Why do we use Neural networks?

In the field of machine learning, there has been a remarkable rise in the popularity and utilization of neural networks, particularly deep neural networks. While classical machine learning algorithms have their own merits, neural networks have emerged as a powerful tool for solving complex problems in various domains. This Answer aims to explain the reasons behind the widespread adoption of neural networks and delve into their unique capabilities.

Approximation power

Deep neural networks have the unique capability to approximate complex decision boundaries. They can learn complicated relationships between inputs and outputs, enabling them to solve highly non-linear problems. This flexibility makes deep neural networks suitable for a wide range of tasks, from image and speech recognition to natural language processing and recommender systems.

High representational power

Deep neural networks possess high representational power, which means they can learn and represent complex functions. With their deep architectures and multiple layers, they can capture complex features and hierarchies of information, enabling them to handle tasks that involve complex patterns.

Utilization of available data

Deep neural networks excel in effectively utilizing available data. They can extract valuable information from large datasets, allowing them to learn more accurate representations and make better predictions. As the amount of data increases, the performance of deep neural networks tends to improve, making them ideal for scenarios where a large amount of training data is available.

Example

The universal approximation theorem states that deep neural networks can effectively approximate nearly all decision boundaries that correspond to functions. While some exceptional cases exist, deep neural networks can approximate most functions.

Let’s discuss an example of supervised learning for the better sake of understanding. In supervised learning, our classification model or regressor learns functions that predict values based on input. For a regressor, these values may be real numbers or vectors of real numbers, while for a classification model, the aim is to learn a decision boundary that separates classes, as shown in the figure, which can also be understood as a function. The ultimate goal of the model is to approximate this function. Additionally, deep neural networks possess a high representational power and can effectively utilize available data.

While classical machine learning models like Support Vector Machines (SVM) and logistic regression have limitations in efficiently utilizing data, deep neural networks excel in this aspect. The advantages of having a larger dataset become evident as it offers valuable insights for approximating the desired functions. The performance of Deep neural networks improves as more data becomes available.

Due to their high representational power and effective data utilization, they can approximate complex decision boundaries, which is hard for classical machine learning algorithms. While there may be specific scenarios where a particular machine learning algorithm yields optimal results, in most cases, DNNs are commonly employed.

Image classification with deep neural networks

In image classification, deep neural networks offer significant advantages over traditional machine learning algorithms that rely on manual feature engineering. Let’s consider the task of automatically classifying images into categories like cats, dogs, and birds. Traditional approaches require experts to manually extract features from the images, such as edges, textures, or color histograms. This process is time-consuming and requires domain expertise to determine which features are relevant for classification.

Deep neural networks, on the other hand, can learn hierarchical representations directly from raw pixel data. Each layer of neurons in the network learns to extract increasingly abstract features, enabling the network to understand complex patterns and structures within the images.

For example, early layers may learn to detect edges or simple shapes, while deeper layers can recognize more complex features like eyes, noses, or wings. In the output layer, the network predicts the image’s class based on these learned features.

The advantage of deep neural networks is their ability to automatically learn relevant features from the data, eliminating the need for explicit feature engineering. This saves time and allows the network to capture complicated patterns and relationships that may not be apparent to human experts.

By utilizing deep neural networks, we can develop highly accurate image classification systems capable of handling large datasets with thousands or millions of images. The ability to learn and generalize from extensive data is a key factor in their superior performance in tasks like image classification.

Conclusion

Deep neural networks have gained significant popularity in the field of machine learning due to their ability to approximate complex decision boundaries, utilize available data effectively, and have high representational power. The example of image classification explains how deep neural networks can automatically learn relevant features from raw data, eliminating the need for manual feature engineering.