Deep Learning and it’s Applications

Deep Learning and it’s Applications

What is Deep Learning?


Deep Learning is a sub-field of machine learning that deals with algorithms inspired by brain structure and function called artificial neural networks.

Deep learning algorithms seek to exploit the unknown structure in the input distribution in order to discover useful representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features

In Deep Learning, each level learns how to turn its input data into more abstract representation, and more specifically, a deep learning algorithm can determine which features to optimally place the level on its own, without human intervention.

Deep Learning is applicable for supervised and unsupervised learning tasks.

Most deep learning methods use neural network architectures, which is why deep learning models are often referred to as deep neural networks. The term “deep” usually refers to the number of hidden layers in the neural network. Traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150.


Artificial Neural Network vs. Machine Learning vs. Deep Learning


Consider the following definitions to understand deep learning vs. machine learning vs. AI:

Deep learning is a subset of machine learning that depends on artificial neural networks

Machine learning is a subset of artificial intelligence that uses techniques (such as deep learning) that enable machines to use the experience to improve at tasks. The learning process is based on the following steps:

Artificial intelligence (AI) is a technique that enables computers to mimic human intelligence. It includes machine learning.

Deep Learning Neural Network is a sophisticated method of neural networks. Unlike a primary neural network, the Deep Learning Neural Network has more than one hidden layer.

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Deep Learning Vs. Machine Learning

The following table compares the two techniques in more detail:

Machine Learning Deep Learning
Number of data points Can make predictions using small amounts of data. Needs to use large amounts of training data to make predictions.
Dependencies of Hardware It can work on low-end machines. There is no need for a large amount of computational power. It depends on high-end machines. It inherently does a large number of matrix multiplication operations. A GPU can efficiently optimize these operations.
Features Processing It requires features that need to be precisely identified and created by users. Learn high-level data features and create new features on their own.
Learning approach It divides the learning process into smaller phases. Then, from each step, it combines the results into one output. Moves through the learning cycle by the end-to-end solution of the problem.
Execution time It takes relatively little time to train, from a few seconds to a couple of hours. It usually takes a long time to train, since several layers are involved in a deep learning algorithm.
Output The output is usually a numerical value, like a score or a classification. The output is typically a numerical attribute, such as a score or a classification.
High dimensional data It is not capable of handling high dimensional data that is where input & output is quite large. It is capable of handling high dimensional data that is where input & output is quite large.

Applications of Deep Learning

Deep learning has been applied to hundreds of issues, ranging from computer vision to natural language processing. It is widely used in both academies to study intelligence and industry to build intelligent systems to assist people in different tasks or jobs. There are a number of applications for deep learning that are given below:

1.Virtual Assistants

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A virtual assistant is an application that can understand voice commands and complete tasks for a user. Virtual assistants are available on most smartphones and tablets, traditional computers, and, now, even standalone devices like the Amazon Echo and Google Home. Deep learning is used by virtual assistants or online service providers to understand our language and voice better when people communicate with them.

2.Language Translations DLA 5

Deep learning algorithms can automatically translate between languages. It can be used in Machine Translation (MT) is a sub-field of computational linguistics that focuses on translating text from one language to another.

3.Autonomous / Self Driving Cars

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Automated driving is becoming one of the most emerging topics nowadays. Various companies are applying deep learning technologies to create an automatic vehicle that doesn’t require human supervision to function. They are automatically detecting objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents.

4.Image colorizationDLA 7

Image colorization is the problem of adding color to black and white photographs. Deep learning can be used to use the objects and their context within the picture to color the image, much like a human operator might approach the problem.

5. Facial Recognition

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Deep learning is also used in face recognition not only for security purposes but for tagged the people on Facebook posts. It is a problem of identifying and verifying people in the photograph by their faces. Face recognition is the process comprised of detection, alignment, and feature extraction.

6. Aerospace and Defense

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Deep learning is used in various defence sectors to identify any unauthorized or can be used to locate areas of interest by using given environmental aspects. Using this information, we can give commands or orders to troops, whether it is safe or unsafe to work in the environment.

7. Speech recognitionDLA 10

Speech recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format.

8. Image / Object Classification and Detection

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Another popular area regarding deep learning is image recognition. It aims to recognize and identify people and objects in images as well as to understand the content and context. Image recognition is already being used in several sectors like gaming, social media, retail, tourism, etc.

9. Medical DiagnosisDLA 12

Deep learning is assisting medical professionals and researchers in discovering the hidden opportunities in data and in serving the healthcare industry better. Deep Learning in healthcare provides doctors the analysis of any disease accurately and helps them treat them better, thus resulting in better medical decisions.

Advantages of Deep Learning

There are several advantages of deep learning which are given below:

  • The deep learning does not require feature extraction manually, and it takes images directly as an input.
  • The performance of deep learning algorithms is improved when the amount of data increased. There are some advantages of deep learning which are given below:
  • The architecture of deep learning is flexible to be modified by new problems in the future.
  • Deep learning is a powerful tool to make a prediction.
  • Deep learning specializes in pattern discovery (unsupervised learning) and knowledge-based prediction.
  • Deep learning can outperform traditional methods. For instance, deep learning algorithms are 41% more accurate than machine learning algorithms in image classification, 27 % more precise in facial recognition, and 25% in voice recognition.

Disadvantages of Deep Learning

There are several disadvantages of deep learning which are given below:

  • It requires a massive amount of data in deep learning to perform better than other techniques.
  • It requires high-performance GPUs and lots of data for processing
  • It requires expensive GPUs and hundreds of machines, and this increases the cost of the user.
  • There is no standard theory to guide you in selecting the right deep learning tools. This technology requires knowledge of topology, training methods, and other parameters. As a result, it is not simple to be implemented by less skilled people.
  • The deep learning is very costly to train the complex data models.