Deep Learning? What’s it?



AI is a big family! The 2 main members of this family are Machine Learning & Deep Learning! Don’t get scared by their names at all!


Tech in 3 has explained ML in very simple terms! If you haven’t read that, please visit our article on “What is Machine Learning??


Deep Learning is basically born out of mimicking the human brain, especially the neural system! The way we humans see objects, perceive them, learn them, and finally classify the output, DL is purely formed from that vision!


Deep Learning is not new! It has been there for years, but it is in the hype due to the ample availability of processing power and data!


Deep Learning is a subset of ML and is quite different from ML as in ML, we are manually selecting the features that identify our output but in DL, the model’s algorithm itself selects the best features that contribute to the output!


Let’s say, we are training a model, that classifies whether the input image we provide is of a cat or dog!



The main game is played here by the hidden layer and it’s better if one realizes very earlier that DL from inside works on pure mathematics!


Tasks such as Detection, Classification, Segmentation, Prediction & Recommendation are the main use cases of DL.


Detection:


Face detection, Video Surveillance, Object detection! These are the very common use cases of DL but it has much progressed in the healthcare sector these days that even COVID-19 has been claimed to be detected through X-Rays of patients.


Classification:

Say, you are having images of apples and oranges and you want a DL model that classifies whether the provided input image is of Apple or Orange? This task is known as classification!

Source: Health castle


Segmentation:

It is just like, identifying parts of the image and understanding what object or class they belong to, it is a basis for performing object detection and classification! There can be multiple objects in one image and if you want your DL model to identify all objects from the image, Segmentation will be to the rescue!


Source: Analytics Vidhya


Prediction:

A common example of this would be, predicting stock prices in advance from the past and current trends through the use of DL algorithms that learn from time-series data and predict the target variable so as to achieve business goals!

Source: Towards Data Science


Recommendation System:

As the name suggests, this application is basically used to develop a recommendation engine that recommends movies, products, etc based on past interests, purchases, and likes of users. This is a very common use case of DL algorithms that we often encounter on e-commerce and OTT platforms such as NetFlix, Amazon Prime, etc

Source: Towards Data Science


Deep learning models learn from their mistakes, So keep making more mistakes! You’ll learn better!


Source: medium.com