Deep learning (DL) represents one of the most profound technological advancements of our time. It has transformed industries, unlocked new scientific discoveries, and altered the way we perceive intelligence. From mastering image recognition to advancing natural language processing, from enabling autonomous systems to revolutionizing healthcare, the applications of deep learning are as diverse as they are impactful. And yet, this is only the beginning.

This book aims to equip readers with the knowledge and skills necessary to design, build, and deploy cutting-edge DL solutions as well as unravelling the complexities of DL and transforming intimidating concepts into approachable knowledge. It is introduced to bridge the gap between theory and practice, providing readers with a comprehensive and hands-on exploration of advanced DL techniques.

Our focus is on the "engineering" aspect of DL. We explore advanced network architectures and techniques such as RNN, GoogLeNet, ResNet, transformer, GCN, R-CNN, YOLO, U-Net, GAN, cycleGAN, DIR, LSTM, BPTT, CBOW, skip-gram, word2vec in detail. We also cover critical aspects of model training, including optimization algorithms, regularization techniques, hyperparameter tuning, and efficient deployment strategies. Throughout the book, we emphasize practical implementation using popular DL framework such as MATLAB, providing concrete examples and code snippets to reinforce the concepts discussed.

Hone your DL skills with this cutting-edge book crafted by one of the top 2% scientists in the world.