Displaying 1 to 20 from 51 results

MLAlgorithms - Minimal and clean examples of machine learning algorithms implementations


A collection of minimal and clean implementations of machine learning algorithms. This project is targeting people who want to learn internals of ml algorithms or implement them from scratch. The code is much easier to follow than the optimized libraries and easier to play with. All algorithms are implemented in Python, using numpy, scipy and autograd.

python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource


This GitHub repository contains the code examples of the 1st Edition of Python Machine Learning book. If you are looking for the code examples of the 2nd Edition, please refer to this repository instead. What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.

machine_learning_basics - Plain python implementations of basic machine learning algorithms


This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.




sod - An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)


SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.

machine-learning-for-software-engineers - A complete daily plan for studying to become a machine learning engineer


Inspired by Google Interview University. This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer.


JSAT - Java Statistical Analysis Tool, a Java library for Machine Learning


JSAT is a library for quickly getting started with Machine Learning problems. It is developed in my free time, and made available for use under the GPL 3. Part of the library is for self education, as such - all code is self contained. JSAT has no external dependencies, and is pure Java. I also aim to make the library suitably fast for small to medium size problems. As such, much of the code supports parallel execution.If you want to use the bleeding edge, but don't want to bother building yourself, I recomend you look at jitpack.io. It can build a POM repo for you for any specific commit version. Click on "Commits" in the link and then click "get it" for the commit version you want.

ofxDarknet - darknet neural network addon for openFrameworks


ofxDarknet is a openFrameworks wrapper for darknet. In order to classify an image with more classes, this is the spot. This classifies an image according to the 1000-class ImageNet Challenge.

pygdf - GPU Data Frame


PyGDF implements the Python interface to access and manipulate the GPU Dataframe of GPU Open Analytics Initialive (GOAI). We aim to provide a simple interface that similar to the Pandas dataframe and hide the details of GPU programming.

machine-learning-articles - Monthly Series - Top 10 Machine Learning Articles


Click "Watch" to get an email notification once a month for Top 10 Machine Learning articles. Update will be made on major releases. Mybridge AI ranks articles by the number of shares, minutes read, and by its own machine learning algorithm.

Agile_Data_Code_2 - Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition


Like my work? I am Principal Consultant at Data Syndrome, a consultancy offering assistance and training with building full-stack analytics products, applications and systems. Find us on the web at datasyndrome.com. There is now a video course using code from chapter 8, Realtime Predictive Analytics with Kafka, PySpark, Spark MLlib and Spark Streaming. Check it out now at datasyndrome.com/video.

differentiable-plasticity - Implementations of the algorithms described in Differentiable plasticity: training plastic networks with gradient descent, a research paper from Uber AI Labs


This repo contains implementations of the algorithms described in Differentiable plasticity: training plastic networks with gradient descent, a research paper from Uber AI Labs. We strongly recommend studying the simple/simplest.py program first, as it is deliberately kept as simple as possible while showing full-fledged differentiable plasticity learning.

nlp - Selected Machine Learning algorithms for basic natural language processing in Golang


An implementation of selected machine learning algorithms for basic natural language processing in golang. The initial focus for this project is Latent Semantic Analysis to allow retrieval/searching, clustering and classification of text documents based upon semantic content.Built upon the gonum/gonum matrix library with some inspiration taken from Python's scikit-learn.

EdgeML - This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India


This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.Machine learning models for edge devices need to have a small footprint in terms of storage, prediction latency and energy. One example of a ubiquitous real-world application where such models are desirable is resource-scarce devices and sensors in the Internet of Things (IoT) setting. Making real-time predictions locally on IoT devices without connecting to the cloud requires models that fit in a few kilobytes.