AN APPROACH TO DEEP LEARNING

Abstract

Deep learning is a subfield of machine learning that has gained significant popularity in recent years due to its ability to achieve state-of-the-art results in a variety of applications, ranging from computer vision and natural language processing to robotics and gaming. It is based on artificial neural networks, which are designed to mimic the structure and function of the human brain. In this book, we provide a comprehensive overview of deep learning, including its definition, history, key characteristics, limitations, and applications. Chapter 1, we delve into the fundamentals of deep learning, including its definition, historical overview, and the differences between deep learning and machine learning. Additionally, we introduce Bayesian learning concepts, which are an important aspect of deep learning. We also cover the concept of decision surfaces and how they can be used to visualize and interpret the results of deep learning algorithms. Chapter 2 focuses on linear classifiers, including linear discriminant analysis, logistic regression, and the perceptron algorithm. We also cover linear machines with hinge loss, which is a popular optimization technique used in deep learning. Chapter 3 discusses various types of optimization techniques, including gradient descent and batch optimization. We provide an overview of each optimization method, as well as their variants, and explain how they work. Chapter 4, we introduce neural networks, including the structure of neural networks, how they work, and the key components of neural networks. We then delve into the multilayer perceptron, which is one of the most commonly used neural network architectures, and the back propagation learning algorithm, which is used to train neural networks. Keywords: Machine learning, Artificial neural networks, Computer vision, Natural language processing, Robotics, Gaming, State-of-the-art results, Human brain, Comprehensive overview, Bayesian learning, Decision surfaces, Linear classifiers, Linear discriminant analysis, Logistic regression, Perceptron algorithm, Linear machines, Hinge loss, Optimization techniques, Gradient descent, Batch optimization, Neural networks, Multilayer perceptron, Back propagation learning algorithm, Key components, Training neural networks