Introduction to Deep Learning
Deep Learning (DL) is becoming increasingly popular among IT enthusiasts due to its promising benefits. It is a method that enables image recognition, natural language processing(NLP), and voice recognition all to take place. Have you ever wondered how machines function like the human brain? <!--more-->
Introduction
Well, that is thanks to the concept of Deep Learning. Deep Learning is a potent tool, and once you read this article, you will have a better understanding of what DL is and how it works.
What is Deep Learning?
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Deep Learning is a subset of machine learning(ML), DL learns features and tasks directly from data such as images, text, or sound.
Machine learning is a subset of artificial intelligence (AI) that allows computer programs to learn data and predict accurate outcomes without being programmed to do so. ML is applied in image recognition, speech recognition, and fraud detection, to name a few. Machine learning takes a statistical approach to obtain patterns from data.
AI is the capability of machines and computers to mimic human intelligence and, behavior. AI is accomplished by studying how the human brain operates while trying to solve a problem.
Deep Learning is a machine learning technique that automatically extracts the useful pieces of information or makes decisions using neural networks.
How Neural Networks work
Neural Networks are algorithms inspired by the human brain. Neural Networks are systems of hardware and software patterned after neuron operations in the human mind.
Neural Networks learn models, identify patterns, and arrange non-identical kinds of information while trying to imitate the human brain.
Neural Networks are made up of an:
- Input Layer
- Hidden Layer
- Output Layer
The input layer receives input data. Hidden layers perform mathematical computations on the information. Finally, the output layer gives the result. The problem is solved based on calculations from the distributive weights of all layers.
The input layer is where the neural network acquires data. Feature extractors such as Scale Invariant Feature Transform (SIFT) and Speeded up Robust Features (SURF) vectors are used in data classification. Each input neuron represents a single feature.
When the neurons have data, it is redirected to other neurons in the next layer (hidden layer). There are many hidden layers. The hidden layers learn the mapping function between the input and the output. The mapping function can be thought of as the intelligence that, once learned, can be used to perform the task.
Neurons in the hidden layer then send data to neurons in the output layer. Theoretically speaking, the goal of each neural network is defined by the loss function.
The output layer predicts the outcome. Neural Networks work with numbers and math. Math is the key to neural networks, artificial intelligence, and its techniques; i.e. Machine Learning and Deep Learning.
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How Deep Learning works
Deep Learning algorithms are inspired by how the human brain functions. Neural Networks are made up of layers and nodes, similar to how the brain is comprised of neurons.
Deep Learning systems require the following:
- Loads of data.
- Computational power to process tons of data.
Deep Learning algorithms strive to reason the same as humans by studying data with a particular logical framework.
Why we need Deep Learning
Deep Learning is revolutionizing so many industries by solving large and complex data problems. It helps many organizations and developers create exciting AI applications.
Think of successful surgeries, causing little or no deaths due to surgical errors. Think of a world with no accident due to smart cars. In several years to come, the world will be on another level thanks to Deep Learning.
Applications of Deep Learning
There is massive excitement about artificial intelligence and its subsets. Here are a few Deep Learning applications that will govern the world.
- Self-driving cars
Companies such as Google are building driver assistance services. They are also teaching computers how to use digital sensors. In the automotive sector, researchers and developers are working diligently on deep learning-based techniques for self-driving cars.
- Natural Language Processing
Machines are taught to understand the complexities associated with languages and semantics. To achieve this, NLP through Deep Learning plays a significant role. NLP also catch linguistic nuances and frame appropriate responses.
- Healthcare
Deep Learning is completely revolutionizing the healthcare and the medical industries. AI has enabled healthcare and medical industries to advance tremendously.
Clinical researchers use DL to find a cure for untreatable diseases. DL helps with a speedy diagnostic of dangerous conditions. Many cancer tests, such as the Pap test and Mammograms, use DL to examine cell images under a microscope.
- Virtual assistants
Siri and Google Assistants are approved deep learning virtual assistants. Deep Learning enables virtual assistants to learn and understand commands given by a user. Virtual assistants then execute by providing the appropriate answer naturally.
Virtual assistants use Deep Learning to learn about the user, from what the user searches most.
- Fraud detection
The banking and financial sectors are benefiting from Deep Learning to detect transaction fraud. Autoencoders in Tensorflow are being used to catch credit card fraud, thus saving a lot of money from fraudsters. Fraud prevention is done by recognizing patterns in customer transactions.
- Image recognition
Image Recognition using Deep Learning aims to recognize and learn content in images. Deep Learning also seeks to understand (gather data from) the surrounding in the image. Image Recognition is used in the gaming industry and within social media.
- Entertainment
Entertainment companies such as Netflix recommends to their viewers what they need to watch. Deep Learning enables the entertainment industry to understand consumer's behaviors. Applying DL to the entertainment industry provides an exciting experience to clients.
Deep Learning is revolutionizing the filmmaking process. Cameras can learn body language and conduct voice synthesis. Deep Learning can also help to emulate someone's voice in virtual characters.
Machine Learning vs. Deep Learning
Deep Learning and machine learning are both types (or subsets) of AI that classify data and train models. Below we will compare a few tasks or features of each.
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Machine Learning algorithms require extensive data, pre-processing, and manual feature extraction. While Deep Learning relies on its layers of neural networks and performs feature extraction automatically.
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Machine Learning can be used when there is a lack of computational power available or a small dataset. As opposed to Deep Learning's performance that improves with the increased size of a dataset. That is why deep learning algorithms are fed petabytes of data, which can require weeks of training.
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Machine Learning algorithms can perform well on low-end machines. While Deep Learning performs better on a powerful machine equipped with multiple GPUs, providing higher performance.
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Although different Deep Learning is an essential part of Machine Learning. It provides solutions to many problems within image recognition, speech recognition, and NLP. Deep Learning performs better with images, text, and sound recognition when compared to Machine Learning alone.
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Be it ML or DL; we need to label all data for classification type tasks. Classification tasks require labeled data.
To get a more detailed understanding of the differences between ML, DL, and AI, take a look at this article.
Conclusion
Deep Learning is an extensible and a complex field. It functions with the help of Neural Networks to mimic human behavior. DL solves complex data problems. Problems that present themselves in pattern recognition, image recognition, speech recognition, and NLP.
This helps save people time as they do not have to perform repetitive actions or tasks. DL is being applied in many industries like healthcare, entertainment, financial sectors, etc. It is being used to solve different problems and reduce the risk of human error on many repetitive tasks.
Peer Review Contributions by: Lalithnarayan C
