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We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. We invite you to sign up here to not miss these free books. 

Currently, the following content is available:

Deep Learning and Computer Vision with CNNs 

By Dan Howarth and Ajit Jaokar, October 2019. 42 pages. Part 1 will introduce the core concepts of Deep Learning. We will also start coding straightaway with Tensorflow 2.0. In part 2, we use another dataset - the mnist dataset - to build on our knowledge. In particular, we will:

  • Introduce Computer Vision
  • Introduce convolutional layers into our models
  • Introduce the concept of regularisation
  • Introduce the validation set in training our model
  • Introduce how to save and reuse our model

The table of content is available here. The book can be accessed here (member sonly.)

Online Encyclopedia of Statistical Science

This online book is intended for beginners, college students and professionals confronted with statistical analyses. It is also a refresher for professional statisticians.  The book covers over 600 concepts, chosen out of more than 1,500 for their popularity. Entries are listed in alphabetical order, and broken down into 18 parts. In addition to numerous illustrations, we have added 100 topics not covered in our online series Statistical Concepts Explained in Simple English. We also included a number of visualizations from our series Statistical Concepts Explained in One Picture.

The table of content is available here. The book can be accessed here (members only.)

Book: Azure Machine Learning in a Weekend

This book by Ajit Jaokar and Ayse Mutlu is the second book in the ‘in a weekend’ series – after Classification and Regression in a weekend. The idea of the ‘in a weekend’ series of books is to study one complex section of code in a weekend to master the concept. Cloud computing changes the development paradigm. Specifically, it combines development and deployment (the DevOps approach). In complex environments, the developer has to know more than the coding. Rather, she has to be familiar with both the data engineering and the DevOps. This book helps you to get started with coding a complex AI application for the Cloud(Azure).

The table of content is available hereThe book can be accessed here (members only.)

Book: Applied Stochastic Processes

Full title: Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems. Published June 2, 2018. Author: Vincent Granville, PhD. (104 pages, 16 chapters.)

This book is intended to professionals in data science, computer science, operations research, statistics, machine learning, big data, and mathematics. In 100 pages, it covers many new topics, offering a fresh perspective on the subject. It is accessible to practitioners with a two-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications (Blockchain, quantum algorithms, HPC, random number generation, cryptography, Fintech, web crawling, statistical testing) with numerous illustrations, is aimed at practitioners, researchers and executives in various quantitative fields.

New ideas, advanced topics, and state-of-the-art research are discussed in simple English, without using jargon or arcane theory. It unifies topics that are usually part of different fields (data science, operations research, dynamical systems, computer science, number theory, probability) broadening the knowledge and interest of the reader in ways that are not found in any other book. This short book contains a large amount of condensed material that would typically be covered in 500 pages in traditional publications. Thanks to cross-references and redundancy, the chapters can be read independently, in random order.

The table of content is available here. The book (PDF) can be accessed here (members only.) 

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Comment by Chinny Ikwuanusi on July 17, 2020 at 1:07am


Comment by Gargi Chiplunkar on July 4, 2020 at 6:03pm

Thanks a million.

Comment by Hassane AZZI on October 20, 2019 at 10:46am

Thanks !

Comment by Christine Nicole Bello on October 17, 2019 at 10:49am

This is amazing. So many wonderful resources! Thank you to all the contributors.

Comment by Yara Armel Désiré on October 15, 2019 at 1:12pm

Thank you for this content

Comment by Fiona Weston on July 30, 2019 at 10:11am

So much great stuff here. Big Thank you

Comment by David Scott on July 10, 2019 at 4:19pm

Wow! This is a lot of content. I look forward to using this for a refresher as well as learning something new.

Comment by Zarak Jamal Khan on June 26, 2019 at 6:40pm

That's Amazing, this platform is a big help for budding data scientist like me. Looking forward to 'Experimental Math' and 'Orignal math, stats and probability' books. 

Can someone guide me, I'm looking for an online course in applied statistics? Vincent if you can guide me through. 

Comment by Hassane AZZI on May 16, 2019 at 12:58am

Good !!

Comment by Vincent Granville on December 5, 2018 at 8:19pm

Hi Christoph, A better resolution picture is available here. The original can be found in this article. Best, Vincent


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