A Beginner’s Guide To Deep Learning

When you read the word “artificial intelligence”, you might imagine a popular science fiction movie about machines that advance way beyond the point of needing human assistance and then go on to destroy the planet. Thankfully, we have not yet reached that point, but machines can still accomplish some extraordinary tasks!

Machines are now capable of processing enormous amount of data and making extremely accurate judgement, which is otherwise very difficult to be done by a human. This is a classic example of machine learning-where human programmers develop algorithms to train machines and they make judgments based on the information they gather from the data.

Deep learning is a step beyond machine learning, in which machines can mimic the functioning of the human brain. Hang on! There’s more to it than just this. Continue reading to find more about deep learning.

What is Deep Learning?

Deep learning is a subset of machine learning, where machine systems learn in a way which comes naturally to humans: learn by examples. It teaches a machine how to identify and categorize information by filtering inputs through layers.

A large set of labeled data and neural network structures are used to train models. Input data can be in the form of images, text, or sound. The way through which the human brain filters information is the source of inspiration for deep learning. Its purpose is to create some actual magic by simulating how the human brain functions.

Any deep learning system would repeat and perform a task several times, modifying and improving the output a little more each time. The term “deep learning” was coined because neural networks have multiple layers that support learning, unlearning, and relearning.

Deep learning is a key component behind self-driving vehicles, that has trained them to identify a stop sign or differentiate between a pedestrian and a lamppost. It enables voice recognition in electronic products such as phones, tablets, televisions, and hands-free speakers. Deep learning has gotten a lot of recognition lately, and for good cause. It’s achieving results that were previously unimaginable.

How Deep Learning Works?

A deep learning model is built to evaluate data continuously using a logic framework, similar to how a human would make conclusions. Deep learning applications achieve this by utilizing a layered structure of algorithms known as an artificial neural network. The design of ANN is based on the biological neural network of the human brain, resulting in a far more capable learning process than standard machine learning models.

It’s a difficult task to ensure that a deep learning model doesn’t come to the wrong conclusions — as with other forms of AI, it takes a lot of training to get the learning processes right. However, when it works as intended, effective deep learning is regarded by many people as a scientific miracle and the backbone of real artificial intelligence.

Google’s AlphaGo is a fantastic example of deep learning. Google developed a computer programme that learnt to play the abstract board game Go, which is known for demanding great intellect and intuition. AlphaGo’s deep learning model learnt how to play at a level that was never seen previously in artificial intelligence. It was efficiently capable of playing against professional Go players, and this was all done without being directed when to make a specific move.

When AlphaGo defeated numerous world-renowned “masters” of the game, it created a lot of buzz- not only could a computer master the complex tactics of the game and abstract features, but it was also emerging as one of its best players.

The Difference Between Deep Learning and Machine Learning

Deep learning is a subset of machine learning in a practical sense. It can be regarded as a type of machine learning that functions in almost similar way to traditional machine learning (hence why the terms are sometimes used interchangeably). The capabilities of deep learning, on the other hand, are quite different.

While simple machine learning models improve over time at whatever task they are given, they still require some supervision. If an AI algorithm makes an incorrect prediction, an engineer must intervene and make changes. With a deep learning model, an algorithm may use its own neural network to assess if a prediction is correct or not.

Practical Examples of Deep Learning

1. Self-Driving Cars

self-driving cars, for example, are designed using deep neural networks, where these automobiles employ machine learning algorithms. Such automobiles detect objects in the near vicinity of the vehicle, the distance between the vehicle and other cars, the location of the footway, traffic signals, and the driver’s condition, among other things.

Tesla is the most reputable brand when it comes to bringing automated, self-driving cars to market.

2. Sentiment Analysis

Sentiment analysis is the process of using natural language processing, text analysis, computational linguistics, and bio-metrics to understand and analyse customer sentiments. A company tries to understand the sentiments and feelings of its customers based on what they say and how they say it, in order to figure out how they feel about the company. They can also categorize the statements as either favorable, unfavorable, or neutral.

Deep learning is suitable for sentiment analysis, sentiment classification, opinion/assessment mining, etc.

3. Social Media

Twitter uses deep learning algorithms to improve its services. They utilise a deep neural network to access and analyse a large amount of data in order to learn over time about the prospects of users ’ preferences.

Instagram uses deep learning to prevent cyberbullying and to remove annoying comments.

Deep learning is used by Facebook to recommend websites, friends, products, and other stuff. Furthermore, Facebook’s facial recognition system is based on the ANN algorithm, which makes accurate face-tagging possible.

In a nutshell, machine learning utilizes algorithms to interpret data, learn from it, and make logical decisions based on what it has learnt. Whereas, Deep learning is capable to structure algorithms in layers to develop an ‘artificial neural network’. With the help of this network, it can learn and make intelligent judgement on its own.

While both come under the umbrella of artificial intelligence, deep learning is the engine that drives the most human-like AI. Deep learning finds its applications in various fields such as virtual assistants, social media, sentiment analysis, etc. as mentioned above.