Deep learning is a small part of the machine learning domain. Machine learning is the capacity of machines to understand and react like humans. The use of deep learning and machine learning is ‘prediction‘. The different models are used for image classification, object recognition, and classification. First, understand what is deep learning.
What is Deep Learning?
Deep learning is a sub-branch of machine learning used for ‘predictions’. The deep learning domain is based on the concept of the human brain. The neural structure inside humans works properly like algorithms. The artificial neural structure is used to understand the model and predict the results. This prediction is based on different learnings like supervised, unsupervised, or semi-supervised. Deep learning is a technology where the machine reacts humanly. Let’s see the example to understand the deep learning domain.
Example of Deep Learning
The best example to understand the working of deep learning nowadays is automated vehicles. The self-driving cars use deep learning technology to understand the routes and take decisions without human interruptions. Different models are developed for this process. For example, models for obstacle detection, stop sign detection, pedestrian detection, lamppost detection, and potholes detection are used here. When all these models work together, then the self-driving car can work properly.
All these models are based on deep learning, so understanding deep learning in detail is very necessary. The base of every deep learning algorithm is an artificial neural network. First, understand the artificial neural network.
One-Artificial Neural Network
Every day we work on different tasks and this is only possible due to neurons. This neuron is a proper system that helps humans to respond correctly. This neuron can be replicated in the form of a coding system in deep learning. In deep learning, this system is called Artificial Neural Network.
The Input Layer consists of all types of data collected during model processing. It actually contains all the information required to build the model. All input layers contain the information. The middle layer is a Hidden layer. The hidden layer contains different functions to process the data from the input layer. These functions are the Transfer function and Activation function. The third layer/last layer is the Output layer. The output layer contains the result.
The transfer function is the main function that is used to convert the input data into the desired form(i.e. Output). In this transform function, we can convert single input into a single output (SISO) or multiple inputs into multiple outputs (MIMO). we can also convert this function as multiple input and single output function (MISO). we can take an example of a regression problem to understand this concept.
An activation function is a kind of validation function in an Artificial neural system (ANS). If the value is greater than the threshold value, the input function will be processed by the hidden layer. Otherwise, the function will be not forwarded to the hidden layer for processing.
Two-Deep Learning Algorithms
10 different types of deep learning algorithms are very popular and mostly used. These algorithms are Autoencoders, Convolution Neural Networks, Deep Belief Networks, Generative Adversarial Networks, Long Short Term Memory Networks, Multilayer Perceptron, Radial Basis Function Networks, Recurrent Neural Networks, Restricted Boltzmann Machine, Self Organizing Maps. Let’s see all these algorithms in detail.
The autoencoders in a type of deep learning algorithm in which the input and output are the same. For example, this algorithm is mainly used in image processing techniques. The blurred image can be converted into a clear image using this algorithm. The image contains the same data only in a clear format. There are different sections as per ANS in this algorithm. The encoder, hidden layer, and decoder layer.
The encoder layer will take the image as input and passes it to the hidden layer and helps to process the input data. The middle layer is a hidden layer that will apply transform and activation functions on the input data. The last layer which is the decoder layer helps to convert and process the final output. Hence, using this autoencoder one can replicate the clear image.
Three-Convolution Neural Network (CNN)
The most popular algorithm in deep learning is a Convolution Neural Network (CNN) which is used in many classification problems. There are many hidden layers in this algorithm that are used to classify the objects according to the training data. The first layer is an input layer where all type of data is collected and forwarded to the next layer for feature extraction. The feature extraction layer is the layer where the features of input data are collected to perform the predictions.
The first layer in the feature extraction layer is the convolution layer. This convolution layer performs the convolution operation on the data. Convolution is nothing but merging and extracting information from the provided data. It produces a feature map. the second layer is the Rectified Linear Unit (ReLU) layer which provides rectified feature map. The third layer is a pooling layer which is used to reduce the dimension of the rectified feature map. The flattening process is done in this layer. The next one is the fully connected layer which is the output layer and helps to classify the objects, and images using the flattening map provided by the pooling layer.
Deep Belief Networks
The deep belief networks algorithm depends upon the greedy algorithm. The layer-by-layer approach is used in deep belief networks. The deep belief network contains various layers which are in the form of hidden layers. a greedy algorithm is used to train the layers of deep belief networks using a top-down approach. Gibbs sampling is done on the top two layers of the hidden layer. The data will be processed by every single layer of the deep belief network.
Let’s see the details of deep belief networks using visualization.
Four-Generative Adversarial Network(GANs)
This algorithm is mainly about learning through fake data. In this algorithm, the original data is copied and new fake data is generated through the data which is already present as training data. This kind of algorithm is very useful when the amount or quality of data is not appropriate. For example, models based on astronomical objects and their photographs.
This GAN model contains different sections like Generator and Discriminator. Both sections help to create the whole system.
Five-Long Short Term Memory Networks (LSTMs)
The long short-term memory network is a kind of model that is used to remember the past data/model. The inputs are remembered after some time interval in this algorithm. So, this is a very important algorithm when we work on models like speech recognition, medical history, and disease detection which require data history.
In this image, consider X1 and X2 is a memorable part and Y1 and Y2 is not important for future models.
Six-Multilayer Perceptron’s (MLPs)
The Multilayer Perceptron (MLP) contains different layers in between the Input and Output layers. This is mainly used in classification problems in deep learning networks. The classification problems like image recognition and speech recognition. Multiple layers contain different activation functions. The below image shows the details structure of the model.
Seven-Radial Basis Function Networks (RBFNs)
In this deep learning algorithm, the RBFNs are a form of all three basic layers: Input, Hidden, and Output structure. Various problems like Regression, Classification, and Image recognition are solved using this model structure. The input vector is present which is required to collect the features/ data points from the datasets. These input vectors are processed by RBF neurons to find the output. The weighted sum of the inputs from the input vector is considered in this algorithm.
Eight-Recurrent Neural Networks (RNNs)
This RNNs algorithm is the extension/ updated version of Long Short Term Memory Networks (LSTMs). The processed output of the LSTMs can be fed as an input to the model of RNNs. This model is a cyclic structure. Considering the X is the current model the previous model will be X-1 and the next is X+1. We can feed all these models to this algorithm. This model learns from previous models and predicts results accordingly. Let’s see the visualization.
Nine-Restricted Boltzmann Machines (RBMs)
Geoffrey Hinton developed this algorithm. This algorithm is based on the probability distribution of the input sets provided. This deep-learning algorithm is used for different problems which are based on regression, classification, and feature learning. It consists of two layers Visible units and hidden units. These units are interconnected with each other. RBMs convert inputs into numbers and pass output again to the next hidden layers.
Ten-Self Organizing Maps (SOMs)
Professor Teuvo Kohonen developed this algorithm. This algorithm is based on the RGB value of the inputs provided. This algorithm takes input as vectors, then converts it into 2D RGB values, and then classifies it according to classes or similar features. This algorithm is mainly used in data visualization models, Image classifications.
In this article, a brief introduction to deep learning and its algorithm is given. The 10 most popular algorithms are explained in this article. All algorithm has their features and is used to solve various problems. Hope you enjoy this article.
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