Guide to CNN Deep Learning

The field aims to give computers the ability to see and understand the world like humans and use this understanding for various tasks, including image and video recognition, image analysis and categorization, media recreation, recommendation systems, natural language processing, etc. Convolutional Neural Network is the primary algorithm used to develop and refine the deep learning improvements in computer vision over time. Let’s find out more about the deep learning algorithm!

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What is Convolution Neural Network?

A Convolutional Neural Network or CNN is a deep learning method that can take in an input image, give various elements and objects in the image importance, such as learnable weights and biases, and distinguish between them. Comparatively speaking, a CNN requires substantially less pre-processing than other classification techniques. CNN has the capacity to learn these filters and properties, whereas, in primitive techniques, filters are hand-engineered.

A CNN’s architecture is influenced by how the Visual Cortex is organized and resembles the connectivity network of neurons in the human brain. Individual neurons react to stimuli only in this constrained visual field area, known as the Receptive Field. A series of such overlapping cover the entire visual field.

The architecture of the Convolution Neural Network

The architecture of convolutional neural networks differs from that of conventional neural networks. A regular neural network transforms an input, passing it through several hidden layers. Each layer consists of a set of neurons linked to all the neurons in the layer below it. The final fully-connected output layer is where the predictions are represented. 

Convolutional neural networks are structured a little differently. The layers are first arranged in three dimensions: width, height, and depth. Additionally, only a portion of the neurons in the following layer are connected to those in the layer below. The output will then be condensed into a single probability score vector and grouped along with the convolution layer.

CNN consists of two parts:

The extraction of features from hidden layers

The network will do a series of convolutional and pooling operations in this section to detect the features. This is where the network would identify the stripes of a tiger, two ears, and four legs if you had an image of one. 

Section Classification

On top of these retrieved features, the convolution layers will work as a classifier in this case. They will give the likelihood that the image’s object matches the algorithm’s prediction.

Extraction of features

One of CNN’s key components is convolution. The mathematical combining of two functions to yield a third function is referred to as convolution. It combines two sets of data. A feature map is created by performing convolution on the input data in the case of a CNN using a filter or kernel. The convolution is carried out by moving the filter over the input. Each location performs a matrix multiplication and sums the output onto the feature map.

We do several convolutions on the input, using a different filter for each operation. As a result, various feature maps are produced. The output of the convolution layer is ultimately assembled using all of these feature maps.

Like every other neural network, we employ an activation process to make our output non-linear, where the activation function is used to send the output of the convolution in a convolutional neural network.

Types of Convolution Neural Network

Convolution Layer:

The foundational component of CNN is the convolution layer. It carries the majority of the computational load on the network. This layer makes a dot product between two matrices, one of which is the kernel, a collection of learnable parameters, and the other is the constrained area of the receptive field. Compared to a picture, the kernel is smaller in space but deeper. This indicates that the kerne’sl width and height will be spatially small if the image consists of three channels; though, the depth will rise to all three channels.

The kernel moves across the picture’s height and breadth during the forward pass, creating an image representation of that receptive region. As a result, a two-dimensional representation of the image called an activation map is created, revealing the kernel’s response at each location in the image. A stride is a name for the kernel’s slidable size.

Pooling Layer:

This layer only reduces the computing power needed to process the data. It is accomplished by further reducing the highlighted matrix’s dimensions. We attempt to extract the dominating features from a small portion of the neighborhood in this layer.

Average-pooling and Max-pooling are two different types of pooling strategies.

In contrast to Max-pooling, which simply takes the highest value among all those inside the pooling region, Average-pooling averages out all the values within the pooling region.

We now have a matrix with the key elements of the image after pooling the layers, and this matrix has even smaller dimensions, which will be very helpful in the following stage.

Fully Connected Layer:

An inexpensive method of learning non-linear permutations of the high-level characteristics provided by the output of the convolutional layer is to add a Fully-Connected layer. In that area, the Fully-Connected layer is now learning a function that may not be linear.

After converting it to a format appropriate for our multi-level perceptron, we will flatten the input image into a column vector. A feed-forward neural network receives the flattened output, and backpropagation is used for each training iteration. The model can categorize images using the Softmax Classification method by identifying dominant and specific low-level features across many epochs.

Non-Linearity Layers:

Non-linearity layers are frequently included right after the convolutional layer to add non-linearity to the activation map because convolution is a linear operation, and images are anything but linear.

Non-linear operations come in a variety of forms, the most common ones being:

Sigmoid

The mathematical formula for the sigmoid non-linearity is () = 1/(1+e ). It demolishes a real-valued number into the range between 0 and 1. The gradient of a sigmoid becomes almost zero when the activation is either at the tail, which is a very unfavorable sigmoid feature. Backpropagation will effectively kill the gradient if the local gradient gets too small. Additionally, suppose the input to the neuron is exclusively positive. In that case, the sigmoid output will either be exclusively positive or exclusively negative, leading to a zigzag dynamic of gradient updates for weight.

Tanh

Tanh condenses a real-valued number to the range [-1, 1]. Like sigmoid neurons, the activation saturates, but unlike them, its output is zero-centered.

ReLU

The Rectified Linear Unit (ReLU) has recently gained much popularity. It performs the function ()=max (0,) computation. To put it another way, the activation just exists at zero thresholds. ReLU speeds up convergence by six times and is more dependable than sigmoid and tanh.

Unfortunately, ReLU can be brittle during training, which is a drawback. A strong gradient can update it by preventing the neuron from updating further. However, we can make this work by choosing an appropriate learning rate.

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