Deep Learning

We specialize in Deep Learning and Machine Learning development. We build Artificial Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks for predictive models. We also work on developing Artificial Intelligence Chatbots.

Outsource your Deep Learning Coding project to us

ComtechRIM Artificial Neural Network (ANNs)
Artificial Neural Networks (ANNs)

Artificial neural networks are some of the primary tools that facilitate machine learning. These networks have the term “neural” because these are brain-inspired systems which are intended to replicate the way a human’s brain processes information. The networks consist of input and output layers and also an additional hidden layer in most cases. The hidden layer comprises of units that transform the input into something that the output layer can process. These tools are designed for pattern recognition which is otherwise deemed too complicated for human programmers to extract and program the machine to recognize.

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Neural networks are also called perceptrons and have been around since the 1940s. They have become a significant part of artificial intelligence in only the last few decades. This is because of the arrival of a new technique known as backpropagation, which allows networks to rearrange their hidden layers of neurons in situations where the outcome does not match the creator’s expectations. Example: In a network which is designed to identify dogs and misidentifies cats.

The latest advancement has come about in the form of deep learning neural networks which comprise multiple layers of multi-layer systems which extract different features until it can identify what it is looking for.

Artificial Neural Networks (ANNs)

An artificial neural network is a computing system made up of multiple highly interconnected processing elements. These elements process information through their dynamic state response to external inputs.

Structure of Artificial Neural Networks

ANNs are designed on the fundamental belief that the human brain functions by making the right connections and the networks are designed to imitate such action using silicon and wires that function as living neurons and dendrites.

The human brain comprises 86 billion nerve cells, and these cells are called neurons. Each of these cells is connected to a thousand cells by Axons. Dendrites accept information in the form of stimuli from the external environment or inputs from sensory organs. These inputs create electric impulses which travel through the neural network very fast. A neuron passes on the message to other neurons to handle the information or may not pass it on if it chooses to do so.

Artificial Neural Network’s comprise of multiple nodes which imitate the functioning of the brain. The neurons are connected through links and interact with one another. The nodes have the potential to accept input data and process simple information only to act on the data while performing simple operations. The derived result is then passed on to other neurons. The information which is passed on is the output of each node and is called its activation or node value.

Types of artificial neural networks

Artificial neural networks exist in two forms namely FeedForward and Feedback.

  1. FeedForward ANN

In this form, each unit sends information to other units from which it does not receive any response. There are no feedback loops in this form. Feedforward ANN is used for generation, recognition, and classification. So, they have fixed inputs and outputs.

  1. FeedBack ANN

This form of ANN allows for feedback loops, and they are used in content addressable memories.

At IT Infrastructure Management, we offer Supervised Learning ANN networks that facilitate pattern recognition.

So, if you are interested in upgrading your organization’s framework with our specialized Artificial Network Services, then give us a call right now!

Convolutional Neuran Networks (CNNs)

Convolutional Neural Networks have application in recommender systems, natural language processing, image and video recognition as well. Just like neural networks, CNNs are made up of neurons with learnable weights and biases. Every neuron receives several inputs, takes a weighted sum over them and passes it on through an activation function. It then responds with an output.

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A CNN network is a class of deep learning, feed-forward neural network.  Such a system uses multi-layer perceptrons which are designed to require minimal pre-processing. They are also known as space invariant artificial neural networks (SIANN). They are based on the principle of shared weight architecture and translation invariance characteristics.

Just like artificial neural networks, even convolutional networks imitate the biological of the human visual cortex. The connectivity pattern between neurons resembles the organization of the human visual cortex. External stimuli make the individual cortical neurons respond only in a restricted region of the visual field known as the receptive field. The receptive fields of the neurons partially overlap in a way that they cover the entire visual field.

In comparison to most of the other image classification algorithms, a CNN network uses minimal pre-processing. This is possible because the network learns the filters that are otherwise hand-engineered in traditional algorithms. The independence from prior knowledge and human effort in the design of features turn out to be a major advantage.


A convolutional neural network consists of input, output, and multiple hidden layers. The hidden layers comprise of convolutional layers, pooling layers, fully connected layers and normalization layers.


A convolutional layer applies a convolution operation to the input. It then passes the result to the next layer. The convolution emulates the response of an individual neuron to visual stimuli. Each convolutional neuron processes data only for its receptive field.

Fully connected feed-forward neural networks can be used for learning features and classifying data, but it is not deemed practical to apply this architecture to images.


CNN networks may include  pooling layers that are either local or global in nature. These layers combine the outputs of neuron clusters at one layer into a single neuron in the next layer.

Fully connected

In such a layer, each neuron is connected to its corresponding neuron from one layer to another. The principle is the same as the multi-layer perceptron neural network in terms of principle.


Convolutional Neural Networks share weights in convolutional layers. It means that the same filter is used for each receptive field within the same layer. As a mechanism, it dramatically reduces memory footprint and enhances performance.

At IT Infrastructure Management, we possess in-depth knowledge in the field of Convolutional Neural Network and have programmers who are highly skilled in integrating such a network with your organizational framework.

If you are interested in implementing a CNN network, then we are just a phone call away!

ComtechRIM Convolutional Neural Networks (CNNs)
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Recurrent Neural Networks (RNNs)

The human brain does not start thinking from scratch every second an external stimulus excites thought in the brain. If you read a book, you tend to understand each word based on your understanding of previous words. All your past experiences, knowledge and memories do not get deleted the moment you go to sleep. This is how you do not have to start from scratch when you wake up every morning. Your thoughts have persistence.

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Traditional neural networks cannot do this. For example, say you choose to classify what kind of incident is occurring at every stage of a movie. It would seem unclear as to how a traditional neural network could use its reasoning power to determine which later incidents are classified as what type based on its reasoning about previous events. This is where recurrent neural networks address such an issue. RNN networks are designed with a loop mechanism which allows information to persist.

Recurrent neural networks are closely related to sequences and lists. They are the architecture of the neural network to use for such data. This is why they are highly used. The last few years have shown great success in the application of RNNs to a variety of issues. Matters such as speech recognition translation, image captioning and many more of such nature can be easily tackled with the recurrent neural network.

Recurrent Neural Network

The convolutional neural network has its own set of limitations simply because its API is very constrained.

Recurrent neural networks are much more flexible because they allow for operation over sequences of vectors which are sequences in the input, output or both.

RNN Computation

Recurrent neural networks accept an input vector and give an output vector but what makes it different is that the output vector’s contents are influenced not just by the input you feed in but also by all contributions that you have supplied in the past.

Fixed networks are doomed from the get-go because of their fixed number of computational steps. The recurrent sequence regime of operation is much more powerful and is also more appealing to those of us who aspire to build more intelligent systems. A recurrent network can be perceived as a running fixed program. It comprises specific inputs and includes certain internal variables. So, it can be said that RNNs essentially describe programs.

It is a well-known fact that RNNs are Turing-Complete. They have the potential to simulate arbitrary programs using proper weights. If convolutional training network is perceived as the optimization over functions then training recurrent networks can be perceived as the optimization over programs.

Powerful models that learn to process sequentially can be formulated and trained with recurrent neural networks.

So, are you looking to implement Recurrent Neural Network? Call us right now!