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TensorFlow can be used in a wide variety of programming languages, including Python, JavaScript, C++, and Java, [12] facilitating its use in a range of applications in many sectors. History [ edit ]
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 is a high-level, general-purpose programming language that is popular in artificial intelligence. [1] It has a simple, flexible and easily readable syntax. [2] Its popularity results in a vast ecosystem of libraries, including for deep learning, such as PyTorch, TensorFlow, Keras, Google JAX.
It is designed to follow the structure and workflow of NumPy as closely as possible and works with various existing frameworks such as TensorFlow and PyTorch. [5] [6] The primary functions of JAX are: [2] grad: automatic differentiation; jit: compilation; vmap: auto-vectorization; pmap: Single program, multiple data (SPMD) programming
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 ...
Adding from __future__ import division causes a module used in Python 2.7 to use Python 3.0 rules for division (see above). In Python terms, / is true division (or simply division), and // is floor division. / before version 3.0 is classic division. [122] Rounding towards negative infinity, though different from most languages, adds consistency.
PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and inference performance across major cloud platforms.
A probabilistic relational programming language (PRPL) is a PPL specially designed to describe and infer with probabilistic relational models (PRMs). A PRM is usually developed with a set of algorithms for reducing, inference about and discovery of concerned distributions, which are embedded into the corresponding PRPL.