#javascript #ai #caffe #caffe2 #coreml #darknet #deep_learning #deeplearning #keras #machine_learning #machinelearning #ml #mxnet #neural_network #onnx #paddle #pytorch #tensorflow #tensorflow_lite #torch #visualizer
https://github.com/lutzroeder/netron
https://github.com/lutzroeder/netron
GitHub
GitHub - lutzroeder/netron: Visualizer for neural network, deep learning and machine learning models
Visualizer for neural network, deep learning and machine learning models - lutzroeder/netron
#other #visualization #data_science #data #vscode #datascience #vscode_extension #machinelearning
https://github.com/dynamicwebpaige/thinking-in-data
https://github.com/dynamicwebpaige/thinking-in-data
GitHub
GitHub - dynamicwebpaige/thinking-in-data: A VS Code extension pack to help users visualize, understand, and interact with data.
A VS Code extension pack to help users visualize, understand, and interact with data. - dynamicwebpaige/thinking-in-data
#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
https://github.com/microsoft/ML-For-Beginners
GitHub
GitHub - microsoft/ML-For-Beginners: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all - microsoft/ML-For-Beginners
#other #2021 #ai #artificial_intelligence #artificialintelligence #computer_science #computer_vision #deep_learning #innovation #machine_learning #machinelearning #paper #papers #python #research #research_paper #sota #sota_technique #state_of_art #state_of_the_art #technology
https://github.com/louisfb01/best_AI_papers_2021
https://github.com/louisfb01/best_AI_papers_2021
GitHub
GitHub - louisfb01/best_AI_papers_2021: A curated list of the latest breakthroughs in AI (in 2021) by release date with a clear…
A curated list of the latest breakthroughs in AI (in 2021) by release date with a clear video explanation, link to a more in-depth article, and code. - louisfb01/best_AI_papers_2021
#python #data_analysis #data_drift #data_science #deep_learning #jupyter_notebook #machine_learning #machinelearning #ml #mlops #model_monitoring #monitoring #performance_monitoring #visualization
https://github.com/NannyML/nannyml
https://github.com/NannyML/nannyml
GitHub
GitHub - NannyML/nannyml: nannyml: post-deployment data science in python
nannyml: post-deployment data science in python. Contribute to NannyML/nannyml development by creating an account on GitHub.
#other #2022 #ai #artificial_intelligence #computer_science #computer_vision #deep_learning #innovation #machine_learning #machinelearning #neural_network #paper #papers #python #sota #state_of_art #state_of_the_art #technology
https://github.com/louisfb01/best_AI_papers_2022
https://github.com/louisfb01/best_AI_papers_2022
GitHub
GitHub - louisfb01/best_AI_papers_2022: A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear…
A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code. - louisfb01/best_AI_papers_2022
#python #chatgpt #clip #deep_learning #gpt #hacktoberfest #hnsw #information_retrieval #knn #large_language_models #machine_learning #machinelearning #multi_modal #natural_language_processing #search_engine #semantic_search #tensor_search #transformers #vector_search #vision_language #visual_search
https://github.com/marqo-ai/marqo
https://github.com/marqo-ai/marqo
GitHub
GitHub - marqo-ai/marqo: Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai - marqo-ai/marqo
#other #awesome #awesome_list #conformal_prediction #datascience #deeplearning #machine_learning #machinelearning #probability #probability_distribution #probability_distributions #python #r #uncertainty #uncertainty_estimation #uncertainty_quantification
https://github.com/valeman/awesome-conformal-prediction
https://github.com/valeman/awesome-conformal-prediction
GitHub
GitHub - valeman/awesome-conformal-prediction: A professionally curated list of awesome Conformal Prediction videos, tutorials…
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries. - valeman/awesome-conformal-prediction
#jupyter_notebook #computer_vision #gpt #huggingface_transformers #llm #machinelearning #nlp_machine_learning #rag
https://github.com/katanaml/sparrow
https://github.com/katanaml/sparrow
GitHub
GitHub - katanaml/sparrow: Structured data extraction and instruction calling with ML, LLM and Vision LLM
Structured data extraction and instruction calling with ML, LLM and Vision LLM - katanaml/sparrow
#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
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
GitHub
GitHub - microsoft/ML-For-Beginners: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all - microsoft/ML-For-Beginners