What is Deep Learning? How it works? What are its applications?

Deep learning is a subset of machine learning in which machine tries to imitate the behavior of the human brain. It tries to simulate the brain working mechanism. Deep learning makes wide use of artificial neural networks that learn from a large volume of data. As we human learn from experience deep learning algorithm also performs task multiple times to learn from them each time tweaking some details. Deep learning algorithms are arranged in a hierarchy of increasing abstraction and complexity and try to draw similar conclusions as the human brain will draw in real-world problems.
Deep Learning

To demonstrate deep learning, let's take the example of babies. When a baby first sees a dog it does not know that it is a dog and s/he asks their mother. Then the mother explains to it that it is a dog. After that, the baby looks at the dog and studies its structure, and the next time, it can identify it as the dog. What the baby does unknowingly is that it creates a hierarchy created with knowledge gained from its mother to clarify the complex abstraction of the concept of dog. Similarly, the deep learning algorithms learn from the training dataset and use that knowledge in real-world problems.
For such accuracy deep learning makes use of a multilayer algorithm called neural networks. Neural networks can be trained to identify patterns and classify different types of problems. The multiple layers of a neural network can also be seen as filters that lead to the identification of correct output. It can be used for a wide range of problems like classification, clustering, regression, etc. Artificial Neural Network has the unique ability to solve problems that a general machine learning algorithm cannot solve. All the big advancements in the field of technology these days are due to deep learning. Deep Learning brought a new revolution in the field of technology.
We human beings generate more than 2.6 quintillion bytes of data each day and these data can be used to predict the future. These resources are the must-have for deep learning in order to predict the future. Deep Learning algorithms require a large volume of data and this increase in data production is the reason behind the growth of deep learning. Thus, deep learning allows its users to solve complex problems using even very diverse and unstructured data.

How it Works?

Architecture of Neural Networks


Almost all deep learning algorithms make use of neural networks which is why they are often called deep neural networks. The term "deep" in deep learning refers to a number of hidden layers in the neural networks. Deep Learning can have more than 100 hidden neural networks. Deep Learning makes use of the large volume of the dataset to learn. Unlike Machine learning, There is no need for manual feature extraction in deep learning. That is feature extraction is automatically performed by deep learning. One of the most popular type of neural network is Convolutional Neural Network(CNN or ConvNet). Neural Networks extract features directly from images. 
Automatic Feature Extraction in Deep Learning


The relevant feature in an image is not trained earlier instead they are captured during the use of neural network. This automatic feature extraction feature helps deep learning to achieve high accuracy in computer vision related problems. Every hidden layer in neural network increases the complexity of learned image features. The general working of image classification is as shown in the figure below:
Working of Neural Networks



Popular Applications of Deep Learning

  • Industrial Automation

Deep learning is being used to create a safer work environment by suggesting workers when they are in an unsafe environment around heavy machines as well as the time of task completion etc.
  • Customer Experience
Chatbots and other services are already being used in various customer service applications that employ deep learning as their core. And as it continues to grow it is expected to be used in a wide range of areas for customer satisfaction and experience.

  • Aerospace and Military
Deep learning is being used in satellites to recognize the safe and unsafe zones for troops and also to identify danger and possible disasters.

  • Text Generation

Machine are now learning language and grammar. One of the most popular examples is Grammarly in this case.

  • Image Modification

Deep learning is able to colorize images by itself as well as make image super-resolution. This application can be very useful in the future.

  • Medical Research

Deep learning is being widely used to identify diseases via pictures and human behavior. It is also being used in the treatment of cancer.

  • Computer Vision

Deep learning has improved the field of computer vision by a large amount making computer vision more accurate in object detection and movement analysis.

  • Self Driving Cars

Self-driving cars would not have been possible without the help of deep learning.

  • Customer Segmentation

Deep learning has made it easier to classify customers so that to provide features that they are more likely to like. The most common example is movie suggestions on Netflix.

  • etc.
We will be implementing our first CNN in our next article. For practical implementation of deep learning please check this article here.

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