Simple. Neural Networks only understand numerical data. Their weights, their biases, and their training, all take place using numerical values, floats, and integers, yada yada. Now when we want to work with words and other textual data, we need a way to convert them to numerical representation, that’s where word embeddings come under the picture. …
Generative Adversarial Networks (GANs) are incredibly powerful in the world of machine learning. They excel at creating synthetic data that’s so close to real data, it’s almost uncanny. Today, we’re diving into how these networks work and even trying our hand at building a simple GAN framework using PyTorch. But why the term “Adversarial”? Well, …
Generative Adversarial Networks (GANs) From Scratch Read More »
With the concerns about data privacy rising and with the rise of AI models, people are increasingly concerned about their data being misused for purposes beyond their consent. As we know that data is the new oil of this century, it is a very sought after commodity. Seeing these concerns, Google introduced the concept of …
The special ingredient to machine learning. We learned in the last post about Linear Regression. We concluded with a cost function that we needed to minimize. Today we will see how we minimize this cost function. To recap, the cost function was : Here, h𝚯(x) is the linear regression equation that we discussed earlier( y= …
A short guide on how to implement linear regression using libraries. Hola mates, long time no C. Yeah, I am just a Python guy and I don’t like C/C++ too much simply because they aren’t that handy. A programming language is a tool that should be easy to use and should allow you to implement …
Let’s look at the hello world of machine learning algorithms. In this post, we will look at a very simple machine learning algorithm which is actually the “hello world” equivalent of programming languages. So what is linear regression? If you are from a statistics background chances are you already know what it means. In statistics, …