Neural Networks for Engineers: A Mathematical Treatise From Fundamentals to Advanced Deep Learning Techniques (Data Sciences)

"Neural Networks for Engineers" is a detailed guide that demystifies the intricacies of neural networks for engineers and practitioners. Starting with the human nervous system fundamentals, this book draws parallels between biological neurons and their artificial counterparts, setting the stage for an in-depth exploration of neural networks. The book meticulously covers topics such as the McCullough-Pitt Neuron, the origins of the Perceptron, and the evolution of modern neural networks.

Chapter 1 introduces readers to the MP Neuron, the first artificial neuron designed in 1943, and provides an unmatched explanation of its formulation and the workings of Perceptrons. This chapter also highlights the manual calculations of Perceptrons, revealing how much neural network computations can be understood and performed manually, enhancing transparency in fields like law enforcement and medicine.

Chapter 2 builds on this foundation by exploring Multi-Layer Perceptrons (MLPs) through the digit classification problem using the MNIST dataset. The book contrasts implementations in Scikit-Learn and Keras, showing the advantages of modern deep-learning frameworks. It covers critical elements such as activation functions, hyperparameter tuning, and practical considerations for optimising neural networks.

Chapter 3 delves into Convolutional Neural Networks (CNNs), explaining their design elements through detailed mathematical insights. It emphasises the transition from MLPs to CNNs, highlighting their applications in specialised fields like image and signal processing. The chapter also discusses Transfer Learning, making complex neural network capabilities accessible to smaller firms and novice engineers.

Chapter 4 provides a gateway into Recurrent Neural Networks (RNNs), progressing from basic RNNs to advanced structures like LSTMs and GRUs. This chapter equips readers with knowledge of sequential data processing, which is critical for next-word prediction and sentiment analysis applications. Including hyperparameter tuning using the Keras Tuner further enhances the reader’s ability to fine-tune complex models.

"Neural Networks for Engineers" combines theory, computation, and application, offering comprehensive coverage of neural networks with hands-on implementations in Scikit-Learn, Keras, and PyTorch. This book is ideal for engineers seeking to master neural networks and leverage their power in real-world applications.