Advanced AI: Neural Networks and Deep Learning

Advanced AI: Neural Networks and Deep Learning provides an in-depth exploration of the architectures, algorithms, and applications that define state-of-the-art artificial intelligence. Beginning with a concise review of machine learning fundamentals, the book delves into core topics such as perceptrons, multilayer feedforward networks, backpropagation, optimization techniques, and regularization methods. It then expands into advanced deep learning architectures, including convolutional neural networks (CNNs) for image processing, recurrent and LSTM networks for sequential data, transformers for language models, and generative approaches like GANs and diffusion models. Each chapter integrates mathematical formulations, intuitive explanations, and illustrative examples to bridge theory and practice. Practical implementation is emphasized through discussions of popular frameworks such as TensorFlow and PyTorch, data preprocessing, hyperparameter tuning, and model evaluation. The book also explores cutting-edge applications in computer vision, natural language processing, speech recognition, autonomous systems, and healthcare, while addressing key issues like scalability, interpretability, and ethical considerations.