In this digital era 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 them. 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 on the convolutional neural networks talks about how the 3-dimensional CNN replicates the simple and complex cells of the human brain, including the receptive fields that humans experience through their senses. We will first address what CNNs are, their structure and biological connections, and their optimum functionality.
Let's start with what CNNs are. Like the way our brains identify objects when we see a picture, computers also should recognize objects in the same manner. However, there exists a huge difference between what a human brain sees when looking at an image and what a computer sees. To a computer, an image is just another array of numbers. Each object has its 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, 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 reduce the image classification errors from 26% to 15%. This substantial drop 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 businesses grow, such as Google, Amazon, Instagram, Facebook, and Pinterest.
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 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.
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 at 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 aid 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.
As is with any completed product, it's 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, hooves, 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.
Companies may find it difficult to integrate convolutional neural networks and neural networks into production-ready applications. Multiple factors need to be taken into consideration to make this happen, such as -
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 smooth integration, you must ensure that you follow these steps -
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 that 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 -
Facial recognition is broken down by a convolutional neural network into the following major components -
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.
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 gray 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 gray. Allowing the machine to understand and process fuzzier logic will help it understand the gray area we 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 a world of difference to advertising with the introduction of programmatic buying and data-driven personalized advertising.
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 the 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.
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.
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