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For example, TensorFlow Recommenders and TensorFlow Graphics are libraries for their respective functionalities in recommendation systems and graphics, TensorFlow Federated provides a framework for decentralized data, and TensorFlow Cloud allows users to directly interact with Google Cloud to integrate their local code to Google Cloud. [68]
Keras is an open-source library that provides a Python interface for artificial neural networks.Keras was first independent software, then integrated into the TensorFlow library, and later supporting more.
Python: Python: Only on Linux No Yes No Yes Yes Keras: François Chollet 2015 MIT license: Yes Linux, macOS, Windows: Python: Python, R: Only if using Theano as backend Can use Theano, Tensorflow or PlaidML as backends Yes No Yes Yes [20] Yes Yes No [21] Yes [22] Yes MATLAB + Deep Learning Toolbox (formally Neural Network Toolbox) MathWorks ...
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
CuPy has been initially developed as a backend of Chainer deep learning framework, and later established as an independent project in 2017. [ 6 ] CuPy is a part of the NumPy ecosystem array libraries [ 7 ] and is widely adopted to utilize GPU with Python, [ 8 ] especially in high-performance computing environments such as Summit , [ 9 ...
MXNet: an open-source deep learning framework used to train and deploy deep neural networks. PyTorch: Tensors and Dynamic neural networks in Python with GPU acceleration. TensorFlow: Apache 2.0-licensed Theano-like library with support for CPU, GPU and Google's proprietary TPU, [116] mobile
As of 2018, SqueezeNet ships "natively" as part of the source code of a number of deep learning frameworks such as PyTorch, Apache MXNet, and Apple CoreML. [ 10 ] [ 11 ] [ 12 ] In addition, third party developers have created implementations of SqueezeNet that are compatible with frameworks such as TensorFlow . [ 13 ]
The library is designed to reduce computing power and memory use and to train large distributed models with better parallelism on existing computer hardware. [2] [3] DeepSpeed is optimized for low latency, high throughput training.