Top 10 Deep Learning Algorithms to Watch 1. Autoencoder 2. Restricted Boltzmann machine 3. Self-organizing mapping 4. Multilayer Perceptron 5. Deep Belief Network 6. Radial basis function networks 7. Generate adversarial networks 8. Recurrent Neural Networks 9. Convolutional Neural Networks 10. Summary of long short-term memory networks

Predicting the future is not magic, but artificial intelligence. Needless to say, AI is in the limelight, and everyone is talking about it, whether they understand the term or not.

According to researchers and analysts, the use of digital assistants is expected to reach 8.4 billion by 2024. Hyper-personalization, chatbots, predictive behavior analytics, and more are the most common use cases in AI applications. Artificial intelligence is revolutionizing the entire planet, leading us toward an unpredictable future. Among them, the two most important concepts are machine learning and deep learning.

Of the 300 billion emails sent every day, machine learning is efficient enough to detect spam. However, in recent years, deep learning has been widely welcomed for its high accuracy, effectiveness, efficiency, and ability to process massive amounts of data. It is a branch of machine learning that presents the entire world as an ingrained hierarchy of concepts through learning, each of which is identified as simple, with tremendous flexibility and power.

With the application of artificial neural networks, deep learning algorithms train machines to perform complex calculations on large amounts of data. Deep learning algorithms allow machines to work and process data like the human brain, and are highly dependent on artificial neural networks and work based on the structure-function of the human brain. Here are the top ten deep learning algorithms to watch, I hope to give you a reference.

<h1 class="pgc-h-arrow-right" data-track="5" >1. Autoencoder</h1>

As some type of feed-forward neural network, an autoencoder is a deep learning algorithm in which both the inputs and outputs are the same. It was designed by Geoffrey Hinton in 1980 to solve the problem of unsupervised learning. It has a neural network that is trained to transfer data from the input layer to the output layer. Some important use cases for autoencoders are: image processing, drug recycling, and population forecasting.

Here are the three main components of an autoencoder:

Encoder

Code

Decoder

Restricted Boltzmann Machines (RBMs) are stochastic neural networks that learn from probability distributions rather than input sets. This deep learning algorithm was developed by Geoffrey Hinton for topic modeling, feature learning, collaborative filtering, regression, classification, and dimensionality reduction.

Restricted Boltzmann machines work in two stages:

Forward pass

Backward pass

In addition, it consists of two layers:

Hidden units

Visible units

Each visible layer is connected to all existing hidden layers. Restricted Boltzmann machines also have an offset layer. The layer is connected to all the hidden layers as well as the visible layer, but there are no output nodes.

Self-Organizing Maps (SOM) visualizes data through self-organizing artificial neural networks to reduce the dimensionality of the data. This deep learning algorithm was developed by Professor Teuvo Kohonen. Data visualization can solve this kind of problem that humans are not easy to visualize when working with high-dimensional data. The purpose of self-organizing mappings is to better understand high-dimensional information.

The best place to start learning deep learning algorithms is multilayer Perceptions (MLP). It falls under the umbrella of feed-forward neural networks, while also having many perceptual layers that contain activation functions. It consists of two fully connected layers:

Input layer

Output layer

A multilayer perceptron contains the same number of input and output layers, and it is possible to have various hidden layers. Some important use cases for multilayer perceptrons include image recognition, face recognition, and machine translation software.

Generative models, deep belief networks (DBNs), have a large number of latent and random variable layers. Latent variables are often referred to as hidden layers and contain binary values. These are the stacks of Boltzmann machines, with connections between the layers. Each deep belief network layer is connected to subsequent and previous layers. Use cases for deep belief networks include video recognition, image recognition, and motion capture data.

Radial Basis Function Network (RBFN) is a special class of feed-forward neural networks that utilize radial basis functions as activation functions. It contains the following layers:

Hidden layer

The radial basis function networks of the above layers are used for regression, classification, and time series prediction.

The Generative Adversarial Network (GAN) is a deep learning algorithm that creates new data instances similar to the training data. Generative adversarial networks help generate realistic pictures, cartoon characters, image creation of faces, and rendering of 3D objects. Video game developers use generative adversarial networks to boost low resolutions through image training.

There are two important components to building an adversarial network:

Generator: Ability to generate fake data.

Discriminator: Ability to learn from false information.

Recurrent Neural Networks (RNNs) consist of connections that help form directed loops, allowing the output of a Long Short-term Memory Network (LSTM) to be provided as inputs at this stage. Recurrent neural networks are able to remember previous inputs because it has internal memories. Some common use cases for recurrent neural networks are handwriting recognition, machine translation, natural language processing, time series analysis, and image description.

<h1 class="pgc-h-arrow-right" data-track="42" >9. Convolutional neural networks</h1>

Convolutional Neural Networks (CNNs), also known as ConvoNets, contain many layers that are primarily used for object detection and image processing. The first convolutional neural network was developed and deployed by Yann LeCun in 1988. In that year, it was called LeNet, which was used for character recognition such as numbers, postal codes, etc. Some important use cases for convolutional neural networks include medical image processing, satellite image recognition, time series forecasting, and anomaly detection.

Here are some of the key layers of convolutional neural networks that play a pivotal role in data processing and extracting features from data:

Convolutional layer

Linear commutation

Pooling layer

Fully connected layers

Long Short-term Memory Networks (LSTM) are a class of recurrent neural networks capable of learning and remembering long-term dependencies. Long-term short-term memory networks are also able to recall information from the past over the long term. It retains information over time, which proves beneficial in time series forecasting. It has a chain structure in which 4 interacting layers connect and communicate uniquely. In addition to time series forecasting, long-short-term memory networks are also used in drug development, music creation, and speech recognition.

<h1 class="pgc-h-arrow-right" data-track="51" > summary</h1>

In recent years, deep learning algorithms and technologies have become popular mainly because of their ability to process large amounts of data and then turn it into information. Using its implicit layer architecture, deep learning techniques learn to define low-level categories, such as letters; then medium-level categories, such as words; and then high-level categories, such as sentences. According to some predictions, deep learning is bound to revolutionize supply chain automation.

Andrew Ng, a former chief scientist at Baidu and one of the prominent leaders of Google's Brain project, affirmed:

Similar to deep learning, rocket engines are deep learning models, and fuel is the massive amount of data we can provide to these algorithms. （“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. ”）

Therefore, the development and progress of technology will never stop, and the same is true of deep learning techniques and algorithms. To remain competitive in this ever-changing world, everyone must keep up with the latest technological advances.

About the Author:

Aliha Tanveer is a technical writer based at ArhamSoft.

Original link:

https://dzone.com/articles/10-crucial-deep-learning-algorithms-to-keep-an-eye