Deep Learning with Python: From Fundamentals to Frontier Models

By Dorcas O. Folarin, Aishat O. Oyewunmi, Olaoluwa S. Yaya

Deep Learning with Python: From Fundamentals to Frontier Models is a comprehensive, meticulously crafted guide that bridges the gap between beginner-friendly introductions and cutting-edge research in artificial intelligence. Written in a clear, structured, and engaging narrative, this book is designed for students, researchers, data scientists, and AI practitioners who seek both a solid foundation and insight into the latest innovations in deep learning.
The journey begins with essential mathematical concepts and Python programming techniques, ensuring that readers have the necessary tools to understand the inner workings of deep neural networks. It then builds up to advanced architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs), and Large Language Models (LLMs). Along the way, readers will explore self-supervised learning, generative modeling, multimodal AI, and the principles behind efficient training, model compression, and sustainable AI practices.
This book stands out by offering a narrative-driven approach - blending theoretical depth with practical examples, intuitive explanations, and forward-looking perspectives. Whether you are looking to master foundational models, apply state-of-the-art architectures in production, or explore emerging trends like Green AI and explainable machine learning, Deep Learning with Python equips you with the knowledge and confidence to innovate responsibly in this rapidly evolving field.
By the final chapter, readers will not only understand today's deep learning landscape but will also be prepared for the future of AI research and deployment, from technical mastery to ethical considerations.

• Format: Paperback
• Language: English
• ISBN: B0FMPZ4H3Q
• ISBN13: 9798298419543
• Release Date: August 2025
• Publisher: Independently Published
• Length: 264 Pages
• Weight: 0.79 lbs.
• Dimensions: 0.6" x 6.0" x 9.0"