A Simple Explanation of Regularization in Machine Learning

In this post, we are going to look into regularization and also implement it from scratch in python (Part02). We will see with example and nice visuals to understand it in a much better way. We already know about the Linear regression where this is used.

Let's get started!!
The first Question always coming to my mind after hearing this term is:
What is regularisation? This is a technique to minimize the complexity of the model (we will see what we mean by that) by penalizing the loss function to solve overfitting
The above definition gives three things to be looked into detail. Minimize the complexityPenalize the loss functionSolve the overfitting (Generalization).
1)Minimizing complexity. What do we mean by that? Consider a simple example. you are trying to predict the score of students in the exam. We use a number of booksread as a feature to predict.
This model will not learn anything new but it can find a few patterns but not enough to predict the score. This is called the underfitti…

In-depth Explained Simple Linear Regression from Scratch - Part 1

In my opinion, most Machine Learning tutorials aren’t beginner-friendly enough. It very math-heavy or it doesn't help you with the algorithms behind it. In this post, we are going to do the simple Linear Regression from scratch. We will see the mathematical intuition behind it and we write the code from scratch + test it and I'm super excited to get started!!

Ready? Let’s jump in.
Let's get the intro done!
The simplest form of the linear regression model is also the linear function of the input variables. However, we can obtain a much more useful class of functions by taking linear combinations of a fixed set of nonlinear functions of the input variables, known as basis functions. Such models are linear functions of the parameters, which gives them simple analytical properties and yet can be nonlinear with respect to the input variables.

The Motivation For most applications, knowing that such a linear relationship exists isn’t enough. We’ll want to understand the nature of the r…

Overview Guide To Tensorflow 2.x with Examples

The most concise and complete explanation of what TensorFlow is can be found at ( and it highlights every important part of the library.

TensorFlow is an open-source software library for high-performance numerical computation.

Its flexible architecture allows easy deployment of computation across a range of platforms (CPUs, GPUs, and TPUs), from desktops to clusters of servers, to mobile and edge devices.

Originally developed by researchers and engineers from the Google Brain team within Google's AI organization, it comes with strong support for machine learning and deep learning,and therefore the flexible numerical computation core is employed across many other scientific domains.

In this blog post, we are going to see the basics for TensorFlow 2.x. This can be used as getting started guide to learn and understand it.

I'm not going to cover the installation/Setup of the Jupyter part as this can be found online easily.

How to Install TF2.0

We will see ho…