Untangling the knots of deep learning

There has been a lot of probing around machine learning since its introduction to the mainstream industries. We often run around with unfounded knowledge on technologies like AI, machine learning, and data science, adding to the misinformation that has been piling up in the last few years. The latest addition to this list is deep learning. Everybody wants to talk about deep learning; each one of them has a vague conception of it; no one is really sure, they express their opinions nonetheless. And there you have the birth of another modern myth. The concept behind deep learning is a complex one, no denying that. But that does not mean we cannot try to break that down and present in layman’s terms. Hence, without further ado let us delve deep.

Let us start with machine learning

Let there be no doubt about the fact that deep learning is essentially a type of machine learning. So, in order to know deep learning we need a basic understanding of machine learning.

Machine learning refers to the process of training machines to learn from data so that they can recognize patterns and make predictions. Now, let us break that down. Let us suppose you want to create a program that can identify a certain age group amongst your consumers which is most likely to respond to a certain ad.

The key resources in this case may be the purchase history of the customers, the response rate to previous ads, the wealth of the customers, etc. Your program should be able to take all these parameters into account, match one source of data with another and provide you with critical insights. The mathematical models which are used to process the data are called algorithms. These algorithms can be used for supervised learning or unsupervised learning depending on the purpose of the operation and the kind of data.

Supervised and unsupervised learning

In case of supervised learning the machine learns from data which is labelled, that is both the inputs and the outputs of the training data is predetermined. The program is therefore prepared to recognize data which is similar to the input data and predicts the outputs.

In unsupervised learning the program learns from the features of the training data since the outputs are not labelled. Once trained the algorithms can be used to segregate data with similar features.

You will gather a deeper understanding of these processes if you pursue a machine learning certification in India.

The applications of these two types of machine learning are expanding fast into new territories. The advent of deep learning has added a new vigor to the whole AI revolution.

Introduction to deep learning

Let us start by talking about neural networks.

Before we get into the intricacies of deep learning let us first check our knowledge of the human nervous system. The unit of our nervous system is called neuron. We have billions of those in our system. The neurons are interconnected cellular organisms that work like the postal system which conducts electrical signals called stimuli. When our sensory organs come under the influence of any external stimulus the neurons carry the signals to the brain and the brain relays commands which lead to our reaction to that particular stimulus. The brain learns to react in a certain way to certain stimuli with the primary goals of surviving and deriving pleasure.

Neural networks are the artificial manifestation of a nervous system where the electrical signals are replaced by mathematical signals and neurons by nodes. Every node in a neural network is interconnected. These interconnected nodes together make a layer. As the amount of data to be processed increases the neural networks require more layers. The number of layers in a neural network is referred to its depth. When we say deep learning what we really refer to is the depth of these neural networks.

As said earlier this is a form of machine learning but since it is empowered by deep neural networks it is called deep learning.   

Key applications of deep learning

Deep learning plays a crucial part in computer vision, voice recognition, and natural language processing, among other facets of AI based technology. Artificial intelligence is way more useful today thanks to deep learning. Neural networks are best equipped to deal with unstructured data.

Since running deep learning models is expensive and requires a lot of data its applications are limited to companies which have that kind of resources. It also takes state of the art computational abilities to use deep learning.

Hopefully this post has answered some questions about deep learning. Keep the curiosity alive and there will always be answers.