Convolutional Neural Network || Beginner’s Guide

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In the journey of Deep Learning, we come across a variety of neural networks. One of the most basic and foundational types is the Artificial Neural Network (ANN). ANNs are great for solving simple problems, but when it comes to complex data like images, texts, and videos, ANNs might struggle to perform effectively. To handle such complex data, we’ve introduced more advanced architectures, one of which is the Convolutional Neural Network (CNN). 🎯

CNNs are designed specifically to work with complex, high-dimensional data, especially in the field of image processing. In this blog, we’ll explore the introduction to CNNs, their history, how they work, and their applications . 🌟

So, let’s dive right in! 🚀

What is Convolutional Neural Network?

Convolutional Neural Networks are special kind of neural networks that are used for processing data that has known grid-like topology such as time-series data, image data. These networks use convolutional layers to process and make predictions from data.

CNN basically consists of an input layer, convolutional layer, pooling layer, fully connected layer (ANN) and output layer.
Don’t worry if you don’t get these points right now. We will discuss them later 😃

A basic CNN takes an image as input applies convolution operation on it then forward the resultant to ANN and generate output.

Why we use CNN?

Note -: Image is collection of pixels

ANN works very well on 1D data such as loan prediction data, house price prediction data etc. But when it comes to 2D data such as image we need to flatten it first then feed it to ANN. Suppose our 2D data is of shape (256,256), on flattening it shape will be (65536,) the trainable parameters in the first layer will be 65536 * number of neurons in layer 1 + bias terms of each neuron. Training of such a large number of neurons is computationally expensive. Hence ANN will not work well with 2D data.

Another problem arises with ANN is loss of important features such as spatial arrangement of pixels. When we flatten 2D data the pixels that are arranged according to the location will get disoriented.
ANN also leads to overfitting.

Seeing the above reasons and to properly process image data CNN was introduced.

History of CNN

CNNs have evolved significantly over time, starting in the 1960s with Hubel & Wiesel's discovery of receptive fields, which laid the foundation for feature detection. In 1980, Kunihiko Fukushima introduced the Neocognitron, a neural network that could recognize patterns in images. In the 1990s, Yann LeCun's LeNet-5 model was a breakthrough in handwritten digit recognition, marking the early success of CNNs in image processing.

The deep learning revolution began in 2012 with AlexNet, developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, which significantly advanced image classification. By 2014, VGGNet and GoogLeNet further enhanced CNN architectures, improving efficiency and performance. In 2015, ResNet introduced deeper networks with skip connections, addressing the vanishing gradient problem and becoming a standard in computer vision.

Today, CNNs power various applications, from autonomous driving to medical imaging, with innovations like Capsule Networks, EfficientNet, and Transformers continuing to reshape deep learning.

Working of CNN

Intuition behind CNN

As we have already seen that CNN was initially used on handwritten digits data for recognizing different digits. Now you might be wondering that different persons have different styles of writing ✍digits then how a model can recognize a digit. And here’s why we need to understand the intuition of CNN first before proceeding to its working.
Suppose we test our model for a handwritten 9️⃣ , when we feed this data to our model Ⓜ, it will extract the basic features first then using these basic features it will extract more complex features. The features are extracted in order to find the pattern in data and according to this pattern it will recognize the corresponding digit.
It is like human brain . Suppose we see an animal 🐤 now based on the physical features of that animal we classify them accordingly.

Working of CNN

Now that we’ve understood the basic intuition behind CNNs, a question still arises: how does the model actually work? 🤔

Let’s break down the basic structure of a CNN. We start with the input layer, which takes in a 2D grid representing a single image. This image is then passed through several layers of the CNN.

First, we have the convolutional and pooling layers. The convolutional layer applies filters (or kernels) to the image to extract features. This is done through a mathematical operation called convolution, which, while similar to matrix multiplication, is specifically designed to detect patterns such as edges, textures, or shapes.

The pooling layer follows, typically used to downsample the result from the convolutional layer (often called the feature map). Pooling helps reduce the dimensionality of the data, retaining only the most important information. For instance, if we want to detect horizontal edges in an image, we can apply a filter designed to extract horizontal features effectively.

Finally, we have the fully connected (dense) layers, which act similarly to a traditional Artificial Neural Network (ANN). This part of the network takes the high-level features extracted by the convolutional and pooling layers and makes predictions based on those.

In simple terms, CNNs work by scanning an image with filters to detect essential features and patterns, then passing the information through dense layers to classify or make predictions.

This explanation provides a high-level overview, as this blog is intended to be an introductory guide.

Applications of CNN

1. Image Classification 🖼️ – CNNs excel at categorizing images, improving accuracy in tasks like object and handwritten digit recognition.
2. Object Detection 🔍 – Used in self-driving cars to detect pedestrians, vehicles, and more by identifying and localizing objects in images.
3. Facial Recognition 👤 – Powering facial recognition in smartphones and security systems by learning distinct facial features.
4. Medical Imaging 🏥 – Helping in disease diagnosis through X-rays, MRIs, and CT scans by accurately identifying anomalies.
5. Self-Driving Cars 🚗 – Performing real-time vision tasks like lane detection and obstacle recognition for safe navigation.
6. Image and Video Processing 🎥 – Enhancing images, segmenting, and tracking objects in real-time for video analysis and editing.
7. NLP 💬 – Applied in text classification tasks like sentiment analysis and spam detection using CNNs on word embeddings.
8. Art Generation 🎨 – Enabling neural style transfer to create artistic visuals by blending styles and patterns.
9. Robotics 🤖 – Assisting robots in recognizing objects and navigating environments using visual data.
10. Gaming and AR 🎮 – Improving gaming realism and blending virtual and real-world elements through real-time visual data processing.

Conclusion

Convolutional Neural Networks (CNNs) have transformed how we process and understand complex data, especially in the fields of computer vision and beyond. From identifying objects in images to powering facial recognition systems, CNNs have become essential in solving a wide range of real-world problems.

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