Understanding Convolutional Neural Networks (CNN)

Understanding Convolutional Neural Networks (CNN)

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Brothers, today we are going to talk about a particularly “high-end” but actually very practical and interesting technology – Convolutional Neural Networks (CNN). Don’t be afraid, the name sounds quite intimidating, but actually, if we chat in plain language, you will find that, hey, this thing is not that difficult!
First, we need to understand what a neural network is. A neural network is like the neurons in our brain; they are connected to each other, transmitting information. In computers, a neural network is a bunch of algorithms and mathematical models that can learn, reason, and help us solve various problems. So what about Convolutional Neural Networks? It is a “superstar” in neural networks, especially in dealing with “visual” problems like images and videos, it is simply excellent!

1. What Is a Convolutional Neural Network?

A Convolutional Neural Network, abbreviated as CNN, has a term “convolution” in its name; this “convolution” is actually a mathematical operation, which we can understand as a process of “filtering” or “feature extraction.” It’s like using a sieve to sift sand; the holes in the sieve are the “convolution kernels,” which can filter out sand (features) that match the hole size. In CNN, the convolution kernel is used to extract features from images, such as edges, corners, and textures.
So why is CNN so powerful? Mainly because it has three “special skills”: local connection, weight sharing, and pooling.
  1. Local Connection: We know that a lot of information in an image is local; for example, an edge might be composed of several adjacent pixels. Therefore, the neurons in CNN do not need to connect to the entire image, but only to local regions. This way, the number of parameters is greatly reduced, and the computation becomes faster.
  2. Weight Sharing: This is even more amazing; it states that the same convolution kernel uses the same parameters (weights) when extracting features from different positions in the image. It’s like using one sieve to sift sand across the entire beach; the size of the sieve holes (weights) remains unchanged. This allows CNN to learn many different features with very few parameters.

  3. Pooling: Pooling is like “compressing” the sand that has been sifted, keeping only the most important parts. In CNN, there are usually two types of pooling: max pooling and average pooling. Max pooling takes the maximum value in a local area, while average pooling takes the average value. This reduces the size of the image while retaining important information.

2. How Does CNN Work?

Alright, having said so much, let’s see how CNN actually works. In fact, the working process of CNN is like a “layered exploration”.
  1. Input Layer: First, we need to give CNN an image, which is the input layer. The image is composed of pixels, each with color and brightness information.
  2. Convolutional Layer: Next, the image enters the convolutional layer. The convolutional layer has many convolution kernels, which act like “sieves” sliding over the image to extract various features. For instance, some convolution kernels may extract edges, while others may extract corners.
  3. Activation Function Layer: After the convolutional layer extracts features, it goes through an activation function layer. The activation function acts like a “switch” that can “activate” the neurons. The most commonly used activation function is ReLU (Rectified Linear Unit), which turns negative values to 0 while keeping positive values unchanged. This allows the neural network to learn more complex features.
  4. Pooling Layer: Then comes the pooling layer. The pooling layer compresses the features extracted by the convolutional layer, retaining only the most important parts. Thus, the size of the image is reduced while the features are preserved.

  5. Fully Connected Layer: Finally, we have the fully connected layer. The fully connected layer acts like the “decision center” in our brain; it integrates the features extracted earlier and makes a “decision”. For example, in image classification problems, the fully connected layer determines which category the image belongs to.

3. What Are the Benefits of CNN?

So, with such complexity, what are the benefits of CNN? In fact, the benefits of CNN are numerous!
  1. Fewer Parameters, Faster Computation: Because CNN uses local connection and weight sharing, it has significantly fewer parameters than traditional neural networks. This makes computation faster and training easier.
  2. Automatic Feature Extraction: Traditional image processing methods require us to manually extract features. For instance, to recognize a cat in an image, we need to identify features such as the cat’s eyes, ears, and tail. However, CNN can automatically extract features; we just need to provide it with an image.
  3. Strong Generalization Ability: CNN has a strong generalization ability; it can learn similar features from different images. For example, it can learn the eyes and ears of a cat from one image and recognize them in another cat image.

  4. Wide Applications: The applications of CNN are vast! Image classification, object detection, facial recognition, image segmentation… all of these can be solved using CNN.

4. Practical Cases of CNN

Having talked about so much, let’s look at how CNN is applied in practice. For example, a particularly classic case is the “Cat vs. Dog” battle. In this case, CNN is given a bunch of images of cats and dogs, allowing it to learn how to distinguish between the two. The result is that CNN learns quite well, achieving a high accuracy rate!

Moreover, facial recognition is also one of CNN’s “strong suits”. Nowadays, many smartphones can use facial recognition to unlock; behind this is CNN working “silently”. It can extract our facial features and compare them with the stored features in the phone; if they match, the phone is unlocked.

5. Still Don’t Understand? I’ll Eat It!

Oh dear, after all this, I wonder if you guys understood? If you still don’t understand, then I’m going to “eat” it! But I don’t really mean I’m going to eat anything; I just want to say that if you still don’t get it, I’ll explain it again in simpler terms.
In fact, CNN is like a combination of a “super-smart sieve” and a “decision-maker”. It first uses the “sieve” (convolution kernel) to sift through the image, extracting various features; then it uses these features to make a “decision” (classification, recognition, etc.). Saying it this way, doesn’t it seem much simpler?
Alright, let’s wrap it up here for today. If you have any questions about CNN, feel free to leave a comment! If you think this article is good, don’t forget to like and follow! See you next time!

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