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  • Downloads - Anaconda
    Download Anaconda Distribution Version | Release Date:Download For: High-Performance Distribution Easily install 1,000+ data science packages Package Management Manage packages
  • Installing Anaconda Distribution
    Miniconda is a free, miniature installation of Anaconda Distribution that includes only conda, Python, the packages they both depend on, and a small number of other useful packages Basic install instructions
  • Anaconda Distribution - Anaconda
    Anaconda® Distribution is a Python R data science distribution that contains: conda: a package and environment manager for your command line interface Anaconda Navigator: a desktop application built on conda, with options to launch other development applications from your managed environments
  • Advance AI with Open Source | Anaconda
    We believe open source is the cornerstone of the next era of AI innovation and that Python will do for AI what it did for data science Get a demo or start today and create an account
  • Learn More About Anaconda | Anaconda
    Conda is Anaconda’s package, environment, and dependency manager It is cross-platform (Windows, macOS, Linux [x86 AARCH64 PPC64LE s390x]), cross-language (supports Python, R, C C++, Rust, Go, and more), and ensures package compatibility and environment correctness
  • Anaconda vs. Python: What’s the Difference?
    In short, Anaconda and Python are both popular tools for data science, machine learning, and AI use cases Anaconda leverages Python at its core and has additional capabilities to provide the best experience for data science teams
  • Open Source - Anaconda
    OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library with C++, Java, Python, and MATLAB interfaces OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products
  • Getting started with Anaconda
    In the Get Started with Anaconda entry-level course, you’ll learn about packages, conda environments, Jupyter Notebooks, and more We’ll also guide you through initiating a Python program (in both a notebook and several popular IDEs) and show you what happens when a bug is identified in Python code


















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