Deep Learning is a machine learning technique that capitalizes on different layers of non-linear information processing for unsupervised and supervised transformation, feature extraction, classification, and pattern analysis.
When it comes to information processing in a non-linear manner, it consists of different hierarchical layers. Here, some low-level concepts are capable of defining higher-level concepts. Supervised learning is a form of machine learning a training set, and a set of examples are submitted as input in the system during the phase of training.
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As each input is labeled with an output value, the system knows the output when a set of inputs is provided. On the other hand, in unsupervised learning, the inputs are not labelled with the class to which it belongs. Hence, it is up to the system to develop and organize data by searching for common characteristics and making the necessary changes based on internal knowledge.
Artificial neural networks are shallow. Thus, they cannot deal with complex data as found in day-to-day applications like images, natural speech, information retrieval, and human-like information processing applications. For these kinds of applications, deep learning models are perfect. With the help of deep learning, it is possible to classify, recognize, and categorize data patterns for a machine with less effort.
Related Article: Top Deep Learning Techniques
Deep Learning Models Types
Deep learning models have been evolving, and most of them are based on artificial neural networks. The most significant among them is the convolutional neural networks (CNNs). It is also inclusive of latent variables and propositional formulas organized layer-wise in deep generative models.
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It is an artificial neural network, which can learn from different coding patterns. An autoencoder is similar to a multilayer perceptron having an input layer, hidden layer, or an output layer in its simplest form. The output layer possesses the same number of nodes as an output layer. It does not predict target values based on the output vector; instead, the Autoencoder can predict its input. This learning mechanism can be outlined as follows:
For each input x,
- Compute activation by letting a feedforward pass at every hidden layer and output layers
- Make use of appropriate error functions for detecting deviation among the calculated values
- Update weights by back-propagating the error
- Keep repeating the task until it generates satisfactory output
If the hidden layer has fewer nodes than the input-output nodes, then the last hidden layer’s activation is thought of as a compressed representation of the inputs. If the hidden layer nodes are greater in number, an autoencoder will learn the identity function and prove useless in most cases.
Deep Belief Net
It provides a solution to handling local minima and non-convex objective functions possessing typical multilayer perceptron. You can think of it as an alternate type of deep learning that consists of multiple layers of latent variables interconnected with other layers. It is a restricted version of Boltzmann machines.
Here, each sub network’s hidden layers will serve as the visible input layer for the network’s adjacent layer. Thus, it makes the lowest visible layer a training set for the adjacent layer of the network. Hence, every layer of the network can be trained greedily and independently. Each layer of the deep structure utilizes hidden variables as observed variables for training each layer of the deep structure. The algorithm for training a deep belief network is as follows:
- Take into consideration input vectors
- Use the input vector for training a Boltzmann machine and obtain the weight matrix
- Use the weight matrix for training two lower layers of the network
- Use the network RBM for generating a new input vector through mean activation and sampling of the hidden units.
- Keep repeating the procedure until you reach the top two layers of the network.
Also Read: Deep Learning vs Neural Networks
Convolutional Neural Networks (CNN)
It is another variant of the multilayer perceptron based on feedforward. It organizes individual neurons in a way such that they respond to all overlapping regions in the visual area. It is one of the deep learning algorithms capable of taking an input image and assigning importance to learnable biases and weights of various aspects/objects in the image.
It can differentiate one from the other. The need for pre-processing in CNN is quite low when compared with other classification algorithms. CNN possesses the ability to learn these characteristics and filters.
CNN’s are one of the main categories for the following:
Face recognition etc.
These are some of the handful of areas where CNN can be widely used.
For image classification, CNN will accept an input image, process it, and do the classification under different categories. Computers visualize input images as an array of pixels, and it is a variable of image resolution. Technically, CNN models will subject each input image through several convolutional layers with filters for training and testing.
The first layer is known as Convolution, which is assigned to extract features from an input image. Convolution can preserve relationships between pixels as it can learn image features through the use of small squares of input data. It executes a mathematical operation by taking two inputs: an image matrix and a filter or kernel.
When an image convolution is provided with different filters, it will become capable of performing operations like edge detection, sharpening, and blur through filters.
In the past few years, the area of computer vision has witnessed considerable progress. One of the biggest advancements is CNN. Deep CNNs have evolved to become the most fancied computer vision applications used in gesture recognition, self-driving cars, auto-tagging friends in pictures posted to Facebook, facial security features, and automated number plate recognition.
Recurrent Neural Networks
It is a type of neural network where the previous step’s output can be fed as input to the current step. Inputs and outputs in a conventional neural network are independent of one another. However, in cases where there is a need to predict the successive words in a sentence, there will be a need for remembering the previous words.
The emergence of RNN promises to solve this issue with the help of a hidden layer. One of the key features of RNN is the Hidden State capable of remembering some information in a sequence.
RNN is equipped with a memory that can remember all information about the calculations. It can use the same parameters for every input for performing the same tasks on all the inputs or hidden layers for producing the desired output. It will greatly reduce the complexity of parameters, which is in sharp contrast to other neural networks.
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Calculation of gradients is dependent not only on the current step but also on the previous step. There is a variant called a bidirectional recurrent neural network that is used by several applications. Here, the network takes into consideration the previous and expected future output. By introducing multiple hidden layers, deep learning can be achieved in two-way straightforward recurrent neural networks.
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