Moving on from autoencoders, it’s time to look at the most widely used applications of Deep Learning. If you missed the previous article on autoencoders, you can find it here: https://bit.ly/3c2nxNv
Neural networks are known for their mechanism on extracting hidden patterns; patterns that cannot be distinctly observed by the human eye as well. This property of a neural network can come in handy for recognising patterns in images and classifying the object inside the image accordingly. But before we go about classifying images, let’s look at how convolution works.
The Convolution Operation:
Consider a scenario wherein we’re tracking the…
In the last week, we took a plunge into the core concepts of Deep Learning and the framework of a Neural Network. We also touched upon the basics of an objective function and its use, the kinds of objective functions and how Gradient Descent plays a pivotal role in rectifying the mistakes/error while predicting a value.
In this article, we shall feast our mind and thought on Autoencoders.
Starting off , let’s have a look at what Autoencoders are. Most of you would be aware of the basic form and structure of a neural network. An auto encoder is a…
In the last week, we took a plunge into the core concepts of Deep Learning and the framework of a Neural Network. We also touched upon the basics of an objective function and its use, the kinds of objective functions and how Gradient Descent plays a pivotal role in rectifying the mistakes/error while predicting a value.
In this article, we shall feast our mind and thought on :
- Principal Component Analysis (PCA)
- Single Value Decomposition (SVD).
- Types of Gradient Descent.
1. Principal Component Analysis
Principal component analysis is a dimensionality reduction method that helps in tackling the dimensions in large data sets. The main…
Last week, we touched upon what a neural network actually does and introduced Deep Learning in brief. If you missed last week’s article, you can find it here.
This week, we’ll dive right in and start off with the core concepts of Deep Learning in pure mathematical detail. After reading this article, you’ll be able to understand:
- Structures of different types of neurons
- The necessity of objective functions, loss functions and activation functions
- The learning algorithm employed in order to help the network draw relations between the outputs and the inputs
- Why Gradient Descent is such a big deal for…
In the recent years of technological advances, deep learning has become quite a buzzword. Some of you probably know it, some of you probably just heard about it and never bothered, while some are extremely clueless about it. This article aims to shine light on the what, where, why and hows of Deep Learning.
The uniqueness of any deep learning algorithm lies in the inherent strength which can easily run through any kind of data — images, voice or even text. Yes, you see the word algorithm there. It isn’t simple 5–10 lines of code that one can write and…
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