You know that artificial neural networks emulate the working principles of the human brain to recognize patterns, objects, images, speech, and videos. The concept of deep neural networks has been extant for quite a few decades. The emergence of big data and superior computational power has helped scientists turn the possibilities of creating an intelligent machine with human-like cognitive faculties into reality.
Today, neural networks are used in autonomous vehicles to learn from the decisions made by human drivers. We encounter deep learning in facial recognition and speech to text transformation. Computer vision which is a function of deep learning is regularly used in various sectors for automatic surveillance, quality assurance and other purposes. Our goal here is to look inside the head of an AI and understand how it recognizes images. You will get a comprehensive understanding of these processes while acquiring your deep learning certification. Consider this article as a primer of sorts.
Human image perception
The process of visual perception is shared by three different components. The first one is the light reflected by an object. The second one is the eye that captures the light and sends an optical signal and the third one is the optical cortex in the brain that processes image. We recognize a certain object because of our previous experience of seeing that object supported by millions of years of evolutionary baggage. We do this with little conscious effort. However, things are a bit different for machines.
Machine perception
An algorithm is unable to process optical data directly – it has to convert the information into structured numeric figures. A large database of previously labelled training data helps the machines to learn to recognize the images. Let us break the process of machine perception with the help of neural networks down into some simple steps.
Step 1. The neural network divides the image presented to it in groups of pixels. These small groups of pixels are called filters.
Step 2. Values representing the intensity of a pixel in the colour spectrum are assigned to each filter. These values are initially randomized.
Step 3. The convolutional neural networks compare these values to different specific patterns of pixels to finally achieve an accurate prediction – the object.
This way it can detect animate and inanimate objects, poses, signs, handwritten language, of course, faces.
The error function
Training a neural network is quite a challenge and it requires massive amounts of labeled data. The neural network tries to predict the significance of the image it is exposed to. Initially the results make little sense. But with every iteration the network uses an error function to understand how close its prediction got to the actual label of the image. The more data it analyzes the better it gets at predicting the identity of the pixel patterns. A well trained CNN can very well challenge human perception.
Video analysis is a whole different ball game
Videos pose a different kind of challenge to neural nets. A video is moving frames of images. Now even if a CNN narrows in on each frame and identifies the objects in it separately it cannot understand the contextual shifts between two image frames. For instance, if it is exposed to a video where a box is being unpacked and each frame is labeled as packing or unpacking the CNN would not be able to tell the difference because it cannot account for temporal movement.
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The Recurrent Neural Networks
The problem regarding video analysis can be solved with an advanced form of neural nets, the recurrent neural networks. RNNs can not only process information but also retain it and use it to drive future decision making. This allows an RNN to understand the context behind a video and thereby analyse it.
The CNNs and RNNs are used across various industry verticals through the deployment of computer vision. The field of computer vision has come up as one of the key areas of AI innovation around the world. It is important to understand that similar technologies can also be applied for speech recognition and text analysis. As whole deep neural networks have pushed our technology right into the future.
Training a neural network, cleaning data for it, seeing through the error patterns to perfect the programmes, all take exceptional dexterity and a deep learning certification helps you put yourself in an acceptable position. The applications of these technologies are ever-increasing and now is the time to evolve with them.
