EBOP Model Automatic input Value Estimation Neural network

A unified framework for machine learning with time series

machine learning theory and exercises

ONNX Runtime binding for Lua

Descriptive And Inferential Data Analysis Using Python Projects

Parallel computing with task scheduling

Empowering Data Driven insights through hands-on projects, SQL challenges and practical tools.

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

This are the Machine Learning notes by leading AI website named Deeplearning.AI. This notes will help you to be a machine learner from beginner to advanced level. Welcome Everyone!!

This is a simple web based application to predict diabetes.

This code demonstrates the use of machine learning to model the multimodal nature of a single cell. Using machine learning to predict RNA from DNA, that is, using chromatin accessibility data to predict the RNA gene expression and to predict surface protein from RNA, that is, using RNA sequence data to predict surface protein levels in a cell

Practices and Assignments from the Advanced Machine Learning Class

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

The goal of this project is to develop a machine learning model that can classify movie reviews as positive or negative based on the sentiment expressed in the text.

Fit interpretable models. Explain blackbox machine learning.

Detailed Description of machine learning algorithms at one place.

🌻Flower Recognition using Multi-class classification 🌻

Fast and Accurate ML in 3 Lines of Code

This repository contains Dockerfiles, scripts, yaml files, Helm charts, etc. used to scale out AI containers with versions of TensorFlow and PyTorch that have been optimized for Intel platforms. Scaling is done with python, Docker, kubernetes, kubeflow, cnvrg.io, Helm, and other container orchestration frameworks for use in the cloud and on-premise

[Quick Start with TensorFlow]-In this course, you will learn the basic concepts and syntax of TensorFlow 2, and how to use TensorFlow 2 to implement deep learning algorithms.