Top 10 Deep Learning Algorithms
“I think people need to understand that deep learning is making a lot of things, behind-the-scenes, much better. Deep Learning is already working in Google search, and in image search; it allows you to image search a term like “hug”.’ Geoffrey Hinton
Amazing Fact:
“Electronics giant Panasonic has been working with research centers and universities to develop deep learning techniques that are related to computer vision”
The median salary of a Deep Learning Engineer is around USD 178,482, as per Indeed.com. For example, the facial recognition market is anticipated to grow from USD 3.2 billion to USD 7 billion in 2024; which cements the demand for deep learning experts.
As a result, Deep Learning experts are highly in demand and clearly conveys that a Deep Learning course can help you make a career in this ever-growing domain.
Table of Contents
What is Deep Learning?
Deep Learning is a subset or form of Machine Learning that mimics the working of the human brain in processing data and creating patterns to be used in making decisions. Deep Learning consists of networks that are capable of learning unsupervised from unstructured or unlabeled data.
The human brain activities that deep learning mimics may include speech recognition, language translations, detecting objects, and making decisions. Deep Learning AI is capable of learning without any human intervention, drawing conclusions from data that is unstructured.
The most important application of Deep Learning is it can help you detect fraud or money laundering. Deep Learning is used across all the industries for various applications.
Few examples of Deep Learning include medical research tools that can identify the possibility of reusing drugs for different diseases, commercial applications that use image recognition, and open-source platforms that are available with consumer recommendation features.
Let us now look at some of the popular Deep Learning algorithms.
Popular Deep Learning Algorithms
Deep Learning Algorithms train machines by investigating or learning from examples. These algorithms feature self-learning representations and rely on ANNs or artificial neural networks that mirror the way the brain processes information.
All the algorithms are designed to perform specific tasks and you need to have a clear understanding of these so as to decide which algorithm is to be applied according to the situation.
They are:
1. Convolutional Neural Networks
CNN or Convolutional Neural Networks or ConvNets, are mainly designed for processing images and object detection. It consists of multiple layers that are intended to process and extract features from data.
The first layer is the Convolutional layer having several layers for performing the convolution operation.
The second layer is the Rectified Layer Unit or ReLU which performs operations on elements giving a rectified feature map as an output.
Next comes the Pooling layer which is fed by a rectified feature map. Pooling is a down-sampling operation intended to reduce the dimensions of the map. It converts the 2-D arrays into a single, continuous, long linear vector by flattening it.
When this flattened matrix is fed as an input from the pooling layer, a fully connected layer is formed, which is capable of classifying and identifying the images.
2. Long Short Term Memory Networks(LSTMs)
These are the type of recurrent neural networks that are able to learn and memorize long-term dependencies. Their default behavior is to recall past information for long periods.
LSTMs can retain information over time and remember previous inputs, so it is ideal for time-series prediction. There are four layers in LSTM that communicate in a unique way, forming a chain-like structure.
Their working involves the following steps:
First, the irrelevant parts of the previous state are forgotten.
Second, they update the cell-state values selectively.
Finally, certain parts of the cell-state are given as output.
3. Recurrent Neural Networks (RNNs)
RNNs consist of connections that form directed cycles allowing the outputs from the LSTM such that it can be fed as input to the current phase.
The input is the output of LSTM that can memorize previous inputs due to its internal memory. RNNs are commonly used for time-series analysis, natural language processing, image captioning, handwriting recognition, and machine translation.
The main feature of RNN is that it can process inputs of any length.
4. Generative Adversarial Networks(GANs)
GANs are deep learning networks that are generative and create new data instances resembling the training data. There are two components in GAN: a generator to learn how to generate fake data, and a discriminator to learn from that false information.
GANs are used to improve celestial images and simulate gravitational lensing to be used in dark-matter research.
5. Random Forest
Random Forest or Random Decision Forest is constructed from many layers, but it is constructed from decision-trees and gives a statistical average of the predictions of individual trees as an output. Bagging or bootstrap aggregation is performed over individual trees and random subsets of the features are considered as the randomized aspects of the Random Forest algorithm.
6. Radial Basis Function Networks(RBFNs)
RBFNs are special types of neural networks where radial basis functions are used as activation functions. There are three layers in RBFNs namely an input layer, a hidden layer, and an output layer. They are mainly used in classification, time-series prediction, and regression. The classification performed by RBFN includes measuring the similarity of input to examples from the training set.
7. Self Organizing Maps(SOMs)
This algorithm enables data visualization to minimize the dimensions of data through self-organizing artificial neural networks. When humans find it difficult to easily visualize high-dimensional data, data visualization techniques serve the purpose. Here, SOMs are created to help you understand the information depicted in high-dimensional data.
8. Multilayer Perceptrons (MLPs)
If you wish to learn deep learning technology, MLPs are an ideal place. In MLPs, there are multiple layers of perceptrons with activation functions and come from the class of feedforward neural networks. It consists of a fully connected input layer and an output layer. The number of input and output layers is the same in MLPs, but the number of hidden layers may vary. It is generally used for building image-recognition, speech-recognition, and machine-translation software.
9. Autoencoders
An autoencoder, designed by Geoffrey Hinton, is a specific type of artificial neural network which is used to learn efficient data codings in an unsupervised manner. Typically, they are a feedforward neural network having identical input and output. The main function of an autoencoder is to replicate the data from the input layer to the output layer. Autoencoders are generally used for image processing, popularity prediction, and pharmaceutical discovery.
1. Deep Belief Networks
Deep Belief Network is a generative graphical model that is composed of multiple layers of talent variables having binary values which are often called hidden units. The layers in DBNs are connected to each other, but connections are not present between the units within each layer. DBNs are a stack of restricted Boltzmann Machines or RBMs.
DBNs are generally used for video-recognition, image-recognition, and motion-capture data.
Conclusion
With the evolution of Deep Learning in recent years, deep learning algorithms are gaining popularity across numerous industries. To make a career in this domain, gaining certification is the best way.
To get certified with trouble-free learning, there are online training providers to help you out. They give you a choice of learning at your own pace and train you according to the level of your knowledge, with the mode of learning preferred by you.
Get yourself registered now!