Ready-to-run Docker images containing Jupyter applications

Repository of teaching materials, code, and data for my data analysis and machine learning projects.

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

Kandinsky 2 — multilingual text2image latent diffusion model

Jupyter notebooks in the terminal

Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

strip output from Jupyter and IPython notebooks

Instructional notebooks on music information retrieval.

CatBoost tutorials repository

A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

Non-Intrusive Load Monitoring Toolkit (nilmtk)

This repository contains all the data analytics projects that I've worked on in python.

Scipy Cookbook

A py.test plugin to validate Jupyter notebooks

A tiny 1000 line LLVM-based numeric specializer for scientific Python code.

IPython Notebooks to learn Python

Jupyter for Visual Studio Code

Jupyter Notebooks from the old UnsupervisedLearning.com (RIP) machine learning and statistics blog

Pytest in IPython notebooks.

Real-time GCC-NMF Blind Speech Separation and Enhancement

Code and Examples for Relevant Search

Object-oriented programming (OOP) is a method of structuring a program by bundling related properties and behaviors into individual objects. In this tutorial, you’ll learn the basics of object-oriented programming in Python.

You'll learn about Iterators, Generators, Closure, Decorators, Property, and RegEx in detail with examples.

Python script (and IPython notebook) to perform RFM analysis from customer purchase history data

Jupyter/IPython notebooks about evolutionary computation.

WebRTC for Jupyter notebook/lab

🖼 Stitching images into 360 panoramas

This repository explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.

Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python. Besides its obvious scientific uses, Numpy can also be used as an efficient multi-dimensional container of generic data.

Python too supports file handling and allows users to handle files i.e., to read and write files, along with many other file handling options, to operate on files. The concept of file handling has stretched over various other languages, but the implementation is either complicated or lengthy, but like other concepts of Python, this concept here is also easy and short. Python treats files differently as text or binary and this is important.