#python #adversarial_machine_learning #artificial_intelligence #attack #catboost #codait #decision_trees #deep_neural_networks #defense_methods #extraction #gradient_boosted_trees #ibm_research #ibm_research_ai #lightgbm #logistic_regression #poisoning #scikit_learn #support_vector_machine #trusted_ai #xgboost

https://github.com/IBM/adversarial-robustness-toolbox

#python #deep_learning #machine_learning #machine_learning_tutorials #pytorch #pytorch_ignite #scikit_learn #sklearn #streamlit #template #tensorflow #webapp #website

https://github.com/jrieke/traingenerator

#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

#python #awesome #awesome_list #best_of #data_science #data_visualization #data_visualizations #deep_learning #keras #machine_learning #nlp #python_library #pytorch #scikit_learn #tensorflow

https://github.com/ml-tooling/best-of-ml-python

#jupyter_notebook #automated_machine_learning #automl #classification #data_science #deep_learning #finetuning #hyperparam #hyperparameter_optimization #jupyter_notebook #machine_learning #natural_language_generation #natural_language_processing #python #random_forest #regression #scikit_learn #tabular_data #timeseries_forecasting #tuning

https://github.com/microsoft/FLAML

#python #data_mining #data_science #forecasting #machine_learning #scikit_learn #time_series #time_series_analysis #time_series_classification #time_series_regression

https://github.com/sktime/sktime

#python #ai #data #data_structures #database #long_term_memory #machine_learning #ml #mlops #mongodb #pytorch #scikit_learn #sklearn #torch #transformers #vector_search

https://github.com/SuperDuperDB/superduperdb

#python #autogluon #automated_machine_learning #automl #computer_vision #data_science #deep_learning #ensemble_learning #forecasting #gluon #hyperparameter_optimization #machine_learning #natural_language_processing #object_detection #python #pytorch #scikit_learn #structured_data #tabular_data #time_series #transfer_learning

AutoGluon makes machine learning easy and fast. With just a few lines of code, you can train and use high-accuracy models for images, text, time series, and tabular data. This means you can quickly build and deploy powerful machine learning models without needing to write a lot of code. It supports Python 3.8 to 3.11 and works on Linux, MacOS, and Windows, making it convenient for various users. This saves time and effort, allowing you to focus on other parts of your project.

https://github.com/autogluon/autogluon

#cplusplus #ai_framework #deep_learning #hardware_acceleration #machine_learning #neural_networks #onnx #pytorch #scikit_learn #tensorflow

ONNX Runtime is a tool that makes machine learning faster and cheaper. It works on many different devices and operating systems, like Windows, Linux, and Mac, and supports popular machine learning frameworks like PyTorch and TensorFlow. This means you can use it to speed up your machine learning models, making your applications run faster and more efficiently. It also helps in training models quickly, especially on powerful NVIDIA GPUs. This benefits you by providing faster customer experiences and lower costs for your machine learning projects.

https://github.com/microsoft/onnxruntime

#other #automl #chatgpt #data_analysis #data_science #data_visualization #data_visualizations #deep_learning #gpt #gpt_3 #jax #keras #machine_learning #ml #nlp #python #pytorch #scikit_learn #tensorflow #transformer

This is a comprehensive, regularly updated list of 920 top open-source Python machine learning libraries, organized into 34 categories like frameworks, data visualization, NLP, image processing, and more. Each project is ranked by quality using GitHub and package manager metrics, helping you find the best tools for your needs. Popular libraries like TensorFlow, PyTorch, scikit-learn, and Hugging Face transformers are included, along with specialized ones for time series, reinforcement learning, and model interpretability. This resource saves you time by guiding you to high-quality, actively maintained libraries for building, optimizing, and deploying machine learning models efficiently.

https://github.com/ml-tooling/best-of-ml-python

#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