 |
5 Best books on Neural Networks
Here is my list of 5 most interesting books about Machine Learning:
1. Make Your Own Neural Network
For beginners, this book serves as an excellent resource, providing clear and comprehensive instruction on artificial neural networks (ANN) and calculus. The author adeptly utilizes standard Python libraries, making it accessible and practical. The explanations of neural networks are not only informative but also reader-friendly, presenting core concepts in a captivating manner. Each decision and mathematical concept required are elucidated with the aid of graphics, physical representations, and accompanying text. Furthermore, the book includes Python code examples that allow readers to practice and solidify their understanding. The code walkthroughs are detailed enough to benefit both novice and experienced coders, ensuring easy comprehension. As an added bonus, the book also offers sections on refreshing calculus knowledge and exploring Raspberry Pi, providing additional benefits to casual readers.
2. Neural Network Design
Many machine learning books often begin with complex mathematical equations, such as the multivariate Gaussian distribution, which can be quite intimidating for those who are not mathematically inclined. However, the authors of this book have taken a commendable approach by starting from the basics and explaining concepts right from the beginning of neural networks. They introduce the first generation of neural networks, along with the relevant linear algebra, and provide an explanation of their limitations before moving on to the next type or generation of neural networks. This approach is highly appreciated and logical. One aspect that stands out is how the authors skillfully integrate the theories of error surface, gradient descent, and Hessian into the context of optimization. They have done an excellent job of presenting these theories in a comprehensive manner. While it is true that this book may be slightly outdated, lacking coverage of CNN, LSTM, or deep learning, I still believe that newcomers to this field can benefit greatly from its content.
3. Neural Networks: Visual Introduction For Beginners
This book holds value for individuals with a specific interest in the subject matter, as well as those seeking general knowledge on the topic. If you are a developer with some programming language knowledge, this book will aid in your understanding of the core principles of neural networks. However, even for individuals who are unfamiliar with neural networks, the visual presentation in this book proves highly beneficial, offering insights into the underlying logical principles. While I personally acquired advanced mathematical knowledge during my high school education, including complex functions, matrices, and vectors, I must admit that grasping the complete scope of the logic behind neural networks remains beyond my current comprehension. As the authors rightfully state, a solid understanding of partial derivatives is necessary to fully leverage the potential of this book. Furthermore, I would also emphasize that having a grasp of statistics, such as understanding the principles of logistic regression, would be advantageous in comprehending the content presented.
4. Artificial Intelligence for Humans: Deep Learning and Neural Networks
This book is suitable for beginners, but it may not provide enough depth if your goal is to truly grasp the underlying concepts. While this book provides a solid starting point for beginners, it may not delve deep enough to fully understand the underlying ideas. The topics covered include neural network basics, self-organizing maps, Hopfield and Boltzmann machines, feedforward neural networks, training and evaluation, various training techniques, NEAT, CPPN, and HyperNEAT, deep learning, convolutional neural networks, pruning and model selection, dropout and regularization, time series and recurrent networks, neural network architecture, visualization, and modeling with neural networks.
5. How to Build a Brain: A Neural Architecture for Biological Cognition
This book offers a highly intuitive approach to the field of neural science, as well as large-scale brain models. It presents concepts in a manner that doesn't require an extensive mathematical background, making it accessible to a wider range of readers. A notable aspect of the book is the inclusion of practical examples at the end of each chapter, which are implemented using NENGO. The book is brimming with innovative ideas, thanks to the groundbreaking work of Chris Eliasmith and his fellow researchers at the University of Waterloo, Canada. Their models utilize spiking neurons, enabling them to be compared against real brain activity. Remarkably, these models often exhibit similarities to the behavior of actual neurons. Whether your goal is to develop a brain-like system or simply exercise your own understanding, this book is a must-read. It provides valuable insights and knowledge in the field of neural science.
See also: Top 10 eBook Organizers
1. Make Your Own Neural Network
For beginners, this book serves as an excellent resource, providing clear and comprehensive instruction on artificial neural networks (ANN) and calculus. The author adeptly utilizes standard Python libraries, making it accessible and practical. The explanations of neural networks are not only informative but also reader-friendly, presenting core concepts in a captivating manner. Each decision and mathematical concept required are elucidated with the aid of graphics, physical representations, and accompanying text. Furthermore, the book includes Python code examples that allow readers to practice and solidify their understanding. The code walkthroughs are detailed enough to benefit both novice and experienced coders, ensuring easy comprehension. As an added bonus, the book also offers sections on refreshing calculus knowledge and exploring Raspberry Pi, providing additional benefits to casual readers.
2. Neural Network Design
Many machine learning books often begin with complex mathematical equations, such as the multivariate Gaussian distribution, which can be quite intimidating for those who are not mathematically inclined. However, the authors of this book have taken a commendable approach by starting from the basics and explaining concepts right from the beginning of neural networks. They introduce the first generation of neural networks, along with the relevant linear algebra, and provide an explanation of their limitations before moving on to the next type or generation of neural networks. This approach is highly appreciated and logical. One aspect that stands out is how the authors skillfully integrate the theories of error surface, gradient descent, and Hessian into the context of optimization. They have done an excellent job of presenting these theories in a comprehensive manner. While it is true that this book may be slightly outdated, lacking coverage of CNN, LSTM, or deep learning, I still believe that newcomers to this field can benefit greatly from its content.
3. Neural Networks: Visual Introduction For Beginners
This book holds value for individuals with a specific interest in the subject matter, as well as those seeking general knowledge on the topic. If you are a developer with some programming language knowledge, this book will aid in your understanding of the core principles of neural networks. However, even for individuals who are unfamiliar with neural networks, the visual presentation in this book proves highly beneficial, offering insights into the underlying logical principles. While I personally acquired advanced mathematical knowledge during my high school education, including complex functions, matrices, and vectors, I must admit that grasping the complete scope of the logic behind neural networks remains beyond my current comprehension. As the authors rightfully state, a solid understanding of partial derivatives is necessary to fully leverage the potential of this book. Furthermore, I would also emphasize that having a grasp of statistics, such as understanding the principles of logistic regression, would be advantageous in comprehending the content presented.
4. Artificial Intelligence for Humans: Deep Learning and Neural Networks
This book is suitable for beginners, but it may not provide enough depth if your goal is to truly grasp the underlying concepts. While this book provides a solid starting point for beginners, it may not delve deep enough to fully understand the underlying ideas. The topics covered include neural network basics, self-organizing maps, Hopfield and Boltzmann machines, feedforward neural networks, training and evaluation, various training techniques, NEAT, CPPN, and HyperNEAT, deep learning, convolutional neural networks, pruning and model selection, dropout and regularization, time series and recurrent networks, neural network architecture, visualization, and modeling with neural networks.
5. How to Build a Brain: A Neural Architecture for Biological Cognition
This book offers a highly intuitive approach to the field of neural science, as well as large-scale brain models. It presents concepts in a manner that doesn't require an extensive mathematical background, making it accessible to a wider range of readers. A notable aspect of the book is the inclusion of practical examples at the end of each chapter, which are implemented using NENGO. The book is brimming with innovative ideas, thanks to the groundbreaking work of Chris Eliasmith and his fellow researchers at the University of Waterloo, Canada. Their models utilize spiking neurons, enabling them to be compared against real brain activity. Remarkably, these models often exhibit similarities to the behavior of actual neurons. Whether your goal is to develop a brain-like system or simply exercise your own understanding, this book is a must-read. It provides valuable insights and knowledge in the field of neural science.
See also: Top 10 eBook Organizers
