线阅读地址：https://datawhalechina.github.io/statistical-learning-method-solutions-manual

Code to synthesise textures using convolutional neural networks as described in Gatys et al. 2015 (http://arxiv.org/abs/1505.07376)

Code and Dataset for CVPR2020 "Dynamic Refinement Network for Oriented and Densely Packed Object Detection"

Deep learning framework for wearable activity recognition based on convolutional and LSTM recurretn layers

Fast, flexible and easy to use probabilistic modelling in Python.

Machine Learning and Deep Learning Resources

Python version of Quantiacs toolbox and sample trading strategies

Exploration of methods for coloring t-SNE.

Jupyter Notebooks for Computational Linear Algebra course, taught summer 2018 in USF MSDS program

SuperComputing 2017 Deep Learning Tutorial

Deep fusion project of deeply-fused nets, and the study on the connection to ensembling

Jupyter notebooks associated with the Algorithms for Optimization textbook

Implementation of Google Brain's WaveGrad high-fidelity vocoder (paper: https://arxiv.org/pdf/2009.00713.pdf). First implementation on GitHub.

Ray tutorials from Anyscale

Code and examples for JHU Computational Genomics class

Jupyter notebooks and other materials developed for the Columbia course APMA 4300

Hands-On Computer Vision with TensorFlow 2, published by Packt

Exercises for the Analysis of Knowledge Graphs

Detect text in natural images with SSD, Single Shot Detection

CNN image style transfer 🎨.

Using Project Jupyter for data science.

exercise for nndl

Transfer learning for music classification and regression tasks

从零基础开始机器学习之旅

Tacotron 2 - PyTorch implementation with faster-than-realtime inference

Use the Mask RCNN for the human pose estimation

Food detection and recommendation with deep learning

Stock for Deep Learning and Machine Learning

Python Bootcamp docs and lectures (UC Berkeley)

Repository with sample code and instructions for "Continuous Intelligence" and "Continuous Delivery for Machine Learning: CD4ML" workshops

Deep learning system course

Machine Learning and NLP: Text Classification using python, scikit-learn and NLTK

Visual Search using Apache MXNet and gluon

Based on the Pytorch-Transformers library by HuggingFace. To be used as a starting point for employing Transformer models in text classification tasks. Contains code to easily train BERT, XLNet, RoBERTa, and XLM models for text classification.

Graph Convolution Network for NLP

An implementation of some of the tools used by the winner of the box plots competition using scikit-learn.

Practical Exercise Codes for PyTorch

Case studies, examples, and exercises for learning to deploy ML models using AWS SageMaker.

An implementation of "mixup: Beyond Empirical Risk Minimization"

Repeatable analysis plugin for Jupyter notebook

A minimal Jupyter Notebook theme

Tutorial on normalizing flows.

This repository contains my python (3) script examples that focus on use cases for Network Engineers.

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