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Deep Learning

Deep learning has become somewhat of a buzzword in the tech community. We always seem to hear about it in the news relating to AI and yet most people don’t actually know what it is! In this article, we will give you a broad idea about it. Deep learning has been introduced with the objective of moving machine learning one step closer to one of its original goals “Artificial Intelligence”.

Concept of Deep Learning

Deep learning is a sub-field of machine learning dealing with algorithms inspired by the structure and function of the brain called artificial neural networks. In deep learning, the tasks are broken down and distributed into machine learning algorithms that are organized in consecutive layers. Together these layers constitute the artificial neural networks. A typical deep learning model will have at least three layers. Each layer accepts the information from previous and pass it on to the next one.

Actually, the concept of deep learning is not new. It’s on hype nowadays because earlier we did not have that much processing power and a bulk of data.  Advancement of quantum mechanics and cloud computing in the last two decades had taken deep learning and machine learning into the real picture.

Before moving further just remember the words of Howard Rheingold

“The neural network is this kind of technology that is not an algorithm, it is a network that has weights on it, and you can adjust the weights so that it learns. You teach it through trials.”

Simply the above words clearly explains deep learning. Given a large data set of input and output pairs, deep learning algorithm will try to minimize the difference between its prediction and expected output. By doing this, it tries to learn the relation between given inputs and outputs. This in turn allows a deep learning model to generalize inputs that it has never seen before. There are several types of deep learning algorithms. Some of them are:

Multi-Layer Perceptron

A multi-layer perceptron has three or more layers. The input layer, hidden layer and output layer and can have multiple hidden layers. It is used to classify data that cannot be separated linearly. It is a type of artificial neural network that is fully connected. This is because every single node in a layer is connected to each node in the following layer.

Convolutional Neural network

A Convolutional Neural Network(CNN) uses a variation of the multi-layer perceptrons. A CNN contains one or more than one convolutional layers. These layers can either be completely interconnected or pooled. Before passing the result to the next layer, the convolutional layer uses a convolutional operation on the input. Due to this ability, convolutional neural networks show very effective results in image and video recognition, natural language processing, and recommender systems.

Recurrent Neural network

A Recurrent Neural Network is a type of artificial neural network in which the output of a particular layer is saved and fed back to the input. This helps predict the outcome of the layer.

Computer Vision in Deep Learning

Computer vision is an important sub field of machine learning in which lot of research is going on. These deep learning models are trained on a set of training data processed from images, videos, etc. to solve a task. Such rigorous training of this perception model helps it to see and visualize like a human. Data labeling is an inevitable part in this training procedure. Images/videos need to be annotated in order for the machine to train and understand the algorithm and interact efficiently. Annotating images has become a tedious task for the AI based companies. We act as a solution for this problem. For the last 3 years we have annotated large set of images with high-quality and  efficiency. We have helped lot of companies by doing all sort of annotation works like bounding box annotation, key point annotation, cuboid annotation, polygonal/contour annotation and poly line annotation.

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