Fundamentals of machine learning in python.
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Run LLM backends, APIs, frontends, and services with one command.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Framework for building applications with LLMs and Transformers (e.g. agents, semantic search, question-answering).
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.
Distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Framework for distributed hyperparameter optimization.
Making large AI models cheaper, faster and more accessible.
Library for hyperparameter optimization and black box optimization benchmarks.
General Machine Learning library using Numenta’s Cortical Learning Algorithm.
"building applications with LLMs through composability".
A framework for elegantly configuring complex applications.
A very simple wrapper for convenient hyperparameter optimization.
General Automated Machine Learning Framework.
Distributed Asynchronous Hyperparameter Optimization in Python.
Toolset for black-box hyperparameter optimisation.
An open-source visual programming environment for battle-testing prompts to LLMs.
A delightful machine learning tool that allows you to train/fit, test and use models without writing code.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Julia-language backend combined with the Jupyter interactive environment.
Ikemen Go is a GitHub repository or organization with source code, releases, documentation, or project resources.
Implementation of image to image (pix2pix) translation from the paper by isola et al.[DEEP LEARNING].