Introduction to Convolutional Neural Networks

A class of deep neural networks, most commonly applied to analyzing visual imagery.

Convolutional Neural Networks (CNNs) are a class of deep learning algorithms that have revolutionized the field of computer vision. They are primarily used to analyze visual imagery and are responsible for breakthroughs in image classification, object detection, and more.

Understanding the Concept of Convolutional Neural Networks

CNNs are designed to automatically and adaptively learn spatial hierarchies of features from the input data. They are inspired by the organization and functionality of the human visual cortex. The "convolutional" in the name refers to the mathematical operation that the network performs on the input data.

The Architecture of CNN

A typical CNN architecture consists of three types of layers: Convolutional Layer, Pooling Layer, and Fully Connected Layer.

• Convolutional Layer: This is the core building block of a CNN. The layer's parameters consist of a set of learnable filters (or kernels), which have a small receptive field, but extend through the full depth of the input volume. During the forward pass, each filter is convolved across the width and height of the input volume, computing the dot product between the entries of the filter and the input, producing a 2-dimensional activation map.

• Pooling Layer: Pooling layers are usually inserted between successive convolutional layers in a CNN architecture. Their function is to progressively reduce the spatial size of the representation, to reduce the amount of parameters and computation in the network, and hence to also control overfitting.

• Fully Connected Layer: Neurons in a fully connected layer have full connections to all activations in the previous layer, as seen in regular Neural Networks. Their activations can hence be computed with a matrix multiplication followed by a bias offset.

The Role of Filters/Kernels in CNN

Filters or kernels in CNN are used to extract features from the input data. During the training process, these filters are automatically learned from the data. The learned filters can detect edges, shapes, textures, objects, and even faces.

Stride and Padding in CNN

Stride and padding are two important concepts that affect the dimensionality of the output feature map.

• Stride: Stride is the number of pixels by which we slide our filter matrix over the input matrix. When the stride is 1, we move the filters one pixel at a time. When the stride is 2, we move the filters two pixels at a time, and so on. The larger the stride, the smaller the output volume size.

• Padding: Padding is used to preserve the spatial dimensions of the input volume so that the output volume has the same width and height. This is achieved by adding zeros around the border of the input volume.

Activation Functions used in CNN

Activation functions introduce non-linearity into the output of a neuron. In CNNs, the Rectified Linear Unit (ReLU) activation function is commonly used. It computes the function f(x)=max(0,x), which returns x for x>0, and returns 0 otherwise.

Building a Basic CNN using Tensorflow

Tensorflow provides a high-level API for building and training deep learning models. It provides a series of functions and libraries to build a CNN. The process involves defining the model, compiling the model by specifying the optimizer and loss function, training the model on the data, and then evaluating the model's performance.

In conclusion, Convolutional Neural Networks are a powerful tool for image analysis tasks. They have been successfully applied to a wide range of tasks, including image classification, object detection, and facial recognition.