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  1. 4 days ago · To install Anaconda, please visit the Anaconda distribution webpage. If more assistance is needed for the installation: See the Anaconda documentation for Windows. See the Anaconda documentation for Mac.

  2. 22 hours ago · At Anaconda, we’ve developed a rigorous new approach to AI development called Evaluations Driven Development (EDD). By continuously testing AI models using real-world cases and user feedback, EDD enables us to create AI assistants that are reliable, relevant, and truly impactful for users.

  3. 4 days ago · Table of Content. Download Python. Install Python on Windows 10. Install Python on MacOS. Install Python on Linux. Download Python. Since Windows doesn’t come with Python preinstalled, it needs to be installed explicitly. Here we will define step by step tutorial on How to install Python on Windows. Follow the steps below :

  4. 22 hours ago · Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow. An autocompletion tool for Python that can be used for text editors. An optimizing compiler for evaluating mathematical expressions.

  5. 2 days ago · A library for efficient similarity search and clustering of dense vectors. A library for efficient similarity search and clustering of dense vectors. PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. Metapackage to select the PyTorch variant. Use conda's pinning mechanism in your environment to control which variant ...

  6. 1 day ago · conda-forge / packages. conda-forge. /. packages. libastra is a C++ library of high-performance GPU primitives for 2D and 3D tomography. Always know what to expect from your data. A software project management and comprehension tool. This add-on to pymatgen provides tools for analyzing diffusion in materials.

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  8. pypi.org › project › transformerstransformers · PyPI

    5 days ago · 🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.

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