What are Convolutional Neural Networks (CNNs)?

Summary

Convolutional Neural Networks (CNNs) are a type of deep learning model used for pattern recognition, especially in image processing. They help computers perform tasks like object identification, which is inherently easy for humans but challenging for machines. The video explains how CNNs work by breaking down an artificial neural network into layers and applying filters to recognize patterns such as shapes or objects within images. These networks have applications across various industries, from medical image analysis to visual recognition, and are instrumental in tasks requiring object and pattern identification.

Highlights

• CNNs are part of deep learning, crucial for object identification. 🧠
• They work by using multiple network layers to analyze image data. 📊
• Filters in CNNs recognize patterns like straight lines or shapes within images. 📏
• The deeper the network layers, the more complex pattern recognition becomes. 📈
• CNNs can be used in business applications like OCR and visual search. 💼

Key Takeaways

• CNNs are specialized in recognizing patterns, particularly in images. 🖼️
• Filters within CNNs help identify common shapes and objects, improving recognition capabilities. 🔍
• CNN layers become more abstract and capable as they go deeper, aiding in complex recognitions. 🚀
• They have broad applications such as in OCR, visual recognition, and medical imaging. 🏥
• CNNs transform raw image pixels into useful data via a process called pooling. 🔄

Overview

Convolutional Neural Networks, often known as CNNs, are a fascinating branch of deep learning. They specialize in pattern recognition - a personality trait that makes humans incredibly adept at identifying and categorizing objects and scenes. For instance, we can easily recognize a house, even if it's just a basic sketch. CNNs attempt to replicate this capability by analyzing images through multiple layers of filters and networks.

At the heart of a CNN are layers that process image data, identifying patterns like shapes, lines, or curves by scanning images through various filters. At a basic level, these filters look for simple patterns, but as the network dives deeper, it can recognize more complex and abstract features within the images. This allows CNNs to excel in tasks that involve detailed image analysis, not unlike how a detective pieces together clues.

CNNs see numerous applications in today’s world – from helping computers understand handwritten documents through OCR to aiding the medical field by interpreting scans and images. They're also a game changer in fields involving visual recognition and searches, making them an invaluable tool in both technology and science. The journey through a CNN is akin to peeling back layers of an onion, uncovering deeper and more abstract capabilities at every step.

What are Convolutional Neural Networks (CNNs)? Transcription

• 00:00 - 00:30 OK, pop quiz. What am I drawing? I'm going to make three predictions here. Firstly. You think at your house, you'd be right? Secondly, that that just came pretty easily to you, it was effortless. And thirdly, you're thinking that I'm not much of an artist and you'd be right on all counts there. But how can we look at this set of geometric shapes and think, Oh, how?
• 00:30 - 01:00 If you live in a house, I bet it looks nothing like this. Well, that ability to perform object identification that comes so easily to us does not come so easily to a computer, but that is where we can apply something called convolutional neural networks to the problem. Now, a convolutional neural network or a. See, and and. Is a area of deep learning
• 01:00 - 01:30 that specializes in pattern recognition. My name is Martin Keane, and I work in the IBM garage at IBM. Now let's take a look at how CNN works at a high level. Well, let's break it down. CNN convolutional neural network Well, let's start with the artificial neural network part. This is a standard network that consists of multiple layers that are interconnected,
• 01:30 - 02:00 and each layer receives some input. Transforms that input to something else and passes an output to the next layer, that's how neural networks work and see an end is a particular part of the neural network or a section of layers that say it's these three layers here and within these layers, we have something called filters. And it's the filters that perform the pattern recognition
• 02:00 - 02:30 that CNN is so good at. So let's apply this to our house example now. If this house were an actual image, it would be a series of pixels, just like any image. And if we zoom in on a particular part of this house, let's say we zoom in around here, then we would get, well, the window. And what we're saying here is that a
• 02:30 - 03:00 window consists of some perfectly straight lines. Almost perfectly straight lines. But, you know, a window doesn't need to look like that window could equally look like this, and we would still say it was a window. The cool thing about CNN is that using filters. CNN could also say that these two objects represent the same thing. The way they do that, then, is through the application of these filters. So let's take a look at how that works. Now, a filter is basically
• 03:00 - 03:30 just a three by three block. And within that block, we can specify a pattern to look for. So we could say, let's look for. Pattern like this, a right angle in our image. So what we do is we take this filter and it's a three by three block here. We will analyze the equivalent three by three block up here as well. So. We'll look at first of all, these first.
• 03:30 - 04:00 Group of three by three pixels, and we will see how close are they to the filter shape? And we'll get that numeric score, then we will move across one, come to the right and look at the next three by three block of pixels and score how close they are to the filter shape. And we will continue to slide over or vote over all of these pixel layers until we have not every three by three block.
• 04:00 - 04:30 Now, that's just for one filter. But what that will give us is an array of numbers that say how closely and the image matches filter, but we can add more filters so we could add another three by three filter here. And perhaps this one looks for a shape like this. And we could add a third filter here, and perhaps this looks for a different kind of right angle shape. If we take the numeric arrays from all of these filters and
• 04:30 - 05:00 combine them together in a process called pooling, then we have a much better understanding of what is contained within this series of pixels. Now that's just the first layer of the CNN. And as we go deeper into the neural network, the filters become more abstract all they can do more. So the second layer of filters perhaps can perform tasks like basic object recognition. So we can have filters here that
• 05:00 - 05:30 might be able to recognize the presence of a window or the presence of a door or the presence of a roof. And as we go deeper into the sea and into the next leg, well, maybe these filters can perform even more abstract tasks, like being able to determine whether we're looking at a house or we're looking at an apartment or whether we're looking at a skyscraper. So you can see the application
• 05:30 - 06:00 of these filters increases as we go through the network and can perform more and more tasks. And that's a very high level basic overview of what CNN is. It has a ton of business applications. Think of OCR, for example, for understanding handwritten documents. Think of visual recognition and facial detection and visual search. Think of medical imagery and looking at that and determining what is being shown in an imaging scan.
• 06:00 - 06:30 And even think of the fact that we can apply a CNN to perform object identification for. Body drawn houses, if you have any questions, please drop us a line below, and if you want to see more videos like this in the future, please like and subscribe. Thanks for watching.