NGC-Learn: Neurobiological Learning and Biomimetic Systems Simulation in Python
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Updated
Jun 2, 2024 - Python
NGC-Learn: Neurobiological Learning and Biomimetic Systems Simulation in Python
Machine learning algorithms for many-body quantum systems
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Implementación en JAX de una red neuronal convolucional (CNN) para clasificar la base de datos Fashion MNIST.
Deep Learning for humans
Accelerate your training with this open-source library. Optimize performance with streamlined training and serving options with JAX. 🚀
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
A retargetable MLIR-based machine learning compiler and runtime toolkit.
A library that includes Keras3 layers, blocks and models with pretrained weights, providing support for transfer learning, feature extraction, and more.
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
Multiple dispatch over abstract array types in JAX.
skscope: Sparse-Constrained OPtimization via itErative-solvers
⚡️SwanLab: your ML experiment notebook. 你的AI实验笔记本,跟踪与可视化你的机器学习全流程
The Unified AI Framework
Factor graphs and nonlinear least squares for JAX
Additive manufacturing simulation with JAX.
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