Wednesday, October 31, 2018

New Book: Enterprise AI - An Applications Perspective

Now published. Enterprise AI: An applications perspective takes a use case driven approach to understand the deployment of AI in the Enterprise. Designed for strategists and developers, the book provides a practical and straightforward roadmap based on application use cases for AI in Enterprises. The authors (Ajit Jaokar and Cheuk Ting Ho) are data scientists and AI researchers who have deployed AI applications for Enterprise domains. The book is used as a reference for Ajit and Cheuk's new course on Implementing Enterprise AI. Download this eBook (PDF).

  • Machine Learning, Deep Learning and AI 
  • The Data Science Process 
  • Categories of Machine Learning algorithms 
  • How to learn rules from Data? 
  • An introduction to Deep Learning 
  • What problem does Deep Learning address? 
  • How Deep Learning Drives Artificial Intelligence 
Deep Learning and neural networks
  • Perceptrons – an artificial neuron 
  • MLP - How do Neural networks make a prediction? 
  • Spatial considerations - Convolutional Neural Networks 
  • Temporal considerations - RNN/LSTM 
  • The significance of Deep Learning 
  • Deep Learning provides better representation for understanding the world 
  • Deep Learning a Universal Function Approximator 
What functionality does AI enable for the Enterprise? 
  • Technical capabilities of AI
  • Functionality enabled by AI in the Enterprise value chain 
Enterprise AI applications 
  • Creating a business case for Enterprise AI 
  • Four Quadrants of the Enterprise AI business case 
  • Types of AI problems 
Enterprise AI – Deployment considerations 
  • A methodology to deploy AI applications in the Enterprise 
  • DevOps and the CI/CD philosophy
This book is available for free to DSC members exclusively, here
DSC Resources

Wednesday, October 24, 2018

29 Statistical Concepts Explained in Simple English

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC
29 Statistical Concepts Explained in Simple English

Machine Learning Perspective on the Twin Prime Conjecture

  This article focuses on the machine learning aspects of the problem, and the use of pattern recognition techniques leading to interesting,...