Название: Neural Networks and Deep Learning: A Textbook, 2nd EditionАвтор: Charu C. Aggarwal
Издательство: Springer
Год: 2023
Страниц: 541
Язык: английский
Формат: pdf (true), epub
Размер: 46.8 MB
Neural networks were developed to simulate the human nervous system for Machine Learning tasks by treating the computational units in a learning model in a manner similar to human neurons. The grand vision of neural networks is to create artificial intelligence by building machines whose architecture simulates the computations in the human nervous system. Although the biological model of neural networks is an exciting one and evokes comparisons with science fiction, neural networks have a much simpler and mundane mathematical basis than a complex biological system. The neural network abstraction can be viewed as a modular approach of enabling learning algorithms that are based on continuous optimization on a computational graph of mathematical dependencies between the input and output. These ideas are strikingly similar to classical optimization methods in control theory, which historically preceded the development of neural network algorithms.
Neural networks were developed soon after the advent of computers in the fifties and sixties. Rosenblatt’s perceptron algorithm was seen as a fundamental cornerstone of neural networks, which caused an initial period of euphoria — it was soon followed by disappointment as the initial successes were somewhat limited. Eventually, at the turn of the century, greater data availability and increasing computational power lead to increased successes of neural networks, and this area was reborn under the new label of “Deep Learning.” Although we are still far from the day that Artificial Intelligence (AI) is close to human performance, there are specific domains like image recognition, self-driving cars, and game playing, where AI has matched or exceeded human performance. It is also hard to predict what AI might be able to do in the future. For example, few computer vision experts would have thought two decades ago that any automated system could ever perform an intuitive task like categorizing an image more accurately than a human. The large amounts of data available in recent years together with increased computational power have enabled experimentation with more sophisticated and deep neural architectures than was previously possible. The resulting success has changed the broader perception of the potential of Deep Learning. This book discusses neural networks from this modern perspective.
The chapters of the book are organized as follows:
1. The basics of neural networks: Chapters 1, 2, and 3 discuss the basics of neural network design and the backpropagation algorithm. Many traditional machine learning models can be understood as special cases of neural learning. Understanding the relationship between traditional machine learning and neural networks is the first step to understanding the latter. The simulation of various machine learning models with neural networks is provided in Chapter 3. This will give the analyst a feel of how neural networks push the envelope of traditional machine learning algorithms.
2. Fundamentals of neural networks: Although Chapters 1, 2, and 3 provide an overview of the training methods for neural networks, a more detailed understanding of the training challenges is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines.
3. Advanced topics in neural networks: A lot of the recent success of deep learning is a result of the specialized architectures for various domains, such as recurrent neural networks and convolutional neural networks. Chapters 8 and 9 discuss recurrent and convolutional neural networks. Graph neural networks are discussed in Chapter 10. Several advanced topics like deep reinforcement learning, attention mechanisms, neural Turing mechanisms, and generative adversarial networks are discussed in Chapters 11 and 12.
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