Allowing computers to see sounds pretty exciting and scary at the same time. However, as I have said before, computers “see” differently than we do. Where we can perceive the shapes, colors, and movements of different objects, machines receive millions of numerical values. This means that where one person can identify an orange color in a picture, the computer would instead have a range of numbers displaying the RGB values of each picture.

One might ask why Multi-layer Perceptrons (MLPs) are not enough to enable deep learning image analysis. Well in Machine Learning, there exists an algorithm much more suited…

Before Reading:

Make sure to pay special attention to the different types of notation being used throughout this article. By far the most intimidating factor in Backpropagation relates to one not understanding the different symbols and indices. A sound understanding will clear the confusion up.

From the previous article on Backpropagation, we learned that after the input data goes through our Neural Network through Forward Propagation, we get a certain output. We then use an activation function to calculate the loss for that output using the actual correct values — telling us how “off” our values are. This is where…

This article focuses on a new concept called Backpropagation as it is in this process that an Artificial Neural Network trains itself to become what is commonly referred to as “Intelligent”.

Backpropagation is the process of calculating gradients that eventually leads our model to update the weights to the optimal values corresponding to the lowest cost function output through numerous iterations. Since backpropagation has a bad reputation for being an intimidating topic to cover, I’ll be going over the intuition behind backpropagation first and then the necessary calculus that is required in a future article.

In the article where I…

Preface: For the rest of this article, it is recommended that you have a high level understanding of what a Neural Network is. In order for you to appreciate how activation functions help us, you first need to understand what the main motivation is behind using Neural Networks. If that sounds like you, keep reading on!

Activation functions are what some would call one of the main building blocks of Artificial Neural Networks (ANNs). In a world where the majority of the problems in the world cannot be fixed by approximating straight lines, activation functions become essential to ANNs. …

A subset of Artificial Intelligence, Machine Learning is an extremely broad field that solves numerous problems such as finding various groupings from data and using regression techniques for predictive analytics. This article focuses on a sub-category of Machine Learning called Deep Learning.

With discoveries made every day, Deep Learning is a fascinating field of research. Deep Learning is the method of using Artificial Neural Networks (ANNs) to learn from given data. A Neural Network is a type of algorithm inspired by the biological function of neurons in the brain. …

Before we jump in to our topic, allow me to introduce myself. I’m Rohan, an A.I. focused software developer training to become a future CEO to impact billions! Well…I hope so at least. Anyways, this article steps a bit outside of my usual trend of covering Machine Learning concepts as I’ll introduce a real-life application that you can implement yourself. Hope you gain something out of this article!

So it’s true! Our computers can’t “see” the same way as we do but we have found a way to make them able process images. In fact, with a little bit of…

Hey Readers! This is a continuation of my medium trend — understanding the theory/mathematics behind machine learning. Since I have started a focus on this topic, I will be writing medium articles describing my journey, stay tuned for more content. Now, back to my article!

In a previous article, I’ve covered the mathematics behind univariate linear regression and the implementation of it in Python. Now you may be looking at multivariate linear regression and think, “Well, my brain is about to be fried”. And while we will build on previous concepts, I assure you this topic is much easier to…

What’s up readers! I’m going to be starting a medium trend to write an article on each concept I learn about my focus — Artificial Intelligence — for the next few months in Python. This will be the first of many to come so stay tuned for more insight and explanations!

Hopefully, you’ve read my first article on Linear Regression and are now ready to dive deep into how machines can actually come up with the lines-of-best-fit — univariate linear regression. Get ready for some mathematical enlightenment! (P.S. …

What’s up readers! I’m going to be starting a medium trend to write an article on each concept I learn about my focus for the next few months — Artificial Intelligence — in Python. This will be the first of many to come so stay tuned for more insight and explanations!

If you're new to Artificial Intelligence, chances are you’ve heard the term “**Linear Regression**” being thrown around. Jeez..could they have picked a more intimidating name? Anyway, I’m here to tell you that it is in no way as complex as the name makes it out to be. …

While pop-culture describes Artificial Intelligence as human-eliminating robots from Skynet or uber-intelligent programs like J.A.R.V.I.S — Artificial Intelligence is the development of machines that can think like humans…but not in the way you might **think**. (Yes, pun intended)

From social media applications on our phones to the red-light cameras detecting distracted drivers at intersections, A.I. has become an inescapable reality. Since I can **guarantee** you have used A.I. before, why don’t I provide a brief introduction?

Even with groundbreaking discoveries every day, our society is still at a stage called “**Artificial Narrow Intelligence**”(ANI) or “**Weak A.I.**”. Essentially, this refers to…

Hey everyone! My name is Rohan, a 16-year-old high school student learning about Artificial Intelligence. Read my articles to learn more about it!