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?

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

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.

**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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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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