Awesome Bazel
Section: Projects · Computation using data flow graphs for scalable machine learning.
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Appears in 23 awesome lists
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.
Section: Projects · Computation using data flow graphs for scalable machine learning.
Section: Machine Learning · Library from Google for machine learning using data flow graphs.
Section: C/C++ 源码学习
Section: AI & Machine Learning Platforms · Computation using data flow graphs for scalable machine learning.
Section: Artificial Intelligence · An open source software library for numerical computation using data flow graphs. [Apache]
Section: Tools · End-to-end open source platform for machine learning and deep learning.
Section: Deep Learning Packages
Section: Frameworks
Section: Librairies and Implementations · Most known deep learning framework, both high-level and low-level while staying flexible.
Section: C++ · (label: stat:contributions-welcome) Computation using data flow graphs for scalable machine learning
Section: Frameworks for Training · An Open Source Machine Learning Framework for Everyone.
Section: Python · Open source software library for numerical computation using data flow graphs.
Section: 1. Core Frameworks & Libraries · End-to-end platform with excellent production deployment, TPU support, and large-scale serving tools.
Section: Computation and Communication Optimisation · TensorFlow is a leading library designed for developing and deploying state-of-the-art machine learning applications.
Section: TensorFlow · Computation using data flow graphs for scalable machine learning by Google.
Section: Data Science · Fundamental algorithms for scientific computing in Python
Section: 库 · Simplified interface for Deep/Machine Learning (now part of TensorFlow)
Section: Past announcements: · 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.
Section: On-Device training and inference
Section: Other · An Open Source Machine Learning Framework for Everyone
Section: Deep Learning · The most popular Deep Learning framework created by Google.
Section: Machine Learning · The official Google-built powerful neural network library port for iOS.
Section: Fundamental libraries · | Python, C++ | - More low level Deep Learning framework
Scikit-learn is a powerful machine learning library that provides a wide variety of modules for data access, data preparation and statistical model building.
All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code).
Self-hosted ML experiment tracker designed to handle 10,000s of training runs with performant UI and SDK for programmatic access. Apache 2.0 licensed.
Unsupervised learning on graphs. PyTorch Geometric - Graph representation learning with PyTorch. DLG - Graph representation learning with TensorFlow.
Fast scalable Machine Learning platform for smarter applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc..
Community-friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility.
OSS collection of C++ code (compliant to C++11) designed to augment the C++ standard library.