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? Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Carbon Language's main repository: documents, design, implementation, and related tools. (NOTE: Carbon Language is experimental; see README)

Visualizer for neural network, deep learning and machine learning models

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

ncnn is a high-performance neural network inference framework optimized for the mobile platform

? A ranked list of awesome machine learning Python libraries. Updated weekly.

Open standard for machine learning interoperability