Convolutional Neural Networks (CNNs): Concept And Application

In the previous tutorial on Introduction to Artificial Neural Networks (ANNs), we explored the fundamentals of deep learning and witnessed the capabilities of fully connected neural networks in tackling various tasks. We delved into how neurons within FNNs connect to solve basic problems like regression and classification. However, the human brain excels at solving significantly more intricate tasks beyond the capabilities of FNNs. To address these challenges, we have developed various types of artificial neurons, each tailored for specific problems. For instance, image and video processing requires a more complex architecture than traditional FNNs can offer. This is where convolutional neural networks (CNNs) come into play, offering a powerful tool to tackle these intricate data types.

Prerequisites:

1. Python, Numpy, Sklearn, Pandas and Matplotlib.
2. Familiarity with TensorFlow and Keras
3. Linear Algebra For Machine Learning.
4. Statistics And Probability Theory.
5. All of our previous machine-learning tutorials

Why Convolutional Neural Networks And Not FNN?

Imagine using a fully connected neural network (FCN or FNN) for image recognition. The first major hurdle is the sheer number of neurons needed in the first layer, as even a small image has thousands of pixels, each requiring a corresponding neuron in the FNN. These neurons would then be fully connected to each other, creating an intricate network of connections. However, this complexity comes at a cost. The network wouldn’t inherently understand the crucial higher-dimensional arrangement of pixels in an image, making it difficult to learn meaningful features. Furthermore, training such a network with huge parameters or with its massive number of connections becomes a computationally challenging task.

However, it’s important to note that while FCNs can learn some spatial information, they struggle significantly compared to approaches specifically designed to handle the 2D (when width and height) or 3D ( when we also take depth into account) structure of images (CNNs in our case). Spatial information here means the relative arrangement and positions of pixels within an image. Spatial information can allow us to understand the outline, shapes, depth and even recognize patterns within an image. This is why we don’t use FNNs or FCNs for image recognition tasks.

To understand the concept behind CNNs, we need to understand the basics of an image.

What Is An Image?