In the 21st century, with the internet and technology, our life has been totally changed. It has made possible what we used to wonder about. There are lots of technologies used to make a digital era possible.
Deep learning is one of the technologies that helped a lot in creating a world of machines and robots. Now you might be thinking, what is deep learning. It's a great human evolution that helps in automating predictive analytics.
But before that learning that, have you ever wondered how it is possible to translate a paragraph from one to another language in a few seconds using google translate. How an OTT platform like Netflix or Amazon Prime is able to figure out our taste in videos or movies and provide us with perfect suggestions.
Or how it is possible to have a self-driving car which is totally driverless. Well, it's all possible through deep learning, or you can say that these are the products of deep learning.
This article will give you a brief deep learning explanation that will help you to understand what is deep learning, how deep learning works, methods, examples and more. So read this article till the end. Let's dive into deep learning.
What is Deep Learning?
Deep learning is a part of machine learning, and machine learning is the type of AI or artificial intelligence. As we know, artificial intelligence is a general term that refers to techniques that enable computers to mimic human behaviour. Whereas machine learning represents a set of algorithms trained on data that make all of this possible and return output based on a series of inputs.
Now deep learning is just a type of machine learning inspired by the structure of a human brain that is the algorithm in deep learning attempt to draw similar conclusions as a human would by constantly analyzing data with a given logical structure. To achieve this deep learning uses a multi-layer structure of algorithms called neural networks.
Data Scientists require machine learning models that are statistics and predictive. It helps them a lot by making the process faster and easier in compiling, analyzing and interpreting huge amounts of data.
Deep Learning Neural Networks
Neural networks are just like our brain, similar to how we use the brain to identify patterns and classify different types of information. Neural networks can be taught to perform the same task on data. Our brain attempts to compare the data with learned things whenever we acquire fresh information. The same concept is also thereby deep neural networks.
If you have ever seen a program that can recognize a flower species based on a photo, or a song based on the sound of someone humming it, that is a result of a neural network.
Beyond Image and song recognitions, deep learning applications can be found in speech recognition and translation software, and self-driving cars.
Difference between Deep Learning and Machine Learning
Both Machine Learning (ML) and Deep Learning (DL) are subfields of AI. Deep learning however has evolved faster over the last few years. This subfield is the advanced phase of machine learning. It's also important to understand the difference between machine learning and deep learning.
In machine learning, humans create algorithms that explore the data learn to form it and derive analysis. Deep learning is radically different. The unique feature of this subfield is that it works on an artificial neural network or ANN, which bears a close similarity to the functioning of a human brain. Machines with deep learning abilities don't need the support of human programmers. They are stand-alone machines.
Let's take an example of understanding how deep learning is different from machine learning.
Suppose we want to build a machine that differentiates between a cat and a dog. If you do it by machine learning, then we need to tell features based on which the two can be differentiated. These features can be the sound they make or the type of clause they have.
But instead with deep learning, these features are picked out by neural networks without human intervention.
How does Deep Learning Work?
A Neural Network is a very loose model of the human brain that we can program in a computer, or it's perhaps more appropriate to say that it is inspired by our knowledge of the inner workings of a human brain.
Let's try to understand how deep learning works by building a hypothetical aeroplane ticket price estimation tool. We will train it by using a supervised learning method, and our price estimator will predict the price using the following inputs.
- Origin Airport
- Destination Airport
- Departure Date
- Airline
So just as a recap, a neural has three ingredients to it. First is the input layer, which receives the input, which in our case is the four neurons in the input layer origin airport, destination airport, departure date and airline.
The input layer passes the inputs to the first hidden layer in the network. The hidden layer is the one which performs mathematical computation on the inputs.
One of the challenges in creating neural networks is deciding the number of hidden layers and also the number of neurons per layer. We should remember the term "deep" in deep learning refers to having more than one hidden layer in it.
And lastly, the output layer returns the output data which in our case is the price predictions. But you must be wondering how it computes the price prediction. Well, each connection between neurons is associated with the weight, and this weight dictates the importance of the input value.
When predicting the price of an aeroplane, the departure date is one of the heavier factors than others. Hence, the departure date neuron connection will have a higher weight.
Now as a single neuron in the human brain receives thousands of signals from other neurons, similarly, in an artificial neural network, signals travel between nodes and assign corresponding weights to the next one.
A heavier weighted node will exert more effect on the next layer of the nodes. In the end, the final layer compiles the weighted inputs to produce an output.
Deep learning examples
There are many tasks that can be done by deep learning because it processes data in forms similar to the human brain. It is mostly used in image recognition tools, natural language processing (NLP) and speech recognition apps.
Currently, it is used in all types of huge data analytics applications, which are basically focused on NLP, stock market trading alerts, network security and picture recognition.
Limitations of Deep Learning Simplified
Now you might be thinking that deep learning can change the world, right? Well, it does have a vast scope but carries some limitations too. Deep learning models learn through observations, and they only know what they have trained on.
A deep learning model trained on a small or irrelevant data set will learn in ways that are not ultimately useful to the task in hand. Here are some points for Limitations of Deep Learning.
Data
As training, a deep learning model requires huge chunks of data set to make it decently accurate.
Computational Power
Traning a deep learning system requires a high amount of computation. That's why we generally employ using GPU or graphical processing unit which have more core than CPU and also carries a higher cost.
Traning Time
Traning an average deep learning system can take weeks or even months to process and perfect. The training time is usually dependent on the amount of data and the number of layers in the hidden network.