Table of Contents
Introduction
Deep learning is one of the subtypes of machine learning and relies on artificial neural networks for carrying out complex computations on data. Deep learning is structured on the pattern of the human brain and is designed to solve complex problems in a short span of time. Neural networks are the central functioning point of deep learning models and are organized in the form of three important layers. The first layer is the input layer, the second layer is the hidden layer and the third layer is the output layer.
Applications of deep learning
Deep learning is becoming extremely important for companies that are functioning in the domains of healthcare, education, logistics, entertainment, e-commerce, and advertising. Professionals who are trained in deep learning are the most sought-after technicians given their rising demand in numerous sectors. Engineering students and even other professionals are now looking for deep learning training and certification to make a prosperous career in the above-mentioned domains.
As compared to other career options, jobs that require deep learning certification are relatively less saturated. On the other hand, some of the highest-paid profiles are the ones that require knowledge of deep learning.
Let us first understand the working of deep learning algorithms in much more detail.
Working of deep learning algorithms
Deep learning algorithms function with the help of self-learning representations which means that they replicate the working of artificial neural networks to solve a particular problem. They can be thought of as mirror images of the neurons that function within the human brain and allow the computation of complex problems. Some of the most important deep learning algorithms include convolutional neural networks and recurrent neural networks.
Generative adversarial networks are also widely used deep learning algorithms. In addition to this, multi-layer perceptrons, autoencoders, and self-organizing maps are other types of deep learning algorithms.
Convolution neural networks
Convolution neural networks were first used for the recognition of characters like zip codes and other simple digits. With the passage of time, the application domain of convolutional neural networks became much more enlarged and they were used in satellite imaging and medical processing. Other prominent applications of such networks include time series forecasting and the detection of anomalies in large data sets.
Convolution neural networks function with the help of a convolution layer that has various types of filters in addition to a pooling layer. The main operation that is performed by the pooling layer is a simple down-sampling operation that helps in the reduction of dimensions to make the problem simpler. The fully connected layer takes the input from the pooling layer and helps in the classification of images.
Recurrent neural networks
Recurrent neural networks function by memorizing inputs from the previous phase and utilizing it in the operational phase by virtue of their internal memory. Since these networks have coherent connections, they are named as such. The most important applications of these algorithms are in the domain of natural language processing as well as machine translation.
At a technical level, recurrent neural networks rely on historic information for the purpose of processing and the advantage of this algorithm is that the model size does not increase even if the input size increases.
Generative adversarial networks
Generative adversarial networks are also important types of deep learning networks that function with the help of two important components. The first component is a generator which helps in the generation of fake data sets. The second component which is called the discriminator is dependent upon the generator because it begins to distinguish and learn to produce output from this fake information.
The application of generative adversarial networks is found in astronomical sciences where they are used to simulate gravitational lensing. This is extremely important for research and development in the domain of dark matter. In addition to this, these networks are also used for the generation of cartoon characters or the creation of photographs or other three-dimensional objects.
Self-organizing maps
Self-organizing maps are other types of deep learning algorithms which are mostly used in data visualization techniques. These maps help in the examination of high-dimensional data which can be easily processed for further operations. These algorithms help in assigning weights to each node and choosing a vector for the purpose of training data. The most likely input vector which is selected is called the best matching unit.
Other important neural networks
Other important neural networks include deep belief networks and restricted Boltzmann machines. Different types of deep neural networks are used in different types of technical processes in various industries for deriving solutions to problems in a short span of time. This has allowed us to achieve operational efficiency as well. As such, the applications of deep learning technology will continue to increase in the coming times.
