612 Open source projects that are alternatives of or similar to autoencoders_tensorflow

Automatic feature engineering using Generative Adversarial Networks using TensorFlow.

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A miniature lisp-like language for querying JSON-like structures. Tuned for clientside ML feature extraction.

Code & data accompanying the KDD 2017 paper "KATE: K-Competitive Autoencoder for Text"

This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

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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.

A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Use advanced feature engineering strategies and select best features from your data set with a single line of code.

Features selector based on the self selected-algorithm, loss function and validation method

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Flexible time series feature extraction & processing

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Comprehensive toolkit for generating various numerical features of protein sequences

Feature engineering package with sklearn like functionality

This repository contains the code related to Natural Language Processing using python scripting language. All the codes are related to my book entitled "Python Natural Language Processing"

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A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

Stock for Deep Learning and Machine Learning

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

🌐 Deep Embedded Self-Organizing Map: Joint Representation Learning and Self-Organization

This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation

Official adversarial mixup resynthesis repository

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A collection of non-residential buildings for performance analysis and algorithm benchmarking

Tensorflow 2.0 implementation of Adversarial Autoencoders

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

Julia package for automatic Bayesian inference on a factor graph with reactive message passing

A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.

Hierarchical, multi-label topic modelling with LDA

C++ implementation of the nonrigid descriptor Geobit presented at ICCV 2019 "GEOBIT: A Geodesic-Based Binary Descriptor Invariant to Non-Rigid Deformations for RGB-D Images"

Proto-RL: Reinforcement Learning with Prototypical Representations

A basic framework for pulmonary nodule detection and characterization in CT

Neural network library written in C and Javascript

Official repository for the ShapeFormer Project

Interactive Visual Machine Learning Demos.

Implementation of feature engineering from Feature engineering strategies for credit card fraud

Versatile Nonlinear Feature Selection Algorithm for High-dimensional Data

Pytorch implementation of "A Simple Framework for Contrastive Learning of Visual Representations"

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Simplified version of "State Representation Learning for Control: An Overview" bibliography

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Classifying time series using feature extraction

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A TensorFlow 2.x implementation of Masked Autoencoders Are Scalable Vision Learners

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The reference implementation of FEATHER from the CIKM '20 paper "Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models".

A common framework for implementing and using log densities for inference.

The Trendiness of Trends

This is my attempt to reproduce and extend the results in the paper "An Introduction to Deep Learning for the Physical Layer" by Tim O'Shea and Jakob Hoydis

AntroPy: entropy and complexity of (EEG) time-series in Python

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