#python #book #machine_learning #machine_learning_algorithms

https://github.com/aburkov/theMLbook

#python #computer_vision #computervision #deep_learning #gimp #gimp_2_10 #gimp_plugin #image_manipulation #machine_learning #machine_learning_algorithms #photo_editing #photography #python27 #pytorch

https://github.com/kritiksoman/GIMP-ML

#other #artificial_intelligence #data_mining #data_science #deep_learning #game_theory #hardware #learning_theory #literature #machine_learning #machine_learning_algorithms #neural_network #paper #pattern_recognition #reinforcement_learning #silicon #statistical_learning #statistics

https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI

#jupyter_notebook #python #data_science #machine_learning #scikit_learn #machine_learning_algorithms #ml #machinelearning #machinelearning_python #scikit_learn_python

https://github.com/microsoft/ML-For-Beginners

#kotlin #machine_learning #deep_learning #machine_learning_algorithms #artificial_intelligence #deeplearning #mlops #mlops_environment

https://github.com/MLReef/mlreef

#jupyter_notebook #ai #deep_learning #interview #interview_practice #interview_preparation #interviews #machine_learning #machine_learning_algorithms #scalable_applications #system_design

https://github.com/alirezadir/machine-learning-interview-enlightener

#python #batch_processing #kafka #machine_learning_algorithms #pathway #real_time #streaming

https://github.com/pathwaycom/pathway

#python #code #machine_learning_algorithms #statistical_learning_method

This resource helps you learn machine learning and AI by providing translated papers, detailed code explanations, and practical implementations. You can read hundreds of papers daily with translations of titles, summaries, and even full texts for some AI papers. The code is well-commented, making it easy to follow along with formulas and examples. There are also blogs and upcoming books to help you understand the concepts better. Additionally, there are plans for offline training sessions in major cities, which can help you quickly get started with ML, MLP, and CV. This makes learning machine learning more accessible and comprehensive.

https://github.com/Dod-o/Statistical-Learning-Method_Code

#cplusplus #cuda #gpu #machine_learning #machine_learning_algorithms #nvidia

cuML - RAPIDS Machine Learning Library

https://github.com/rapidsai/cuml

#html #data_science #education #machine_learning #machine_learning_algorithms #machinelearning #machinelearning_python #microsoft_for_beginners #ml #python #r #scikit_learn #scikit_learn_python

Microsoft’s "Machine Learning for Beginners" is a free, 12-week course with 26 lessons designed to teach classic machine learning using Python and Scikit-learn. It includes quizzes, projects, and assignments to help you learn by doing, with lessons themed around global cultures to keep it engaging. You can access solutions, videos, and even R language versions. The course is beginner-friendly, flexible, and helps build practical skills step-by-step, making it easier to understand and apply machine learning concepts in real-world scenarios. This structured approach boosts your learning retention and prepares you for further study or career growth in ML[1][5].

https://github.com/microsoft/ML-For-Beginners