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Jupyter Notebooks can execute cells of Python code, retaining the context between the execution of cells, which usually facilitates interactive data exploration. [5] Elixir is a high-level functional programming language based on the Erlang VM. Its machine-learning ecosystem includes Nx for computing on CPUs and GPUs, Bumblebee and Axon for ...
AIML, an XML dialect for creating natural language software agents. [48] Apache Lucene, a high-performance, full-featured text search engine library written entirely in Java. [49] Apache OpenNLP, a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence ...
Free AIML sets [5] in several languages have been developed and made available by the user community. There are AIML interpreters available in Java, Ruby, Python, C++, C#, Pascal, and other languages (see below [dead link ]). A semi-formal specification [6] and a W3C XML Schema for AIML [7] are available.
Orange is an open-source software package released under GPL and hosted on GitHub.Versions up to 3.0 include core components in C++ with wrappers in Python.From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.
OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms.
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1]
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 ...
Google JAX is a machine learning framework for transforming numerical functions. [1] [2] [3] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and TensorFlow's XLA (Accelerated Linear Algebra).