Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Example notebooks demonstrating how to use Clara Train to build Medical Imaging Deep Learning models

cube studio开源云原生一站式机器学习/深度学习/大模型AI平台，支持sso登录，大数据平台对接，notebook在线开发，拖拉拽任务流pipeline编排，多机多卡分布式训练，超参搜索，推理服务VGPU，边缘计算，标注平台，自动化标注，大模型微调，vllm大模型推理，llmops，私有知识库，AI模型应用商店，支持模型一键开发/推理/微调，支持国产cpu/gpu/npu芯片，支持RDMA，支持pytorch/tf/mxnet/deepspeed/paddle/colossalai/horovod/spark/ray/volcano分布式，deepseek推理

Real-time inference for Stable Diffusion - 0.88s latency. Covers AITemplate, nvFuser, TensorRT, FlashAttention. Join our Discord communty: https://discord.com/invite/TgHXuSJEk6

Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage machine learning and generative AI workflows using Google Cloud Vertex AI.

Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.

This repo covers Kubeflow Environment with LABs: Kubeflow GUI, Jupyter Notebooks on pods, Kubeflow Pipelines, Experiments, KALE, KATIB (AutoML: Hyperparameter Tuning), KFServe (Model Serving), Training Operators (Distributed Training), Projects, etc.

This repository contains demo notebooks (sample code) for the AutoMLx (automated machine learning and explainability) package from Oracle Labs.

The website is at following link

Compilation of MSFT docs into a set of functioning pipeline and notebook examples.

Toward Automated Continual Learning (for fully auto-adaptive learning methods and systems). Here, is a list of materials useful to realize this project.

A compilation of Machine Learning Notebooks for beginners.

Coursera Practical Data Science on AWS Specialization Courses Notebooks.

In this project, we use a dataset external to Azure ML ecosystem to train and deploy models using AutoML and HyperDrive services.

In this project, we are using the Bank Marketing dataset to create a cloud-based machine learning production model and a pipeline on Azure Machine Learning. We will create a model with Auto Machine Learning, deploy it, and consume it

Contains a Jupyter Notebook that focuses on creating an AutoML trained model using Google Cloud Platform's Vertex AI to predict how long a customer will engage with a video ad for

This project demonstrates a comprehensive approach to solving a regression problem using various machine learning models. The notebook includes: Data Preprocessing, Exploratory Data Analysis (EDA), Model Training, Hyperparameter Tuning, Model Evaluation, Feature Importance

This notebook is designed to interactively guide the user through an end-to-end process for deploying an automated machine learning workflow utilizing h2o.ai's autoML function. The user is simply required to select a dataset and choose a variable they would like to predict before running the automation. The user can choose to run the automation …

This is the Capstone project (last of the three projects) required for fulfillment of the Nanodegree Machine Learning Engineer with Microsoft Azure from Udacity. In this project, we use a dataset external to Azure ML ecosystem. Azure Machine Learning Service and Jupyter Notebook is used to train models using both Hyperdrive and Auto ML and then …