Writes about machine learning, data science, artificial intelligence and more | Looking forward to solving real world problems as a data scientist.

Neural networks are algorithms that have a unique ability to extract meaningful information from complex data — Data that are extremely complex for a human brain to follow.

Let’s say a cat classifier, What are the features you would use to train a model to classify whether a given image is a cat or not? At first, it sounds easy, you would go for features like the size, color, paws, teeth, etc. But there are 40–70 different breeds of cats in the world and each of them differ somewhat in their color, size, etc. Now all of a sudden this…

Gradient descent is an iterative optimization algorithm that is used for finding the local/global minimum of a differential function. Since the main goal of this function is to find the minimum, it is widely used in machine learning to find the perfect parameters that minimize the cost value.

In this article, we will see how this algorithm works, the mathematics behind this algorithm, and the types of gradient descent.

Let’s say we have a task. The goal is to create an algorithm that can predict whether the given image of an animal is a cat or not — for each…

Machine Learning is used in multiple fields and industries. When I say multiple, I mean it.

Let’s say like, Cooking industry!

“What? Cooking? How is it even related to cooking?!!”

Well.. it *is* actually. Industries have developed food sorting solutions with machine learning capabilities. The systems use various technologies, including cameras and near-infrared sensors, to sort vegetables based on their size and color. For example, sorting potatoes by size can help manufacturers decide which ones should be made into French fries versus potato chips.

“Whoa!”