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Synthetic data generation for tabular data

A graph-based functional API for building complex scikit-learn pipelines.

Neuraxle ⭐ 533

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.

基于自然语言理解与机器学习的聊天机器人，支持多用户并发及自定义多轮对话

Ai_composer ⭐ 275

AI Composer for Machine Learning for Hackers #2

Devops Roadmap ⭐ 266

DevOps methodology & roadmap for a devops developer in 2019. Interesting books to learn new technologies.

Programl ⭐ 253

A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

Aws Step Functions Data Science Sdk Python ⭐ 203

Step Functions Data Science SDK for building machine learning (ML) workflows and pipelines on AWS

Lightweight, Python library for fast and reproducible experimentation 🔬

Nanonets Ocr Sample Python ⭐ 129

NanoNets OCR API Example for Python

Tools for using computer algebra systems to solve math problems step-by-step with reinforcement learning

Cloud Ml Examples ⭐ 123

A collection of Machine Learning examples to get started with deploying RAPIDS in the Cloud

Applied Machine Learning ⭐ 117

A step-by-step guide to get started with Applied Machine Learning

Smackage ⭐ 112

Smackage Package Manager for Standard ML

Mac Graph ⭐ 105

The MacGraph network. An attempt to get MACnets running on graph knowledge

Easy Yolo ⭐ 91

Yolo (Real time object detection) model training tutorial with deep learning neural networks

Deepracer Waypoints Workshop ⭐ 88

AWS DeepRacer Free Student Workshop: Run faster by using your custom waypoints. Step by Step to learn reinforcement learning,

Procedurallocomotion ⭐ 63

Automating human walk cycles using machine learning

Codesnippetsearch ⭐ 63

Neural bag of words code search implementation using PyTorch and data from the CodeSearchNet project.

Example templates for the delivery of custom ML solutions to production so you can get started quickly without having to make too many design choices.

Deep Learning Walkthrough ⭐ 60

Slides showing a walkthrough of the tutorial by kozyrkov and bkungfoo available at github.com/google-aai/sc17.

Slik_python_package ⭐ 60

A data wrangling and modeling tool.

Code Bert ⭐ 48

Automatically check mismatch between code and comments using AI and ML

Mujocounity ⭐ 48

Reproducing MuJoCo benchmarks in a modern, commercial game /physics engine (Unity + PhysX).

Tensorpy ⭐ 42

Easy Image Classification with TensorFlow

Ml_dev_env ⭐ 39

Machine Learning / Deep Learning Environment. Everywhere. Anywhere.

A python multi-variate time series prediction library working with sklearn

Adasoptimizer ⭐ 33

ADAS is short for Adaptive Step Size, it's an optimizer that unlike other optimizers that just normalize the derivative, it fine-tunes the step size, truly making step size scheduling obsolete, achieving state-of-the-art training performance

Machine- and Reinforcement Learning ExtensioN for (game) Engines

Torchtracer ⭐ 27

A python package for visualization and storage management in a pytorch AI task.

Ml Pipeline Generator Python ⭐ 24

ML Pipeline Generator is a tool for generating end-to-end pipelines composed of GCP components so that any customer can easily migrate their local ML models onto GCP and start realizing the benefits of the cloud quickly.

Code for AutoGP

Roundprogressbar ⭐ 23

一个自定义的圆形可颜色渐变的ProgressBar

Text2imagenet ⭐ 21

Generate image from text with Generative Adversarial Network

Babewatch ⭐ 20

Laravel Application using Machine Learning to ensure a 👶🏾 is secure

Tensorflow_tutorial ⭐ 20

Step by step, Let you learn how to use tensorflow in practical.

Pulse Iot Wml Mobile Health ⭐ 17

WARNING: This repository is no longer maintained ⚠️ This repository will not be updated. The repository will be kept available in read-only mode.

Imagelabelview ⭐ 16

A view for data-labeling(a tool for machine learning).

Framework for data processing

Machine Learning Experiment Manage Platform

Swiftaveiro Sneakersdetector ⭐ 15

Data Scientist In Python ⭐ 15

This repository contains notes and projects of Data scientist track from dataquest course work.

ADENINE: A Data ExploratioN PipelINE

Solar Panel Segmentation ⭐ 13

A U-Net for solar panel identification and segmentation

Ml Workshop March ⭐ 12

🧲 Multi-step adaptive estimation for reducing false positive selection in sparse regressions

Workshop_2018_dsgo ⭐ 12

DataScienceGO 2018 - Machine Learning Workshop

Pyspark_pipes ⭐ 11

Helper functions for building complex Spark ML pipelines

Sbsps Challenge 1160 Predicting The Energy Output Of Wind Turbine Based On Weather Condition ⭐ 9

Predicting Energy Output of Wind Turbine

Notes for HungYi Lee - The Next Step for Machine Learning

Predict_tc ⭐ 9

Predict the Critical Temperature of a Superconductor

A Python step-by-step primer for Machine Learning and Optimization

Machine Learning Based Classification Of Schizophrenic And Healthy Control Using Resting State Fmri ⭐ 8

My Bachelor's Thesis on Automated Classification of Schizophrenic from Healthy subjects by analyzing Resting State Networks using Machine Learning, Signal Processing and Computer Vision algorithms

CNN-based image classification of AntWeb images

Repo for practical data science problems approaches, including notebook demo and working scripts | #DS | #analysis

Simpledeepnettoolbox ⭐ 7

Simple MATLAB toolbox for deep learning network: Version 1.0.3

Interpret all the models - a genetic optimization approach to model agnostic black box explanations based on MAGIX.

Autoencoder ⭐ 7

Simple Implementation of AutoEncoder, one type of deep learning algorithm. This is implemented based on Tensorflow.

Load2python ⭐ 6

生命太短暂，不如用Python。将Python用于机器学习、网络爬虫、图像处理，我的Python学习

All code related to the LoopMC paper

Geneticlearning ⭐ 6

Machine and Deep Learning algorithms to replace multi-objective functions in Genetic Algorithms

Tmleworkshop ⭐ 6

Targeted maximum likelihood estimation (TMLE) enables the integration of machine learning approaches in comparative effectiveness studies. It is a doubly robust method, making use of both the outcome model and propensity score model to generate an unbiased estimate as long as at least one of the models is correctly specified.

Mergenet Filter Ldr2hdr ⭐ 6

MergeNet-filter-ldr2hdr, detail in paper 《Reconstructing HDR Image from a Single Filtered LDR Image Base on a Deep HDR Merger Network》

Aceleradev Ds ⭐ 6

A repo to store my walkthrough through the CodeNation's program, AceleraDev Data Science

Machinelearningroadmapusingpython ⭐ 6

An updated R package deeper: Deep Ensemble for Environmental Predictor

Qrreader Android ⭐ 5

QR reader library with CameraX & Firebase ML for Android.

A simplified implement of Adaptive boosting.

Image Classification Sample Python ⭐ 5

NanoNets Image Classification API Example for Python

Moodle.ai ⭐ 5

Python, ML, DL resources and more...

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