Not Davinci, we need ChatGPT itself. — So, I had to work recently to integrate ChatGPT into a Python app, and to not re-invent the wheel, as any developer would do, I started searching about it on Medium, and other websites. So, after spending a lot of time, all I could really find was implementations of The…

No hard and fast rule, choose the one you like. — It’s been so long since my last post on Web Development. So, here is another one. In this post, I will mention 8 ways to handle state in a React application. …

A look into how a Multi-Layered Perception performs its feedforward. — “Deep Learning is the new Electricity”. ~ Andrew NG Yet so many people fail to understand it. The study and understanding of core concepts in Deep Learning require background knowledge in Linear Algebra, Calculus, Probability, and Statistics. …

A quick intro to the topic. — This article is actually a continuum of a series that focuses on the basic understanding of the building blocks of Deep Learning. Some of the previous articles are, in case you need to catch up: Multi-class Classification? Yes. Let’s discuss what is it!medium.com Why should we use Batch Normalization in Deep Learning? Let’s discuss why!becominghuman.ai Insights to How to Tune Your Hyper Parameters A guide to tuning your Hyper Parameters to get the best accuracy.d3nyal.medium.com

Let’s discuss what is it! — Stuck behind the paywall? Click here to read the full story with my friend link! This article is actually a continuum of a series that focuses on the basic understanding of the building blocks of Deep Learning. Some of the previous articles are, in case you need to catch up: Insights to how to tune your hyper parameters! A guide to tuning your Hyper Parameters to get the best accuracy.medium.com

Let’s discuss why! — Stuck behind the paywall? Click here to read the full story with my friend link! This article is actually a continuum of a series that focuses on the basic understanding of the building blocks of Deep Learning. Some of the previous articles are, in case you need to catch up: Want to Optimize your Model? Use Learning Rate Decay! Adapting your Learning Rate Parameter with time can make a huge difference! Let’s see how.medium.com

A guide to tuning your Hyper Parameters to get the best accuracy. — Stuck behind the paywall? Click here to read the full story with my friend link! This article is actually a continuum of a series that focuses on the basic understanding of the building blocks of Deep Learning. Some of the previous articles are, in case you need to catch up: Want to Optimize your Model? Use Learning Rate Decay! Adapting your Learning Rate Parameter with time can make a huge difference! Let’s see how.medium.com

Adapting your Learning Rate Parameter with time can make a huge difference! Let’s see how. — Stuck behind the paywall? Click here to read the full story with my friend link! This article is actually a continuum of a series that focuses on the basic understanding of the building blocks of Deep Learning. Some of the previous articles are, in case you need to catch up: Want your model to converge faster? Use RMSProp! This is another technique used to speed up Training.medium.com

This is used to speed up Gradient Descent. — Stuck behind the paywall? Click here to read the full story with my friend link! This article is actually a continual of previous series of articles. Here are links to the stories, if you will to come along. Model Overfitting? Use L2 Regularization! Use this to enhance your Deep Learning models!medium.com Training taking too long? Use Exponentially Weighted Averages! Use this optimization to speed up your training!medium.com