Deep Learning

Why Deep Learning is called Deep Learning?

In this post, you will learn why deep learning is called as deep learning.

You may recall that deep learning is a subfield of machine learning. One of the key difference between deep learning and machine learning is in the manner the representations / features of data is learnt. In machine learning, the representations of data need to be hand-crafted by the data scientists. In deep learning, the representations of data is learnt automatically as part of learning process.

As a matter of fact, in deep learning, layered representations of data is learnt. The layered representations of data are learnt via models called as neural networks. The diagram below represents the multiple layers using which the representation of number 4 is learnt. The diagram is taken from one of my favorite books, Deep Learning with Python by  Francois Chollet

Fig 1. Deep Learning – Learning Layered Representations of Data

One may note that there are four different successive layers through which data passes before being classified as digit 4. From the above diagram, you may note that the neural network transforms the digit image into representations that are increasingly different from the original image and increasingly informative about the final result. Thus, the model (neural network) learns different representations of data such as those above in order to identify the digit. In modern deep learning models , hundreds of layered representations of data is learnt from the training data. 

If the number of layered representations which need to be learnt are one or two, the learning is called as shallow learning and the model is termed as shallow neural network. In case, the large number of representations need to be learnt, the learning is called as deep learning and the model is called as deep neural network. The deep learning, at times, is also termed as layered representations learning or hierarchical representations learning. 

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog, Revive-n-Thrive.com

Recent Posts

Pricing Analytics in Banking: Strategies, Examples

Last updated: 15th May, 2024 Have you ever wondered how your bank decides what to…

2 days ago

How to Learn Effectively: A Holistic Approach

In this fast-changing world, the ability to learn effectively is more valuable than ever. Whether…

4 days ago

How to Choose Right Statistical Tests: Examples

Last updated: 13th May, 2024 Whether you are a researcher, data analyst, or data scientist,…

4 days ago

Data Lakehouses Fundamentals & Examples

Last updated: 12th May, 2024 Data lakehouses are a relatively new concept in the data…

5 days ago

Machine Learning Lifecycle: Data to Deployment Example

Last updated: 12th May 2024 In this blog, we get an overview of the machine…

5 days ago

Autoencoder vs Variational Autoencoder (VAE): Differences, Example

Last updated: 12th May, 2024 In the world of generative AI models, autoencoders (AE) and…

5 days ago