Okay, that title might be a little bit misleading, because let’s face it no 5 year old will try to find out what a Convolutional Neural Network 🤣. This guide is targeted for beginners in deep learning who want to learn more about Image Processing, but feels pressured when seeing papers and other writing that seems a little bit too hard to understand 😂.
A Gentle Introduction
Artificial Intelligence or AI is a monumental breakthrough that bridges the gap between what humans can do and what machine can do. One of many areas that was affected by the development of AI was Computer Vision. Those advancement created an algorithm for the Computer Vision domain that was known as Convolutional Neural Network or CNN for short.
CNNs, like neural networks, are made up of neurons with learnable weights and biases. Hummm, wait what are neural networks? Wasn’t this guide supposed to teach a 5 year old ?? 😢. Yes, I hate to break it to you, but to understand CNN, you have to know what a neural network is first. You can refer to this reference though 😁. Each neuron in CNN will receive several inputs, takes a weighted sum over them, passes it through an activation function and responds with an output. The whole network has a loss function and all the tips and tricks that we developed for neural networks still apply on CNNs. Okay, that’s enough of the hard part 😊.
So, CNN is basically a deep learning algorithm that can take images as its input and by some process of learning able to differentiate one image from another. This result could be achieved by changing the parameters (learnable weights and biases) of the model itself.
Representation of CNN Architecture
What makes CNN stand out from other classification techniques for classification of images is the total number of pre-processing required for the CNN / ConvNet is much lower as compared to other classification algorithms. Fun fact, the architecture of CNN and the majority of neural networks itself are really similar to the connectivity pattern inside of human brains and was inspired by the Visual Cortex of humans itself.
A few concept about CNN
Okay, if you are still reading this post till this section, you may realize that I was lying to you guys about a guide for a 5 year old 🤣. Truthfully, this post was made for beginners who are scared to learn about CNN and neural networks (not for 5 year olds). Understanding a few concepts about CNN may also open your eyes about how fascinating this neural network is. So without further ado, brace yourself for the concept needed to truly understand CNN 😉
CNN Layer for classification
Image as an input
An image is a matrix of matrix value that indicates the pixel value for the image. One of the main reasons CNN is really good with classification based on image was because CNN is able to capture the Spatial and Temporal dependencies in an image through the application of relevant filters. Remember, the role of the CNN is to reduce the image into a form that is simpler for the algorithm to process, while still retaining the information of the images. Because of this, CNN takes less time to process images than other algorithms, making it one of the best algorithms to use when trying to tackle image problems.
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