The Beginners Guide to Deep Learning – Introduction

The concept that robots are smarter than humans has captured our collective imagination for as long as there has been Science Fiction. 

However, while Artificial Intelligence (AI) has not yet reached that level, we have made significant breakthroughs in generating machine intelligence, as proven by Google, Tesla, and Uber testing with self-driving cars. 

The scalability and utility of Deep Learning, the Machine Learning approach that enables this technical advancement, is partly responsible for AI’s successful transition from universities and research laboratories to products. 

The next computer revolution will be built on artificial intelligence, deep learning, and machine learning. 

These technologies are built on the capacity to discern patterns and then forecast future events based on data collected in the past. This explains why Amazon makes ideas when you purchase online or how Netflix knows you like awful 1980s movies.

Although computers that use AI concepts are sometimes called “smart,” the majority of these systems do not learn on their own; human interaction is required.

Data scientists prepare the inputs by picking the variables that will be applied in predictive analytics. Deep learning, on the other hand, can perform this function automatically.

This post serves as a field guide for any data lovers interested in learning more about deep learning, its breadth, and future potential.

What is Deep Learning?

Deep learning can be thought of as a subset of machine learning. 

It is a field that is built on self-learning and improvement through examining computer algorithms. 

Deep learning, as opposed to machine learning, works with artificial neural networks, which are supposed to mimic how people think and learn. Until recently, neural networks were restricted in complexity due to computer power constraints. 

However, advances in Big Data analytics have enabled larger, more powerful neural networks, enabling computers to monitor, understand, and respond to complicated situations quicker than people. 

We recommend reading – Tesla Neural Network Architecture Explained

Image categorization, language translation, and speech recognition have all benefited from deep learning. It can tackle any pattern recognition issue without the need for human interaction.

It’s essentially a three- or more-layered neural network. These neural networks seek to imitate the activity of the human brain, albeit with limited success, by enabling it to “learn” from enormous volumes of data. 

While a single layer of a neural network can still produce approximate predictions, more hidden layers can help in optimizing and tuning for accuracy.

What is Neural Network?

Artificial neural networks are based on neural nets seen in the human brain. Usually, a neural network is made up of three layers. 

The three levels are input, output, and concealed. A neural network in action is seen in the diagram below. 

As the neural network shown above only has one hidden layer, it’s dubbed a “shallow neural network.”

More hidden layers are added to such systems to form more sophisticated structures.

What is Deep Network?

In a deep network, many hidden layers are added. 

Training such designs becomes increasingly complicated as the number of hidden layers in the network rises, not only regarding the time required to properly train the network but also in terms of the resources required. 

A deep network with an input, four hidden layers, and an output is shown below.

How does Deep Learning work?

Neural networks are built up of layers of nodes, similar to how neurons make up the human brain. Individual layer nodes are linked to nodes in neighboring layers. 

The number of layers in a network indicates its depth. A single neuron in the human brain receives thousands of messages.

Signals move between nodes in an artificial neural network, which assigns weights to them. 

A node with a higher weight has a greater impact on the nodes below it. The last layer combines the weighted inputs to provide an output. 

Deep learning systems need strong hardware due to the massive quantity of data handled and the numerous sophisticated mathematical computations involved.

Deep learning training calculations, even with such sophisticated technology, can take weeks.

Deep learning systems require a significant quantity of data to provide correct findings; hence, information is fed in the form of massive datasets. 

When processing data, artificial neural networks can classify information based on responses to a sequence of binary yes or false questions that involve very complicated mathematical computations. 

A facial recognition algorithm, for example, learns to identify and recognize the edges and lines of faces.

Then more significant elements of faces, and eventually entire representations of faces. 

The algorithm trains itself over time, increasing the likelihood of the right replies.

In this situation, the facial recognition algorithm will recognize faces more correctly over time.

Deep Learning VS Machine Learning

How does deep learning differ from machine learning if it is a subset of it? 

Deep learning differs from traditional machine learning in the types of data it uses and the methods it uses to learn.

To create predictions, machine learning algorithms use structured, labeled data, which means that certain characteristics are specified from the model’s input data and grouped into tables. 

This does not necessarily imply that it does not employ unstructured data; rather, if it does, it usually goes through some pre-processing to put it into a structured format.

Deep learning does away with part of the data pre-processing that machine learning generally entails.

These algorithms can ingest and interpret unstructured data such as text and pictures, as well as automate feature extraction, reducing reliance on human specialists. 

Let’s imagine we had a collection of images of various pets that we wanted to organize into categories such as “cat,” “dog,” “hamster,” and so on. 

Deep learning algorithms can figure out which traits (such as ears) are most essential in separating one animal from another. This feature hierarchy is manually determined by a human specialist in machine learning.

The deep learning system then changes and fits itself for accuracy via gradient descent and backpropagation, allowing it to generate more precise predictions about a fresh snapshot of an animal.

Deep Learning applications

1. Chatbots

Chatbots can fix client issues in a matter of seconds. A chatbot is an artificial intelligence (AI) tool that allows you to communicate online via text or text-to-speech. 

It can communicate and conduct acts in the same way humans do. Chatbots are widely used in customer service, social media marketing, and client instant messaging. 

It responds to your inputs with automatic answers. It generates many forms of replies using machine learning and deep learning techniques.

2. Self-driving cars

Deep Learning is the primary factor behind self-driving cars becoming a reality. 

A million data sets are loaded into a system to create a model, train the machines to learn, and then evaluate the findings in a safe environment. 

The Uber Artificial Intelligence Labs in Pittsburgh is not only trying to make driverless cars more common but also to integrate numerous smart features, such as food delivery possibilities, with the usage of driverless cars. 

The most pressing worry for self-driving vehicle development is dealing with unanticipated events. 

A continual cycle of testing and implementation, typical of deep learning algorithms, ensures safe driving as it is exposed to millions of scenarios more and more.

3. Virtual Assistant

Virtual Assistants are cloud-based programs that recognize natural language voice commands and do things on your behalf. 

Virtual assistants such as Amazon Alexa, Cortana, Siri, and Google Assistant are common examples. 

To fully utilize their potential, they require internet-connected devices. When a command is given to the assistant, it tends to deliver a better experience based on previous encounters utilizing Deep Learning algorithms.

4. Entertainment

Companies like Netflix, Amazon, YouTube, and Spotify provide appropriate movie, song, and video suggestions to their customers to improve their experience.

Deep Learning is responsible for all of this. 

Online streaming firms provide product and service recommendations based on a person’s browsing history, interests, and activity. 

Deep learning algorithms are also used to produce subtitles automatically and add sound to silent movies.

5. Robotics

Deep Learning is widely employed in developing robots that can do human-like jobs. 

Deep Learning-powered robots employ real-time updates to detect barriers in their route and quickly arrange their course.

It can be used to transport things in hospitals, factories, warehouses, inventory management, product manufacture, and so on.

Boston Dynamics robots respond to humans when they are pushed about. They can empty a dishwasher, they can get up when they fall, and they can accomplish a variety of other activities.

6. Healthcare

Doctors cannot be with their patients around the clock, but one thing we all virtually always have with us is our phones. 

Deep learning also allows medical technologies to analyze data from images we capture and movement data to uncover potential health concerns. 

AI’s computer vision program, for example, uses this data to follow a patient’s movement patterns to forecast falls as well as changes in a  mental state. 

Deep learning has also been used to identify skin cancer using photos and many more.

7. Natural Language Processing

Developing natural language processing technology has enabled robots to read communications and derive meaning from them. 

Nonetheless, the approach can be oversimplified, failing to account for the ways in which words join to affect the meaning or purpose of a phrase. 

Deep learning helps natural language processors to recognize more complex patterns in phrases and deliver more accurate interpretations.

8. Computer Vision

Deep learning tries to replicate how the human mind processes information and recognizes patterns, making it an ideal method for training vision-based AI applications. 

Those systems can take in a succession of tagged photo sets and learn to recognize items like airplanes, faces, and weaponry using deep learning models.

Deep Learning in Action

Aside from your favorite music streaming service recommending songs you might like, how is deep learning changing people’s lives? 

Deep learning, it turns out, is making its way into a wide range of applications. Anyone who uses Facebook will notice that when you post new images, the social site frequently recognizes and tags your pals. 

Deep learning is used for natural language processing and speech recognition by digital assistants such as Siri, Cortana, Alexa, and Google Now.

Real-time translation is provided via Skype. Many email services have advanced in their ability to detect spam messages before they reach the inbox. 

PayPal has used deep learning to prevent fraudulent payments. CamFind, for example, allows you to take a photo of any object and, using mobile visual search technology, determine what it is.

Deep learning is being used to provide solutions by Google in particular. AlphaGo, a computer program developed by Google Deepmind, has trounced current Go champions. 

WaveNet, developed by DeepMind, can create speech that sounds more natural than currently available speech systems. To translate oral and textual languages, Google Translate employs deep learning and picture recognition. 

Any photo can be identified using Google Planet. To aid in developing AI applications, Google created the Tensorflow deep learning software database.

Future of Deep Learning

Deep learning is an unavoidable topic while discussing technology. Needless to say, deep learning has evolved into one of technology’s most crucial elements.

Organizations used to be the only ones interested in technologies like AI, deep learning, machine learning, and so on. Individuals, too, are becoming interested in this element of technology, particularly deep learning. 

One of the many reasons deep learning is getting so much attention is its capacity to allow better data-driven decisions while also improving prediction accuracy.

Deep learning development tools, libraries, and languages might very well become regular components of any software development toolkit in a few years. 

These current tool sets will pave the way for simple design, setup, and training of new models.

Style transformation, auto-tagging, music creation, and other tasks would be much easier to do with these skills.

The demand for rapid coding has never been greater. 

Deep learning developers will increasingly use integrated, open, cloud-based development environments that allow access to a wide range of off-the-shelf and pluggable algorithm libraries in the future.

Deep learning has a very bright future!

The benefit of a neural network is that it excels at dealing with large amounts of heterogeneous data (think of everything our brains have to deal with, all the time). 

This is especially true in our age of powerful smart sensors, which can collect massive amounts of data. Traditional computer systems are struggling to sift, categorize, and derive conclusions from so much data.

Conclusion

Deep learning powers most of the artificial intelligence (AI) solutions that can improve automation and analytical processes.

Most individuals come into contact with deep learning daily when they utilize the internet or their mobile phones. 

Deep learning is used to produce subtitles for YouTube videos. Conduct voice recognition on phones and smart speakers.

Give face identification for images, and allow self-driving automobiles, among many other uses. 

And, as data scientists and academics tackle increasingly complicated deep learning projects using deep learning frameworks, this sort of artificial intelligence will become an increasingly important part of our daily lives.