“Hands-On Machine Learning with Scikit-Learn and TensorFlow” Excerpt From: Aurélien Géron. “Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.” iBooks.

Breast Cancer Classification using CNN and transfer learning

机器学习实战

Discovering Interpretable GAN Controls [NeurIPS 2020]

A lightweight book theme built off of the pydata sphinx theme

自然语言处理NLP在中文文本上的一些应用，如文本分类、情感分析、命名实体识别等

Convolutional Gaussian processes based on GPflow.

Deliverable: This Jupyter notebook will help aspiring data scientists learn and practice the necessary python code needed for many data science projects.

Course materials for: Introduction to Data Science and Programming

Automagically remove trends from time-series data

100+ python programming exercise problem discussed ,explained and solved in different ways

A collection of code examples as well as presentations for training purposes

Designing Deep neural network architectures using topologies from the world of Complex Networks/network Science

Koç University deep learning framework.

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.

Book sample (AI Machine-learning Deep-learning)

This repository shows a sample example to build, manage and orchestrate Machine Learning workflows using Amazon Sagemaker and Apache Airflow.