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?

MNIST: The 'Hello World' of Machine Learning Programming

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

Go back to using the **old style Wikipedia** that had more content per screen (on widescreens), without using browser extensions and **without compromising privacy**.

Get path to selected items (in Windows explorer) into the clipboard

Let's make the computer play thousands of times betting on different strategies to see if it's really a fallacy. And more!

It's possible! With VS Code, we can connect to a **Linux** machine from **Windows** and work on our code as if the code was sitting on our host machine.

Further, we can even configure VS Code to read our SSH config and present us with our pre-configured connections along with the option of using public key authentication.

Some Important Tweaks

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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.

Learn about **LSTMs**, and see why they work the way they do by **interacting** with one!

It's astonishing to see that by using a very **simple mechanism**, we can somewhat generate the pattern long and short term memory are supposed to follow.

LSTM or Long-Short Term Memory - Learn by Interacting

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

Let's get to the basic physics of flying using a thrust that is less than the weight of the body. **It's not only possible, but it's so natural** that we should actually ask why do we need any thrust at all!

With a Thrust-to-Weight Ratio of Less Than 1!

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

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!

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

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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