**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

See the Information Theory in a new light. Read this article to understand intuitively how the **information of an event relates to its probability and encoding** - not because that's how it is defined, but because that is the natural way!

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

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

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

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.

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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

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?

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

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.

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

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.

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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