10 Crucial Deep Learning Algorithms to Keep an Eye on in 2021
Deep learning algorithms train machines to perform sophisticated computations on an immense amount of data. Here is the list of top 10 deep learning algorithms for 2021.
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Predicting the future is not magic; it’s artificial intelligence. Without a doubt, artificial intelligence is all the rage and everyone is talking about it, whether they understand this term or not.
According to researchers and analysts, digital assistant utilization is expected to strike a figure of 8.4 billion by the year 2024. Some of the most prominent use-cases of artificial intelligence include hyper-personalization, chatbots, predictive behavior analysis, and much more. Artificial intelligence is revolutionizing the whole globe and is leading us towards an unpredictable future. The establishment of understanding about the latest technological advancements in artificial intelligence can seem to be overwhelming; however, the two most significant concepts are machine learning and deep learning.
Machine learning is efficient enough to detect spam out of 300 billion emails sent every single day. Lately, however, deep learning is attaining tremendous popularity because of its high rate of accuracy, effectiveness, efficiency, and capability to process the stupendous amount of data. It is a branch of machine learning that holds great flexibility and power by learning to present the globe as an ingrained hierarchy of concepts, with each concept determined to be simple.
With the utilization of artificial neural networks, deep learning algorithms train machines to perform sophisticated computations on an immense amount of data. Deep learning algorithms make the machines capable of performing work and process data the way human brains do. Deep learning algorithms highly depend upon artificial neural networks and work based on the structure-function of the human brain. Here is a list of top 10 deep learning algorithms everyone must be familiar with in this evolving big data era.
A certain type of feedforward neural network, autoencoder is a deep learning algorithm in which both inputs and outputs are identical. It was designed by Geoffrey Hinton in the year 1980 with the purpose to solve unsupervised learning problems. It possesses trained neural networks that transfer data from the input layer to the output layer. Some important use cases of autoencoders are: image processing, pharmaceutical recovery, and population prediction.
Following are the three major components of an autoencoder:
2. Restricted Boltzmann Machines
Restricted Boltzmann Machines (RBMs) are stochastic neural networks that are capable of learning from a probability distribution instead of a set of input. This deep learning algorithm was developed by Geoffrey Hinton and is used for topic modeling, feature learning, collaborative filtering, regression, classification, and dimensionality reduction.
RBMs work in two phases:
Also, it consists of two layers:
Every visible unit is connected to all of the existing hidden units. RBMs also have a bias unit. That unit is connected to all of the hidden units as well as the visible units without having output nodes.
3. Self-Organizing Maps
Self-organizing maps (SOMs) enable data visualization for the reduction of data’s dimensions via self-organizing artificial neural networks. This deep learning algorithm was developed by Professor Teuvo Kohonen. Data visualization is capable of solving such problems that are not easy for humans to visualize while dealing with high-dimensional data. The purpose of the development of SOMs is to develop a better understanding of high-dimensional information.
4. Multilayer Perceptions
The best place to initiate learning about deep learning algorithms is multilayer perceptions (MLPs). It belongs to the category of feedforward neural networks, along with numerous layers of perceptions containing activation functions. It consists of two layers that are completely connected:
MLPs contain the same number of input layers and output layers with the probability of having various hidden layers. Some important use-cases of MLPs include image recognition, face recognition, and machine translation software.
5. Deep Belief Network
The generative models, deep belief networks (DBNs) possess numerous layers of latent and stochastic variables. Latent variables are usually called hidden units and contain binary values. These are the stack of Boltzmann machines with connections among layers. Every RBM layer connects with both subsequent and previous layers. Use cases of DBNs include video recognition, image recognition as well as motion capture data.
6. Radial Basis Function Network
Radial basis function network (RBFNs) is a special category of feedforward neural networks that utilize radial basis functions as activation functions. It contains the following layers:
The above-mentioned layers of RBFNs are utilized for regression, classification, and time series prediction.
7. Generative Adversarial Networks
Generative adversarial networks (GANs) are the type of deep learning algorithms that create new data instances that are similar to the training data. GANs help in the generation of realistic pictures, cartoon characters, image creation of human faces, and 3D objects rendering. GANs are utilized by video game developers to upscale low resolution through image training.
GANs has two important components:
Generator: it is capable of generating fake data
Discriminator: it is capable of learning from false information.
8. Recurrent Neural Networks
Recurrent neural networks (RNNs) consist of connections that help to form directed cycles that permit the output from the long short-term memory networks (LSTMs) to be provided as inputs in the present phase. An RNN is capable of memorizing previous inputs because of its internal memory. Some common use-cases of RNNs are: handwriting recognition, machine translation, natural language processing, time-series analysis, and image captioning.
9. Convolutional Neural Networks
Convolutional neural networks (CNNs) also called ConvoNets, contain numerous layers that are majorly utilized for object detection and image processing. The first CNN was developed and deployed by Yann LeCun in 1988. Back in that year, it was known as LeNet and was utilized for character recognition such as digits, ZIP codes, etc. Some significant use-cases of CNNs include medical image processing, satellite image identification, time series forecasting, and anomaly detection.
Following are some crucial layers of CNN that play a pivotal role in data processing, as well as feature extraction from data:
Rectified linear unit (ReLU)
Fully connected layer
10. Long Short-Term Memory Networks
Long short-term memory networks (LSTMs) are a category of Recurrent Neural networks (RNN) that are capable of learning and memorizing long-term dependencies. An LSTM is also capable of recalling past information for long periods. It retains information over time, which proves to be beneficial in time series prediction. It has a chain-like structure where 4 interacting layers connect and communicate uniquely. Along with time series prediction, LSTM is also utilized for pharmaceutical development, music composition, and speech recognition.
There would be no wrong in saying that you can have data without information, but you cannot have information without data. The major reason why deep learning algorithms and techniques are popular these days is because of their capability to process tremendous amounts of data and then convert that into information. Via its hidden layer architecture, the deep learning technique learns to define low-level categories such as letters, then mid-level categories such as words, then higher-level categories such as sentences. According to certain predictions, deep learning will surely revolutionize supply chain automation.
The chief scientist of China's most popular search engine Baidu as well as one of the prominent leaders of the Google brain project, Andrew Ng, affirmed that,
“The analogy to deep learning is that the deep learning models are the rocket engines and the immense amount of data is the fuel to those rocket engines. ”
Hence, technological evolutions and advancements are never going to stop, nor are the deep learning techniques and algorithms. Everyone must stay up-to-date with the latest technological progressions to stay competitive in this ever-evolving world.
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