If you're new to deep learning or just curious, you'll find plenty of job openings in the next few years. In recent years, deep learning has become a huge trend. Startups to the world's largest corporations are all jumping on the bandwagon. In this post, we'll go through some of the most popular deep learning algorithms so you can decide which one is right for your particular situation and workload.
If you are new to deep learning or someone who wants to get into the world of deep learning, the future is filled with opportunities. Deep learning has gained immense popularity in recent years. Right from startups to top MNCs, all are rushing towards this field.
It seems chaotic for anyone who wants to start from scratch with deep learning and understand how it works. Here, in this article, we’ll discuss the most widely-used deep learning algorithms you should know and help you understand which algorithm you should use in each specific case.
Deep learning is a subset of machine learning involved with algorithms inspired by the working of the human brain called artificial neural networks. Neural networks are composed of multiple layers that drive deep learning. With the advancements of big data analytics, deep learning represents a truly disruptive digital technology and the power of neural networks has reached heights allowing computers to learn and respond to complex situations faster than humans.
Models in deep learning are trained using a large set of labeled data and neural network architectures containing many layers. Deep neural networks can execute any kind of complex tasks that abstract and represent specific to text, image, and sound. Deep learning has aided language translation, image classification, and speech recognition.
The term “deep” represents the number of hidden layers in the neural network. While deep networks can have as many as 150 layers.
Neural networks consist of layers of nodes, similar to the human brain made of neurons. These neurons are grouped into three different types of layers such as the input layer, hidden layer, and output layer. The input layer will receive input data, hidden layers are used to perform mathematical computations on the inputs, and the output layer returns the output data. In artificial neural networks, the nodes within individual layers are connected to the next layers. As a single neuron receives thousands of signals from other neurons in the human brain, the signal will travel between the nodes and assign the following weights in the artificial neural networks. The heavier weighted node will exert more effect on the next layer of nodes. The final layer compiles the weighted inputs to produce an output.
Deep learning systems require huge amounts of data to provide accurate results. When data is processed, then neural networks will classify that data based on the series of binary true or false questions comprising highly complex mathematical calculations. Deep learning models make use of several algorithms to perform specific tasks. Having a clear understanding of algorithms that drive this cutting edge technology will fortify your neural network knowledge and make you feel comfortable to build on more complex models.
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Feedforward neural network
Generative Adversarial Networks (GAN)
Convolutional Neural Network (CNN)
Recurrent Neural Networks (RNN)
Multilayer Perceptron Neural Network (MLPNN)
Long Short-Term Memory (LSTM)
Deep Belief Networks and Restricted Boltzmann Machines
Recursive Neural Network
Backpropagation is the most fundamental building block in a neural network, effectively used to train a neural network through a method called chain rule. It’s one of the most popular supervised learning algorithms for training feedforward networks for supervised learning.
Let’s understand how backpropagation works as well as its importance.
Backpropagation evaluates the derivative expression between each layer from left to right (backward). Commonly, we feed the network with the data and generated output will be compared with a relevant one using a loss function. We’ll try to readjust the weights based on the difference and repeat this using a non-linear optimization technique called stochastic gradient descent.
For example, if you want to identify the images with a tree. With any type of images, we can feed the network and it generates the output. Since you know that the image actually has a tree or not, you can compare the output with the truth and adjust the network. As more images are passed, the network will make few mistakes and as an unknown image you feed, and it tells us if the image contains a tree. Some of the use cases of Backpropagation are speech and image recognition, which improves the accuracy of predictions in machine learning and data mining.
Can be used to train deep neural networks and works well in error-prone projects.
It allows you to know how the points of error contribute to weights.
Feedforward neural networks are fully connected, which means each neuron in a layer will be connected to all the other neurons in the next layers and the structure is defined as a multilayer perceptron(which is able to learn non-linear relationships between the data).
These are extremely well on tasks like regression and classification. These models are defined as feedforward because the data enters the input and passes through the network layer by layer until it arrives at the output.
Represents more complex functions easily.
Computation speed is very high
AutoEncoders are often used as an unsupervised algorithm through which we leverage neural networks for the task of representation learning. It is most widely used to perform tasks in dimensionality reduction and compression. Particularly, we’ll design a neural network architecture such that we inflict the bottleneck in the network which forces a compressed knowledge representation of the original input.
Generally, it consists of an encoder and decoder, the encoder is used for receiving input and encodes it in a latent space of a lower dimension, whereas decoder is used for taking back that vector and decodes back to the original input. Through that, we can extract the middle of the network representation of the input with fewer dimensions.
These are referred to as direct distribution neural networks as they restore the input signal at the output. They are designed to accurately copy the input to the output. Some of their developments like variational auto-encoder (VAE) and its combination with competing generative networks (GAN) present us with intriguing results.
Present a resultant model primarily based on the data rather than predefined filters.
Easier to train them
Generative Adversarial Networks are mostly used as an unsupervised learning algorithm. The network automatically determines the patterns and regularities in the input data by providing a training set, so it can understand to generate new data. It can mimic any data essentially with small variations.
A generative adversarial network (GAN) consists of two parts, such as generator and discriminator. A generator is used for generating plausible data and its instances become negative training examples for
the discriminator. Discriminator distinguishes the generator fake data from real data and also penalizes for creating implausible results. They run repeatedly, making them more and more robust with each repetition. Some of the use cases of GAN include astronomical images, video games, interior design, Natural language processing, Health diagnostics, cybersecurity, and speech processing.
GANs allow efficient training of classifiers in a semi-supervised manner
Do not allow any deterministic bias unlike variational autoencoders
Captures and copies variations within a given data set, generates images from a given data set of images, creates high-quality data, and manipulates data.
Convolutional neural networks are successful in identifying objects, images, and traffic signs. It is a feedforward and a multi-layer neural network that uses perceptrons for supervised learning and to analyze data. The concept behind them is to connect neurons with only the respective fields, instead of connecting each neuron with all the next ones.
In a way to avoid overfitting, they regularize feedforward networks and make them better in recognizing the spatial relationships between the data. The CNN architecture is somewhat different from other neural networks. To better understand, consider images as data. Through computer vision, the images will be interpreted as two-dimensional matrices of numbers.
Some of the use cases of CNN include Image processing, Medical image analysis, Natural language-processing tasks, Video recognition, Pattern recognition, Recommendation engines, and more.
Very good for visual recognition
Efficient at recognition and highly adaptable.
Easy to train because of fewer training parameters, and is scalable when coupled with backpropagation.
Recurrent neural networks are used for recognizing the data set’s sequential attribute and use patterns for predicting the next likely scenario. This network is perfect for time-related data and used in time series forecasting. To train the network along with a backpropagation algorithm, stochastic gradient descent (SGD) is used.
In RNN, the hidden layers are used for preserving sequential data from the previous steps. That means the output from the previous step is passed as input to a current step, using the same bias and weighs repeatedly for prediction purposes. The layers are joined to create a single recurrent layer and these feedback loops process sequential data, letting data to persist, as in memory, and inform the final output.
Recurrent neural networks are layered for processing information in two directions such as feed-forward to process data from initial input to final output and feedback loops using backpropagation to loop information back into the network.
Some of the use cases of RNN include Sentiment classification, Natural language processing, Image captioning, Speech recognition, Video classification, and more.
RNN shares the same parameters across all steps and this is great to reduce the number of parameters that we need to learn
It can be used along with CNN's to produce accurate descriptions for unlabeled images.
The multilayer perceptron is a feed-forward supervised learning algorithm, which is used up to two hidden layers to produce outputs from a given set of inputs. As the name implies, it teams up with other perceptrons stacked in various layers to solve complicated tasks.
The below-mentioned diagram shows an MLP with three layers. The individual perceptron in the first layer on the left side (the input layer) sends outputs for all the perceptrons in the second layer (the hidden layer), and all the perceptrons from the second layer send outputs for the final layer on the right side (the output layer).
For each signal, the perceptron uses different weights. The model is trained to self learn the dependencies or correlation between input and output from a training set. Tuning the weights and biases reduces the errors at the output layers. It is repeated for hidden layers going backward. Backpropagation is used for adjusting the biases relative to the error.
Some of the use cases of MLPNN are Data classification, Image verification and reconstruction, Machine translation, Speech recognition, and E-commerce (where many parameters are involved).
Classifies non-linearly separable data points
Solves complex problems including various parameters, and manages data sets through a large number of features, specifically non-linear ones.
LSTM networks (Long Short-Term Memory) are a type of RNN used to master order dependence in sequence prediction problems. Patterns can be stored in memory for more extended periods, with the ability to selectively delete or recall data.
Backpropagation is used in this algorithm but using memory blocks masters the sequence data that are connected into layers instead of neurons. While the data is processed through layers, architecture can add, modify, or delete the data as needed.
LSTMs are mostly used in machine translation, language translation, sentiment analysis, speech recognition, and more.
Best suited for classification and prediction based on time series data
Offers sophisticated results for diverse problems.
A deep belief network is an unsupervised probabilistic algorithm. It’s a blend of directed and undirected graphical networks, involving the lower layers directed downwards and top layer undirected RBM. Restricted Boltzmann Machine is a stochastic neural network containing a visible layer, hidden layer, and a bias unit. Visible unit is connected to all the hidden units and the bias unit is connected to all the visible units and all the hidden units.
Various RBMs can be stacked to arrange a Deep Belief Network. They resemble exactly like Fully Connected layers but differ in the way they are trained. DBNs are useful for Image and face recognition, Motion-capture data, Video-sequence recognition, Classifying high-resolution satellite image data
Offers energy-based learning
Useful for probabilistic as well as non-probabilistic statistical models
Recursive Neural Networks are non-linear adaptive models that are able to learn deep structured information. They are another type of RNN but differ in structure, they are in a tree-like form structure. So that, hierarchical structures in the training can be modeled. It can operate on any hierarchical tree structure. Traditionally, it is used in NLP in applications like Audio to text transcription, sentiment analysis, and more.
Have the potential of capturing long-distance dependencies
Parsing is slow and domain-dependent
In this article, we have covered an overview of major deep learning algorithms that you should get familiar with. If you have any questions on deep learning algorithms, please feel free to share them with us through comments.
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Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .
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