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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.
machine-learning deep-learning neural-networks machine-learning-algorithmspython-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 machine-learning-algorithms logistic-regression data-science data-mining scikit-learn neural-networkRecommenders - Recommender Systems
Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting train/test data. Implementations of several state-of-the-art algorithms are provided for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on Spark, or on Azure Databricks.
recommender-system machine-learning-algorithms ranking recommendation-algorithmsmachine_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.
machine-learning logistic-regression ipynb machine-learning-algorithms linear-regression perceptron python-implementations kmeans algorithm python3 neural-network k-nearest-neighbours k-nearest-neighbor k-nn neural-networksawesome-quantum-machine-learning - Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web
A curated list of awesome quantum machine learning algorithms,study materials,libraries and software (by language).
quantum quantum-computing quantum-programming-language machine-learning artificial-intelligence artificial-neural-networks tensorflow awesome-list awesome machine-learning-algorithms knn-classification fcm kmeans hmm-model qubits ant-colony-optimization ai quantum-ai qmlsod - 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.
computer-vision library deep-learning image-processing object-detection cpu real-time convolutional-neural-networks recurrent-neural-networks face-detection facial-landmarks machine-learning-algorithms image-recognition image-analysis vision-framework embedded detection iot-device iotmachine-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.
machine-learning deep-learning artificial-intelligence software-engineer machine-learning-algorithms100-Days-Of-ML-Code - 100 Days of ML Coding
Check out the code from here. Check out the code from here.
machine-learning machine-learning-algorithms infographics tutorial siraj-raval-challenge siraj-raval implementation 100-days-of-code-log 100daysofcodeJSAT - 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.
machine-learning machine-learning-library machine-learning-algorithms svm tsne jsatofxDarknet - 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.
machine-learning-algorithms neural-networkspygdf - 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.
gpu gpu-data-frame h2o python-api machine-learning-algorithms mapd anaconda analyticsAndroid-TensorFlow-Lite-Example - Android TensorFlow Lite Machine Learning Example
Android TensorFlow Lite Machine Learning Example
tensorflow tensorflow-tutorials machine-learning tensorflow-lite tensorflow-examples deep-learning deep-neural-networks android-example machine-learning-algorithms tflitePlay-with-Machine-Learning-Algorithms - Code of my MOOC Course <Play with Machine Learning Algorithms>
Code of my MOOC Course <Play with Machine Learning Algorithms>. Updated contents and practices are also included. 我在慕课网上的课程《Python3 入门机器学习》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。
machine-learning-algorithms machine-learning mooc imooc jupyter-notebooksmachineJS - Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml
I just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.
machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competitionmachine-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.
machine-learning machine-learning-algorithms nlp neural-networks deep-learning reinforcement-learningAgile_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.
data-syndrome data data-science analytics apache-spark apache-kafka kafka spark predictive-analytics machine-learning machine-learning-algorithms airflow python-3 python3 amazon-ec2 agile-data agile-data-science vagrant amazon-web-servicesdifferentiable-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.
ai ml differentiable-plasticity machine-learning machine-learning-algorithmsMath-of-Machine-Learning-Course-by-Siraj - Implements common data science methods and machine learning algorithms from scratch in python
This repository was initially created to submit machine learning assignments for Siraj Raval's online machine learning course. The purpose of the course was to learn how to implement the most common machine learning algorithms from scratch (without using machine learning libraries such as tensorflow, PyTorch, scikit-learn, etc). Although that course has ended now, I am continuing to learn data science and machine learning from other sources such as Coursera, online blogs, and attending machine learning lectures at University of Toronto. Sticking to the theme of implementing machine learning algortihms from scratch, I will continue to post detailed notebooks in python here as I learn more.
machine-learning statistical-concepts siraj-raval machine-learning-algorithms machine-learning-from-scratch
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