Artificial Neural Networks and Deep Learning is a comprehensive academic resource designed to introduce and explore the concepts, architectures, and methodologies underlying modern neural network technologies. Structured into seven systematically organised chapters, this book offers a balanced blend of theory, mathematical formulation, and real-world applications.
The initial chapters provide a foundation by explaining basic concepts, such as the perceptron model, activation functions, and the importance of neural networks. From there, the content expands into deeper territories, including multi-layer networks, learning strategies, and backpropagation. It then progresses to cover essential deep learning frameworks, including CNNs for image processing, RNNs for sequential data, and LSTM models for long-term dependencies.
A significant portion of the book focuses on advanced models such as autoencoders, Generative Adversarial Networks (GANs), and transformer-based architectures like BERT. The final chapters offer practical insights into training and optimisation techniques, covering batch normalisation, dropout, various optimisers, and evaluation metrics.
Throughout Artificial Neural Networks and Deep Learning, the aim is to provide clarity and applicability, making complex topics accessible without compromising depth. This book is ideal for students of computer science, data science enthusiasts, and professionals in the AI field seeking to enhance their understanding of deep learning systems.
By combining foundational knowledge with the latest advancements, Artificial Neural Networks and Deep Learning serves as both a learning guide and a reference manual, encouraging readers to delve deeper into one of the most exciting and impactful areas in technology today.