Part 1 – Foundational Methods

In this part of the book, you will gain a comprehensive understanding of the foundational methods and techniques in deep learning architectures. Starting with the deep learning life cycle, you will explore various stages at a high level, from planning and data preparation to model development, insights, deployment, and governance. You will then dive into the intricacies of designing deep learning architectures such as MLPs, CNNs, RNNs, autoencoders, and transformers. Additionally, you will learn about the emerging method of neural architecture search and its impact on the field of deep learning.

Throughout this part, you will also delve into the practical aspects of supervised and unsupervised deep learning, covering topics such as binary classification, multiclassification, regression, and multitask learning, as well as unsupervised pre-training and representation learning. With a focus on real-world applications, this part provides valuable...