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TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. [3] [4] It was developed by the Google Brain team for Google's internal use in research and production.
Pythonis a high-level, general-purpose programming languagethat 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.
Business Intelligence - Generative BI. Generative BI [ 67] refers to the application of generative AI techniques, like Large Language Models (LLMs), in business intelligence. This combination accelerates the development of advanced models, automates data analysis, and facilitates the generation of actionable insights.
Machine learningand data mining. These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning ), computer hardware, and, less ...
GitHub Copilot is a code completion tool developed by GitHub and OpenAI that assists users of Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments (IDEs) by autocompleting code. [ 1] Currently available by subscription to individual developers and to businesses, the generative artificial intelligence ...
Heuristic (computer science) In mathematical optimization and computer science, heuristic (from Greek εὑρίσκω "I find, discover") is a technique designed for problem solving more quickly when classic methods are too slow for finding an exact or approximate solution, or when classic methods fail to find any exact solution in a search space.
Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of their features support functional programming and aspect-oriented programming (including metaprogramming [ 70] and metaobjects ). [ 71] Many other paradigms are supported via extensions, including design by ...
Prompt engineering is enabled by in-context learning, defined as a model's ability to temporarily learn from prompts. The ability for in-context learning is an emergent ability [ 14] of large language models. In-context learning itself is an emergent property of model scale, meaning breaks [ 15] in downstream scaling laws occur such that its ...