Complete Guide to Neural Network in Computer Vision

Complete Guide to Neural Network in Computer Vision

Computer vision is an essential branch of artificial intelligence and machine learning that deals with digital media such as images and videos. The basic fundamental concept developers should be aware of is the neural network. Although research is still going on that deals with the further optimization of artificial neural networks, the performance given by the current techniques combined with neural networks is exemplary. This article not only talks about the usage of neural networks in computer vision but also outlines a complete introduction and insight into what neural networks are, how they work and where they find their application in the real world. The readers are encouraged to use a pen and paper or Notion to note the essential points they find in this article. 

Neural Networks

The word “neural” symbolizes the existence of neural networks as an artificial attempt to copy the neurons in the human brain. The terminology used for the parts of this network is precisely the same as that of the human neural network. Thousands of neurons work together in a neural network to generate results and follow the process of learning and penalizing to make the model more efficient and accurate. Neural networks are generally used for problems that involve the recognition or comparison of a new data point with a massive dataset of existing data points. 

The human brain functions in a way that our actions are affected by our past experiences. If a child falls when he runs on the road after his mother explicitly told him not to, the next time the child will think not to listen to his mother talking about his welfare, he will recall the experience and will not undergo the same agony again. Similarly, in neural networks, the action taken by the hidden layers is affected by the previously hidden layers by accumulating the learnings gathered by the neurons. 

The significant advantage of using neural networks for deep learning problems is that deep learning generally deals with massive datasets having millions of data points. Standard machine learning algorithms take significant time to run through them and learn. However, because of their vast interconnection between neurons, neural networks eliminate the need for such effective durations of time and can efficiently correlate amongst different data points. This becomes highly advantageous since the learnings of the model and the accuracy increase when a large dataset is provided, which enables the model to fit most of the data points and predict results accurately. 

Architecture and Working of Neural Networks

As we discussed in the introduction to neural networks, the neural network model is an imagery of the human brain as there are billions of neurons present in the human brain connected, the neural network as well as a collection of hidden layers where each hidden layer comprises of a certain number of neurons which the computer vision or machine learning engineer can define themselves. 

The input to the neural network is the features of the data points. A significant aspect of neural networks is the activation function which incorporates non-linearity into the model and enables it to decide if a neuron is supposed to be activated or not. This means determining if a neuron’s learnings are significant enough to be fed to the next hidden layer. Another term introduced is called the bias, which is used to prevent overfitting and under-fitting and enables the activation function to shift and dispose of such irregularities. Weight is an essential part of machine learning algorithms and even neural networks, which helps determine the necessity of a feature present in the input. The inputs are multiplied by weights, and then the bias term is added and passed through the activation function before feeding to the next layer. The first layer of the neural network is called the input layer, where the data which is supposed to be used to train the model is fed. The subsequent layers are known as hidden layers, which contain neurons. The number of hidden layers and the neurons in each hidden layer defines the complexity of the neural network. It is traditional to decrease the number of neurons with progressing hidden layers. The output layer makes the neural network’s final layer, which gives our model’s result. The number of neurons in the output layer equals our problem statement’s number of classes. The output is calculated by utilizing linear regression models. 

Types of Neural Networks

Perceptron 

A perceptron is a neural network with only one layer in its entire architecture. This single layer is the computational layer that applies weight to the input, introduces bias, and passes it through the activation function to produce an output. 

Feed Forward Network

Feedforward networks are the most common types of neural networks. The principle is “feeding” neutrons to the subsequent layers and moving “forward” in a singular direction. This neural network type requires several hidden layers to be incorporated as the learnings are enhanced as the number of hidden layers is increased. 

Multi-Layer Perceptron

The multi-layer perceptron neural network utilizes the gradient descent property popularised in linear regression. This gradient descent is responsible for adjusting the weights to facilitate the neural network’s learning process. 

Radial Basis Networks

Suppose a problem statement introducing a new class or category must be considered for generating output. In that case, the radial basis neural network proves to be helpful. It utilizes the euclidean distance and then compares the results with the learnings of the neurons to offer its predictions. 

Recurrent Neural Networks 

The recurrent neural networks are utilized for sequential problems such as image captioning, word prediction, code completion, sentence completion, etc. Although robust, recurrent neural networks must deal with the vanishing gradient issue. This means that the partial derivative tends to zero during the gradient calculation. 

Long-Short-Term Memory Networks

This solves the problem of vanishing gradients that is present in recurrent neural networks. It has memory cells that stores data. LSTM neural networks also work for sequential issues. 

Convolutional Neural Networks

Convolutional neural networks deal with digital media datasets such as images and video. Convolutional layers are applied to the data for feature extraction and fed to a neural network to predict the output classes. 

Backpropagation

Backpropagation, as the name suggests, moves in a backward direction. It moves from the output layer back to the input layer to determine the optimum value of the weights used in a neural network. The weights are updated according to the findings of the backpropagation model. This is done to minimize the error produced by the neural network model. Introducing backpropagation while working with neural networks significantly reduces the deviation of the predicted value from the actual value. 

Applications of Neural Networks

Neural networks are utilized in all spheres where there is a need to analyze and work on datasets containing millions of data points. The first thing that comes to mind when we think of the generation of large data is social media. Neural networks power recommendation engines on social media websites like Instagram and Twitter. Next time you see some profiles being recommended to you to follow, you can quickly think of what is happening in the backend. The AR filters on Snapchat also recognize facial recognition and spatial recognition techniques which are again an advanced example of the utilization of neural networks. The next place where millions of customers are present and generate millions of gigabytes of data daily is e-commerce. E-commerce websites like Amazon and Flipkart and food delivery applications like Swiggy and Zomato also use neural networks for their recommendation engines. Personal assistants such as Siri and Alexa utilize neural networks in combination with natural language processing. Stock prediction and weather forecasting models also use neural networks. The Healthcare industry also finds the importance of neural networks in predicting diseases and analyzing medical imagery and scans. 

Conclusion

Neural networks are crucial in almost every field, and machine learning algorithms solve problems. It is undoubtedly a breakthrough that has enabled developers to build complex models in the minimum amount of time while utilizing massive datasets. The engineers and developers need to take care of the type of neural network and the hyperparameters associated with it, such as the number of hidden layers, the number of neurons in each layer, and the activation function used to produce the best results. In this article, the author has tried to give readers a crisp introduction to neural networks, which will serve as a basis for further reading and development. It is recommended that the readers also look for the mathematical aspect of backpropagation and solve some numerical regarding the same to gain a better understanding. The author suggests videos by Stanford University, which are available for public use on YouTube. 

The readers are also encouraged to build neural networks and tweak the parameters to see the changes in their preferred language, Python or R. Start with minor prediction problems. Then you can solve more complex issues with complex mathematical formulae. 

Read more about learning paths at codedamn here.

Happy Learning! 

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