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.
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.
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
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.
A truly portable and secure solution for managing your 2FA accounts! No need to worry about losing your laptop, phone or changing from Android to iOS or vice versa (well, at least in terms of having your 2FA accounts reconfigured 🙂).
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.
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 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!
Archimedes' principle is straightforward, but let's see if there are other more natural explanations.
In this chapter, we'll be looking at the description of recurrent neural network (RNN).
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.
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.
