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In today's day and age of instant image sharing, it's essential to get your tech ready to talk the language of images. While it is easy for our brains to process what an image means, and what it signifies and correlates to, getting a machine to do the same is a complicated task. Computers view images as 2D arrays of numbers to decipher it. If we include colors, then it becomes a 3D array where the last field signifies the RGB value. Their job is to take a regular image as input and provide a classification output, similar to the processes followed by the human brain. This is where convolutional neural networks (CNNs) are born.
This guide to convolutional neural networks talks about how the 3-dimensional convolutional neural network replicate the simple and complex cells of the human brain, including the receptive fields that humans experience through their senses. In this guide to convolutional neural networks, we will first address what CNNs are, their structure and what their biological connection is, and the optimum functionality which can be extracted from them.
What are Convolutional Neural Networks?
Let's start with what CNNs really are. Like the way our brains identify objects when we see a picture, the goal is to get computers to recognize objects in the same manner. However, there exists a huge difference between what a human brain sees when looking at an image or a computer. To a computer, an image is just another array of numbers. Each object has its own pattern and that is what the computer will use to identify an object in an image.
To explain convolutional neural networks in simple terms - Just as parents train their children to understand what a ball is or what food is, similarly, computers are also trained by showing a million images of the same object so that their ability to recognize that object increases with each sample.
The true catching-on of CNNs came with Alex Krizhevsky winning 2012's ImageNet competition wherein he used the networks to drop the image classification error from 26% to 15%. This was a substantial drop and was considered a turning point in the history of digital image classification. Since then, several digital giants have used CNNs in functionalities that will help their business grow such as Google, Amazon, Instagram, Facebook, and Pinterest.
Structure of CNNs
CNNs are structured differently as compared to a regular neural network. In a regular neural network, each layer consists of a set of neurons. Each layer is connected to all neurons in the previous layer. The way convolutional neural networks work is that they have 3-dimensional layers in a width, height, and depth manner. All neurons in a particular layer are not connected to the neurons in the previous layer. Instead, a layer is only connected to a small portion of neurons in the previous layer.
Let's start with the top layer -
The Math Layer
The top layer is perceived as the mathematical layer. It is essentially the convolutional layer and deals with understanding the number pattern it sees. Let's assume the first position in this layer starts applying a filter around the top left corner of the image. The filter is also referred to as a neuron or a kernel. It reads that part of the image and forms a conclusion of an array of numbers, multiplies the array, and deduces a single number out of this process.
This single number represents the top left corner that the convolutional layer has just read of the image. The part of the image that the filter scans over is the receptive field. The filter then moves right by 1 unit and starts the same process again. In this fashion, the convolutional layer reads the entire image and assigns a single number to each unit. This data gets stored in a 3D array. In essentiality, this entire process functions like the human brain. What we are referring to as the receptive field in the world of CNNs is the visual field in the world of human biology. The filter acts as the visual cortex containing small regions of cells targeting reading of specific areas of the visual field.
The Rectified Linear Unit Layer
The next layer encountered is the Rectified Linear Unit Layer (ReLU). This is where the activation functions take place. The activation function is initially set a zero threshold. The activation gradient only functions at 0 and 1 and does not include intermediary gradients like its predecessors. Due to its linear, non-saturating form, it is said that ReLUs greatly aide in the declining gradient of error. However, due to the fragile nature of a ReLU, it is possible to have even 40% of your network dead in a training dataset.
At a higher level, the first layer in a deep convolutional neural network is the convolutional layer, followed by a rectified linear unit, followed by another convolutional layer, and then alternating rectified linear units and pool layers in conjunction with only one more convolutional layer. While the process that the first convolutional layer follows is pretty straightforward, the process gets more complex as we go down layers, as the convolutional layers are no longer dealing with a simple image. They are dealing with the then processed output of the initial mathematics applied at each level.
The Fully Connected Layer
As is with any completed product, its required to have one final layer encompassing all the interior complexities. This layer is the completion layer in a convolutional neural network. It takes the final output of the layer before it (be it a ReLU or a convolutional layer) and provides an N-dimensional vector output. ‘N' here signifies the number of classes the program chooses from. For example, if the program is looking at pictures of horses, it will look at high-level features such as 4 legs, the hooves, or the tail, or muzzle. This fully connected layer will look at the high-level features and connect that with the image thus giving the output of a classification of a horse.
How are CNNs Integrated with Deep Learning to Create World-class Applications?
Companies may find it difficult to integrate convolutional neural networks and neural networks into production-ready applications. There are multiple factors that need to be taken into consideration to make this happen, such as -
What convolutional architecture should be used?
What kind of data and data sets should be used?
Which data model or deep learning model should be used to accommodate the data?
It is advisable to map the major architectures of networks that deep learning offers, with major architectures of CNNs. You could perhaps adopt the strategy of "transfer learning" to build a set of images and then train the selected network architecture in the specified data set. Essentially, for a smooth integration, you must ensure that you follow these steps -
Choose a CNN architecture which is capable of modeling data similar to the data that you want to model. This is to ascertain that it is capable of modeling those desired types of features
Make sure you have a system that allows creating and deploying the model within the limitations of security and data storage
Manage the access to the correct version of each model across several different applications
Top 7 Applications of Convolutional Neural Networks
How to make use of convolutional neural networks? Companies are usually on the lookout for a convolutional neural networks guide, which is especially focused on the applications of CNNs to enrich the lives of people.
Simple applications of CNNs which we can see in everyday life are obvious choices, like facial recognition software, image classification, speech recognition programs, etc. These are terms which we, as laymen, are familiar with, and comprise a major part of our everyday life, especially with image-savvy social media networks like Instagram. Some of the key applications of CNN are listed here -
Decoding Facial Recognition
Facial recognition is broken down by a convolutional neural network into the following major components -
Identifying every face in the picture
Focusing on each face despite external factors, such as light, angle, pose, etc.
Identifying unique features
Comparing all the collected data with already existing data in the database to match a face with a name.
A similar process is followed for scene labeling as well.
Convolutional neural networks can also be used for document analysis. This is not just useful for handwriting analysis, but also has a major stake in recognizers. For a machine to be able to scan an individual's writing, and then compare that to the wide database it has, it must execute almost a million commands a minute. It is said with the use of CNNs and newer models and algorithms, the error rate has been brought down to a minimum of 0.4% at a character level, though it's complete testing is yet to be widely seen.
Historic and Environmental Collections
CNNs are also used for more complex purposes such as natural history collections. These collections act as key players in documenting major parts of history such as biodiversity, evolution, habitat loss, biological invasion, and climate change.
CNNs can be used to play a major role in the fight against climate change, especially in understanding the reasons why we see such drastic changes and how we could experiment in curbing the effect. It is said that the data in such natural history collections can also provide greater social and scientific insights, but this would require skilled human resources such as researchers who can physically visit these types of repositories. There is a need for more manpower to carry out deeper experiments in this field.
Introduction of the grey area into CNNs is posed to provide a much more realistic picture of the real world. Currently, CNNs largely function exactly like a machine, seeing a true and false value for every question. However, as humans, we understand that the real world plays out in a thousand shades of grey. Allowing the machine to understand and process fuzzier logic will help it understand the grey area us humans live in and strive to work against. This will help CNNs get a more holistic view of what human sees.
CNNs have already brought in a world of difference to advertising with the introduction of programmatic buying and data-driven personalized advertising.
Other Interesting Fields
CNNs are poised to be the future with their introduction into driverless cars, robots that can mimic human behavior, aides to human genome mapping projects, predicting earthquakes and natural disasters, and maybe even self-diagnoses of medical problems. So, you wouldn't even have to drive down to a clinic or schedule an appointment with a doctor to ensure your sneezing attack or high fever is just the simple flu and not symptoms of some rare disease. One problem that researchers are working on with CNNs is brain cancer detection. The earlier detection of brain cancer can prove to be a big step in saving more lives affected by this illness.
Choose Flatworld Solutions: Pioneers in Data Science Solutions
We have aimed to explain the basics of convolutional neural networks. As you can see, CNNs are primarily used for image classification and recognition. The specialty of a CNN is its convolutional ability. The potential for further uses of CNNs is limitless and needs to be explored and pushed to further boundaries to discover all that can be achieved by this complex machinery.
We, at Flatworld Solutions, have a unique and strong understanding of the field of convolutional neural networks and data science. Our team of experienced data scientists is working with companies across the globe to help them understand this space better, as well as carve out solutions that work.
We will be happy to work with you. Contact Us to know how we can become your partner of choice in the field of deep convolutional neural networks.
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