Why do we use standard deviation at most places when we have conceptually easier to understand mean absolute division? Let's try to figure it out.

A Simple yet Interesting Question in Statistics

Find solution to a **knight's tour problem** starting with any position.

What is a Tensor - in real physical sense? Is it a complex **physical entity**, a **double vector**, or just a **mathematical notation with no physical meaning**? Have an understanding from different points of view.

Create private equivalent of a fork of a public repo on github

After constructing the theoretical framework in the last chapter, **we will now be dealing with some of the practical difficulties**.*From traditional regression to neural networks - it's not that big a leap as you might think.* In this book, let's get a peek into this transition while appreciating how animal kingdom is already using this strategy. We will be taking help from our friend - *intuition* - time and again.

Let's Look at some of the Practical Difficulties

A simple 9x9 Sudoku Solver tool using backtracking. The current version runs on Python.

This page utilizes a service that solves a 9x9 Sudoku. The service is written in Python.

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See the Information Theory in a new light. Understand intuitively how the **information of an event naturally relates to its probability and encoding**

And, it's nice to know that understanding information theory helps in getting some of the aspects of **machine learning** as well.

In this chapter, we will be looking at the basics - the **idea of prediction**, using **traditional regression** and moving towards **learning based methods**.*From traditional regression to neural networks - it's not that big a leap as you might think.* In this book, let's get a peek into this transition while appreciating how animal kingdom is already using this strategy. We will be taking help from our friend - *intuition* - time and again.

Chapter 1: The Idea - Why Machine Leaning? What makes it different from linear and other simple regressions?

**From traditional regression to neural networks** - it's not that big a leap as you might think. Let's get a peek into this transition while appreciating how biology has figured out this strategy and worked it to almost perfection. We will be taking help from our friend - *intuition* - time and again.

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Archimedes' principle is straightforward, but let's see if there are other more natural explanations.

Multiple Explanations

In this chapter, we'll be looking at the description of **recurrent neural network (RNN)**.

Recurrent Neural Network - RNN

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In this chapter, we will be sharpening our **theoretical tools** and sneak our way into the **mathematics of neural networks.***From traditional regression to neural networks - it's not that big a leap as you might think.* In this book, let's get a peek into this transition while appreciating how animal kingdom is already using this strategy. We will be taking help from our friend - *intuition* - time and again.

Chapter 2: Beating the Theoretical Difficulties and Making Gradient Descent Work

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A very beautiful, counterintuitive and yet so simple puzzle! Have a look here, and see for yourself intuitively why it works.

Let's derive the probability equations that govern the predictions of the famous **Monty Hall problem**. Doing it for generalized number of total, closed and open doors gives us a better understanding and deeper satisfaction!

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Let's dissect one of the (if not **the**) most beautiful equations in mathematics.

A demo tool for scraping share prices from Google Finance.

This is a demo tool for scraping quotes from Google Finance.

A humble attempt at explaining the relativity of physics and the physics of relativity, with special treatment to vector analysis. Some knowledge of calculus is required.

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