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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 100daysofcodecudf - cuDF - GPU DataFrame Library
NOTE: For the latest stable README.md ensure you are on the main branch. Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
anaconda gpu arrow machine-learning-algorithms h2o cuda pandas python-api mapd gpu-dataframe rapids cudfcuml - cuML - RAPIDS Machine Learning Library
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.
machine-learning gpu machine-learning-algorithms cuda nvidiastellargraph - StellarGraph - Machine Learning on Graphs
StellarGraph is a Python library for machine learning on graphs and networks. StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy. It is thus user-friendly, modular and extensible. It interoperates smoothly with code that builds on these, such as the standard Keras layers and scikit-learn, so it is easy to augment the core graph machine learning algorithms provided by StellarGraph. It is thus also easy to install with pip or Anaconda.
machine-learning graphs machine-learning-algorithms networkx graph-data graph-analysis graph-machine-learning link-prediction graph-convolutional-networks gcn saliency-map interpretability geometric-deep-learning graph-neural-networks heterogeneous-networks stellargraph-libraryigel - a delightful machine learning tool that allows you to train, test, and use models without writing code
The goal of the project is to provide machine learning for everyone, both technical and non-technical users. I needed a tool sometimes, which I can use to fast create a machine learning prototype. Whether to build some proof of concept or create a fast draft model to prove a point. I find myself often stuck at writing boilerplate code and/or thinking too much of how to start this.
data-science machine-learning automation neural-network scikit-learn sklearn machine-learning-algorithms artificial-intelligence neural-networks data-analysis machine-learning-library machinelearning preprocessing automl multilayer-perceptron-network scikitlearn-machine-learning multilayer-perceptron automl-api automl-algorithms automl-experimentstutorial - Deeplearning Algorithms Tutorial
Deeplearning Algorithms Tutorial
tutorial neural-network machine-learning-algorithms deeplearning algorithms-tutorialHyperparameter-Optimization-of-Machine-Learning-Algorithms - Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
This code provides a hyper-parameter optimization implementation for machine learning algorithms, as described in the paper: L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, 2020, doi: https://doi.org/10.1016/j.neucom.2020.07.061. To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model's performance. In this paper, optimizing the hyper-parameters of common machine learning models is studied. We introduce several state-of-the-art optimization techniques and discuss how to apply them to machine learning algorithms. Many available libraries and frameworks developed for hyper-parameter optimization problems are provided, and some open challenges of hyper-parameter optimization research are also discussed in this paper. Moreover, experiments are conducted on benchmark datasets to compare the performance of different optimization methods and provide practical examples of hyper-parameter optimization.
machine-learning deep-learning random-forest optimization svm genetic-algorithm machine-learning-algorithms hyperparameter-optimization artificial-neural-networks grid-search tuning-parameters knn bayesian-optimization hyperparameter-tuning random-search particle-swarm-optimization hpo python-examples python-samples hyperbandJSAT - 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 tflite
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