The following are some of the points mentioned in the book : Cloud is just a building full of computers. There are racks of computers specially built to fit in datacenters A cloud provider rents computers as a service. It is akin to a car rental agency Cloud computing is the new electricity and everyone’s fighting to be the new utility provider of choice Cloud Storage came first with S3(Simple Storage Service) came in 2006 Cloud Computing began with EC2(Elastic Cloud Compute) in 2007 Software developers have made S3 and EC2 successful Cloud costing - Shift from Capex to Opex Cloud Native - Cloud-centric approach to developing software Netflix - An example of Cloud Native service: Early adopter of EC2 and one of the biggest success stories of Cloud Computers failing in the cloud is the norm.

This book is a great quick read that highlights various aspects of Neural Network components. There are three parts to the book. The first part of the book takes the reader through the various components of a Neural Network. The second part of the book equips the reader to write a simple piece of python code from scratch(not using any off-the-shelf library) that sets up a neural network, trains the neural network and test the structure for a certain set of test samples.

The author says that there are five things about Neural Networks that any ML enthusiast should know: Neural Networks are specific : They are always built to solve a specific problem Neural Networks have three basic parts, i.e. Input Layer, Hidden Layer and Output Layer Neural Networks are built in two ways Feed Forward : In this type of network, signals travel only one way, from input to output.

The entire book is organized as 20 small bite sized chapters. Each chapter focuses on one specific thing and explains everything via visuals(as is obvious from the title). The author starts off by explaining the basic idea of Random Forests, i.e. a collection of decision trees that have been generated via randomization. The randomness comes from the fact that a random subset is used from training the dataset and a random set of attributes are used for splitting the data.

This book provides a non-mathy entry point in to the world of decision trees and random forests. Chapter 1 introduces the three kinds of learning algorithms: Supervised Learning Algorithm: The algo feeds on labeled data and then use the fitted algorithm on unlabeled data Unsupervised Learning Algorithm: The algo feeds on unlabeled data to figure out structure and patterns on its own Semi-Supervised Learning: The algo feeds on labeled and unlabeled data.