Conda packges for ai (general)

Here is a list of Conda packages related to artificial intelligence, including a brief description and the installation command for each package:

Intel AI Analytics Toolkit Packages

intel-aikit-tensorflow\ Optimized TensorFlow for deep learning workflows using Intel® architecture.

conda create -n aikit-tf -c intel intel-aikit-tensorflow

intel-aikit-pytorch\ Optimized PyTorch for deep learning workflows tailored for Intel® architecture.

conda create -n aikit-pt -c intel intel-aikit-pytorch

intel-aikit-modin\ Accelerated data analytics and machine learning workflows using Modin, Scikit-learn, and XGBoost optimizations.

conda create -n aikit-modin -c intel intel-aikit-modin

General AI and Machine Learning Packages

tensorflow\ An open-source library for numerical computation that makes machine learning faster and easier.

conda install tensorflow

pytorch\ A flexible deep learning framework that provides tools for building complex neural networks.

conda install pytorch torchvision torchaudio -c pytorch

scikit-learn\ A machine learning library for Python that provides simple and efficient tools for data mining and analysis.

conda install scikit-learn

keras\ An API designed to enable fast experimentation with deep neural networks, built on top of TensorFlow.

conda install keras

xgboost\ An optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable.

conda install xgboost

Additional Tools

nltk\ The Natural Language Toolkit is a leading platform for building Python programs to work with human language data.

conda install nltk

gensim\ A library for topic modeling and document similarity analysis, particularly useful in natural language processing.

conda install gensim

These packages cover a range of functionalities essential for AI development, from deep learning frameworks to tools for natural language processing and data analysis.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.