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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.
Prompt engineering is the process of structuring an instruction that can be interpreted and understood by a generative artificial intelligence (AI) model. [1] [2]A prompt is natural language text describing the task that an AI should perform. [3]
In computer science, a generator is a routine that can be used to control the iteration behaviour of a loop.All generators are also iterators. [1] A generator is very similar to a function that returns an array, in that a generator has parameters, can be called, and generates a sequence of values.
GDScript, a scripting language very similar to Python, built-in to the Godot game engine. [238] Go is designed for the "speed of working in a dynamic language like Python" [239] and shares the same syntax for slicing arrays. Groovy was motivated by the desire to bring the Python design philosophy to Java. [240]
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.
GitHub Copilot was initially powered by the OpenAI Codex, [13] which is a modified, production version of the Generative Pre-trained Transformer 3 (GPT-3), a language model using deep-learning to produce human-like text. [14]
TensorFlow is a software library for machine learning and artificial intelligence. It can be used across a range of tasks, but is used mainly for training and inference of neural networks . [ 3 ] [ 4 ] It is one of the most popular deep learning frameworks, alongside others such as PyTorch and PaddlePaddle.
AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...