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An open-source Python library for image augmentation in machine learning experiments.
This GitHub repository contains implementations and illustrative code to accompany DeepMind's AI research publications.
A comprehensive list of Generative Adversarial Networks (GANs) for developers working with AI tools.
Distributed training framework for deep learning models using popular ML libraries like TensorFlow, Keras, PyTorch, and MXNet.
Python implementation of the reinforcement learning concepts from the classic textbook.
An AI-powered business intelligence tool that generates SQL, charts, and insights from natural language queries.
A conversational speech generation model for developers building AI-powered applications.
Burn is a high-performance tensor library and deep learning framework for AI and scientific computing in Rust.
A large-scale, open-source video generation model for developers building AI-powered media applications.
StyleGAN is an official TensorFlow implementation of a generative adversarial network (GAN) for creating photorealistic images.
A fast, lightweight deep learning framework used in Alibaba's business-critical use cases, supporting LLM and 3D avatar apps.
RWKV is an RNN-based language model with high performance, fast training, and flexible transformer-like architecture.
A C++ toolkit for building real-world machine learning and computer vision applications.
A simple, state-of-the-art NLP framework for tasks like named entity recognition and semantic role labeling.
An open-source AutoML toolkit for automating the machine learning lifecycle, including feature engineering, neural architecture search, and hyperparameter tuning.
Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
Face recognition using the FaceNet deep learning model, built with TensorFlow.
Open-source code for AlphaFold 2, a powerful AI system for predicting protein structures.
A comprehensive repository covering papers, codes, datasets, tutorials, and applications for transfer learning, domain adaptation, and more.
A free MLOps course that covers machine learning model deployment and monitoring
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