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JAX

Appears in 6 awesome lists

| Python | - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Open github.comgoogle/jax

Found in these lists

AWESOME DATA SCIENCE

Section: General Machine Learning Packages

FreshScore 92

Awesome JAX

Section: Community

SlowScore 64

Awesome LLMOps

Section: Frameworks for Training · Autograd and XLA for high-performance machine learning research.

ActiveScore 75

Awesome Machine Learning

Section: Python · JAX is Autograd and XLA, brought together for high-performance machine learning research.

FreshScore 93

Awesome Python Data Science

Section: JAX · Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more.

ActiveScore 71

Awesome Systematic Trading

Section: Fundamental libraries · | Python | - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

FreshScore 89

TensorFlow

How to use the Hexagon Delegate to speed up model inference on mobile and edge devices. Also see blog post Accelerating TensorFlow Lite on Qualcomm Hexagon DSPs.

In 23 listsDetails

PyTorch

(label: good first issue) PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing.

In 16 listsDetails

Colossal-AI

(from Hpcaitech) - A Unified Deep Learning System for Large-Scale Parallel Training (1D, 2D, 2.5D, 3D and sequence parallelism, and ZeRO protocol).

In 14 listsDetails

XGBoost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow. [Apache2]

In 11 listsDetails

scikit-learn

Scikit-learn is a powerful machine learning library that provides a wide variety of modules for data access, data preparation and statistical model building.

In 10 listsDetails

CatBoost

General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, contains fast inference implementation and supports CPU and GPU (even multi-GPU) computation.

In 10 listsDetails

Caffe

is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

In 10 listsDetails

Keras

High-level, beginner-friendly API that now runs on multiple backends (TensorFlow, JAX, PyTorch). Perfect for rapid experimentation.

In 9 listsDetails