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Machine-Learning-Tutorials - machine learning and deep learning tutorials, articles and other resources


This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list. If you want to contribute to this list, please read Contributing Guidelines.

ludwig - Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code

  •    Python

Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is a CSV file containing your data, a list of columns to use as inputs, and a list of columns to use as outputs, Ludwig will do the rest. Simple commands can be used to train models both locally and in a distributed way, and to use them to predict on new data.

nsfwjs - NSFW detection on the client-side via TensorFlow.js

  •    Javascript

A simple JavaScript library to help you quickly identify unseemly images; all in the client's browser. NSFWJS isn't perfect, but it's pretty accurate (~90% from our test set of 15,000 test images)... and it's getting more accurate all the time. Why would this be useful? Check out the announcement blog post.

AIDL-Series - :books: Series of Artificial Intelligence & Deep Learning, including Mathematics Fundamentals, Python Practices, NLP Application, etc


:books: Series of Artificial Intelligence & Deep Learning, including Mathematics Fundamentals, Python Practices, NLP Application, etc. 💫 人工智能与深度学习实战,机器学习篇 | Tensoflow 篇

gorse - A High Performance Recommender System Package based on Collaborative Filtering for Go

  •    Go

More examples could be found in the example folder. All models are tested by 5-fold cross validation on a PC with Intel(R) Core(TM) i5-4590 CPU (3.30GHz) and 16.0GB RAM. All scores are the best scores achieved by gorse yet.

tensorwatch - Debugging, monitoring and visualization for Deep Learning and Reinforcement Learning

  •    Jupyter

TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning. It fully leverages Jupyter Notebook to show real time visualizations and offers unique capabilities to query the live training process without having to sprinkle logging statements all over. You can also use TensorWatch to build your own UIs and dashboards. In addition, TensorWatch leverages several excellent libraries for visualizing model graph, review model statistics, explain prediction and so on. TensorWatch is under heavy development with a goal of providing a research platform for debugging machine learning in one easy to use, extensible and hackable package.

horovod - Distributed training framework for TensorFlow.

  •    C++

Horovod is a distributed training framework for TensorFlow. The goal of Horovod is to make distributed Deep Learning fast and easy to use.Internally at Uber we found that it's much easier for people to understand an MPI model that requires minimal changes to source code than to understand how to set up regular Distributed TensorFlow.

cordova-plugin-tensorflow - On-device image recognition via TensorFlow/Inception

  •    Objective-C++

The plugin provides a TensorFlow class that can be used to initialize graphs and run the inference algorithm. To use a custom model, follow the steps to retrain the model and optimize it for mobile use. Put the .pb and .txt files in a HTTP-accessible zip file, which will be downloaded via the FileTransfer plugin. If you use the generic Inception model it will be downloaded from the TensorFlow website on first use.

aloha - A scala-based feature generation and modeling framework

  •    Scala

So, Aloha models are are not written in terms of Instances, Tensors, or DataModels. Instead, models are written generically, and different semantics implementations are provided to give meaning to the features extracted from the arbitrary input types on which the models operate. While these differences may not sound extremely useful, together they produce a number of advantages. The most notable is probably the way input features make their way to the models. Typically, when interacting with APIs, data is translated into a format that can be understood by the objects being called. By tying a model interface to an input type specified inside the library, we require the caller to convert the data to the input type before the model can use the data to make a prediction. There are some ways to ease the woes that are involved in the ETL process, but as we've seen many times, transforming data can be slow, error-prone, and ultimately, unnecessary altogether. It's almost always the case that data is in an alternate format than the one required for learning or prediction. Because data, in its natural form, typically has a graph-like structure and many machine learning algorithms operate on vector spaces, we often have to perform such a transformation. The question is who should do the data transformation.

artos - Adaptive Real-Time Object Detection System with HOG and CNN Features

  •    C++

ARTOS is the Adaptive Real-Time Object Detection System, created at the University of Jena (Germany). It can be used to quickly learn models for visual object detection without having to collect a set of samples manually. To make this possible, it uses ImageNet, a large image database with more than 20,000 categories. It provides an average of 300-500 images with bounding box annotations for more than 3,000 of those categories and, thus, is suitable for object detection. The purpose of ARTOS is not limited to using those images in combination with clustering and a technique called Whitened Histograms of Orientations (WHO, Hariharan et al.) to quickly learn new models, but also includes adapting those models to other domains using in-situ images and applying them to detect objects in images and video streams.

libmaxdiv - Implementation of the Maximally Divergent Intervals algorithm for Anomaly Detection in multivariate spatio-temporal time-series

  •    C++

Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection. Björn Barz, Erik Rodner, Yanira Guanche Garcia, Joachim Denzler. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. An efficient C++ implementation called libmaxdiv is provided in maxdiv/libmaxdiv and may be used stand-alone. If it has been built in maxdiv/libmaxdiv/bin, it will be used automatically by the GUI and the maxdiv function in the maxdiv.maxdiv Python package. See maxdiv/libmaxdiv/README.md for build instructions. Otherwise, the pure Python implementation of the MDI algorithm will be used, which is not recommended, since it is extremely slow and lacks some features such as support for spatial data.

tensorflow-cdcgan - A short Conditional DCGAN tensorflow implementation.

  •    Python

This is a short implementation of a Conditional DCGAN, however if you need a cDCGAN for real-world use cases, please consider using a more serious implementation. Here can be seen a cDCGAN trained on CIFAR-10 using the same networks architectures I used for MNIST, obviously it shows that we need to be careful when designing the architecture. It works better using more filters.

tensorflow-retrain-sample - A sample for tensorflow retraining

  •    Python

This is an example on how to use pre-existing TensorFlow models to retrain on your data. In this example, we use Inception which is an image classifier. You can train it on the existing images of different noodles type and then use images in the test directory to get the predictions.

semantic-vision - Official repository for the semantic vision research initiative using SynerGAN.


We propose a novel architecture for recognizing, generating and reasoning about patterns in perceptual data. The first core component of the architecture is SynerGAN, an extension of the InfoGAN methodology to incorporate symbolic probabilistic logic and symbolic pattern mining, alongside subsymbolic neural net learning. In SynerGAN, each of the players in the game underlying InfoGAN, includes both symbolic and subsymbolic learning components, collaborating together to play their part in the game. The symbolic components play the role of the structured latent variables in InfoGAN, and are carrying out probabilistic reasoning and pattern mining, in a way that interacts appropriately with the subsymbolic (neural) components.

linearReg.js - Linear regression with Gradient descent package for NPM.

  •    Javascript

A javascript implementation of linear regression. Loosely based on Prof. Ng's MOOC on Machine Learning.

cshl-singlecell-2017 - Single Cell Analysis course at Cold Spring Harbor Laboratory 2017

  •    Jupyter

This is one of many single cell courses/tutorials. An excellent list of all single cell package, courses, tutorials, speakers for conferences, can be found here. We'll use some additional dependencies outside of the scientific python ecosystem.

MiniCat - Custom Text Classifier

  •    Python

MiniCat is short for Mini Text Categorizer. It is recommended to use a Virtual Environment, but not required. Installing the above dependencies in a new virtual environment allows you to run the sample without changing global python packages on your system.

deeplearning4j-docs - Documentation for Deeplearning4j - Deep Learning for the JVM, Java & Scala

  •    HTML

The documentation for Deeplearning4j and all of its libraries (DL4J, ND4J, Arbiter, DataVec, etc.) live in this repository. Warning: DO NOT edit the user guide directly in this repository. Commits will be reverted. Please make changes to the main repository here, run the autogeneration process, and open a pull request.