In this article, we examine the concept and nature of deep learning. In the following article, we will learn about different types of Deep Learning methods.

Deep Learning is a class of machine learning techniques that uses multiple layers of non-linear information processing for supervised or unsupervised feature extraction and transformation for pattern analysis and classification. It contains many hierarchical layers to process information in a non-linear manner where some lower-level concepts help define higher-level concepts. ​

Deep learning followed artificial intelligence. This learning has come with the help of artificial intelligence to respond to human needs and desires more naturally.

Superficial neural networks are unable to handle a significant amount of complex data, which is evident in many common applications such as natural speech, images, information retrieval, and other human-like information processing applications. Deep learning is suitable for such applications. With deep learning, a machine can recognize, classify, and classify patterns in data with relatively less effort. Deep learning provides human-like multi-layer processing compared to shallow architecture. The main idea of deep learning is to use hierarchical processing using many layers of architecture. Architectural layers are arranged hierarchically.

After some training introductions, the input of each layer goes to the adjacent layer. Deep learning follows a distributed approach to handling big data. This method assumes that data is generated by considering multiple factors, different times, and different levels. Deep learning facilitates the ordering and processing of data in different layers according to its time, scale, or nature. Deep learning is often associated with artificial neural networks. In the following section, three categories of deep learning are examined.

  • Generative
  • Discriminatory
  • Hybrid deep learning architectures

Architects from general categories focus on pre-training a layer in an unsupervised way. This approach eliminates the problem of training lower-level architectures that rely on previous layers. Each layer can be pre-trained and later incorporated into the model for general tuning and further learning. Doing so solves the problem of training a neural network architecture with multiple layers and enables deep learning. ​

The neural network architecture may have the ability of discriminative processing by stacking the output of each layer with the original data or with different information combinations, thus forming a deep learning architecture. Descriptive models often consider neural network outputs as a conditional distribution over all possible label sequences for a given input sequence, which will be further optimized through an objective function. The hybrid architecture combines the features of generative and differentiating architecture. Typically, one can perform deep learning as follows. ​

Construct a network consisting of an input layer and a hidden layer with necessary nodes. Add another hidden layer on top of the previously learned network to create a new network. Restore the network. Repeat adding more layers and retraining the network after each addition. Repeat adding more layers and reevaluate the mesh after each addition.

Different types of deep learning models


An AutoCAD is an artificial neural network capable of learning different coding patterns. The simple form of the encoder is just like the multilayer perceptron, which consists of an input layer or a hidden layer, or an output layer. A significant difference between the conventional multilayer perceptron neural network and the autoencoder is in the number of nodes in the output layer. In encoder mode, the output layer contains the same number of nodes as the input layer. Instead of predicting target values according to the output vector, the autoencoder must predict its inputs. The outline of the learning mechanism is as follows. ​

  • Calculation of activation functions provided in all hidden layers and output layers
  • Find the deviation between the calculated values and the inputs using the appropriate error function
  • Error propagation for updating weights
  • Repeat the output until you get a satisfactory result

If the number of nodes in the hidden layers is less than the number of input/output nodes, then the activity of the last hidden layer is considered a compressed representation of the inputs. When there are more nodes in the hidden layer than in the input layer, an encoder can potentially learn the recognition function and become useless in most cases. ​

Deep Belief Net

A deep belief network is a solution to the problem of controlling non-convex objective functions and local minima when using conventional multilayer perceptron. This alternative type of deep learning involves multiple layers of hidden variables with connections between layers. Deep belief networks can be considered as Restricted Boltzmann Machines (RBM) where each hidden layer of the network acts as an observable input layer for the adjacent layer of the network. It turns the lowest visible layer into a training set for the adjacent layer of the network. In this way, each network layer is trained independently and greedily. Latent variables are used as observed variables to train each layer of deep structure. The training algorithm for such a deep belief network is presented as follows:

Consider an input vector, train a constrained Boltzmann machine using the input vector, and obtain the weight matrix. The lower two layers of the network generate the new input vector weight matrix using the network (RBM) through sampling or average activation of the hidden units. Repeat this process to reach the top two layers of the network. The fine-tuning of the deep belief network is very similar to the multilayer perceptron network.

  Convolutional neural networks

Convolutional neural network (CNN) is another type of multilayer perceptron network. It is a type of neural network in which individual neurons are arranged in such a way that they respond to all overlapping areas in the visual field. Deep CNN works by successively modeling small pieces of information and combining them deeper into the network. The final layers take an input image with all the templates, and the final prediction is like the weighted sum of all of them. Therefore, deep CNNs can model complex changes and behavior and provide highly accurate predictions. ​

Such a network follows the visual mechanism of living organisms. The cells of the visual cortex are sensitive to small areas of the visual field called the receptive field. The subregions are arranged to cover the entire visual area, and the cells act as local filters on the input space. Algorithms are used to train the parameters of each convolution kernel. In addition, each kernel with the same parameters is repeated throughout the image. Some convolutional operators extract unique features of the input. In addition to the convolutional layer, the network includes a single linearly corrected layer, pooling layers to calculate the maximum or average value of a feature in a region of the image, and a loss layer containing application-specific loss functions. Image recognition and image analysis and natural language processing are the main applications of such a neural network. ​

The field of computer vision has witnessed many developments in the past few years. One of the most important developments in this field is CNNs. Now, the deep part of CNNs forms the core of the most sophisticated computer vision applications, such as self-driving cars, gesture recognition, automatic tagging of friends in Facebook pictures, facial security features, and automatic license plate number recognition.

Recurrent Neural Networks

A convolutional model operates on a fixed number of inputs and produces a fixed vector as output with a predefined number of steps. Recursive networks allow us to operate on sequences of vectors at the input and output. In the case of recurrent neural networks, the connection between the units forms a direct cycle. Unlike traditional neural networks, the input and output of recurrent neural networks are not independent but related. In addition, the recurrent neural network shares standard parameters in each layer. One can train a recurrent network like a traditional neural network using the backpropagation method. ​

Here the calculation of the gradient depends not only on the flow step but also on the previous steps. Another type called Bidirectional Recurrent Neural Network is also used for many applications. A bidirectional neural network considers not only the previous output but also the expected future output. In bidirectional and direct recurrent neural networks, deep learning can be achieved by introducing multiple hidden layers. Such deep networks provide higher learning capacity with a large amount of training data. Speech, image processing, and natural language processing are some of the chosen areas where recurrent neural networks can be used. ​

Reinforcement learning in neural networks

Reinforcement learning is a combination of dynamic programming and supervised learning. Typical components of this approach are environment, agent, measures, policy, and cost functions. The agent acts as the controller of the system; the policy determines what actions should be taken and the reward function defines the overall objective of the reinforcement learning problem. An agent that receives the maximum possible reward can be considered the best practice for a given state. ​

Here, an agent refers to an abstract entity, or an object or a subject (autonomous cars, robots, humans, customer support, etc.), that perform actions. The state of an agent refers to its position and state in the abstract environment; For example, a specific location in a virtual reality world, a building, a chess board, or position and speed on a race track. Deep reinforcement learning holds the promise of a highly general learning method that can learn user behavior with very little feedback.

The most important benefits of deep learning

  • Automatic learning of features
  • Learning multiple layers of features
  • High accuracy in results
  • High generalization power and identification of new data
  • Extensive hardware and software support
  • The potential to create more features and applications in the future

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