Hands-On Mathematics for Deep Learning

Dive into the essential mathematics that forms the backbone of deep learning in "Hands-On Mathematics for Deep Learning." This book provides a practical, hands-on approach to mastering concepts like linear algebra, calculus, and optimization while applying them directly to deep neural network training in Python.

What this Book will help me do

• Grasp linear algebra and calculus concepts for understanding and building neural networks.
• Gain practical insights into optimization techniques like SGD and Adam.
• Learn the details of forward propagation and backpropagation in deep learning.
• Understand essential architectures like CNNs, RNNs, and GANs through math-driven explanations.
• Develop the skills to optimize and troubleshoot deep learning models effectively.

Author(s)

None Dawani is an author and educator with deep expertise in data science, machine learning, and mathematics. With years of experience crafting solutions and teaching mathematical concepts in AI, None brings clear explanations and practical examples to complex topics. Their passion is empowering learners to reach new technical heights.

Who is it for?

This book is tailored for data scientists, machine learning practitioners, and programmers aiming to fortify their understanding of deep learning mathematics. It assumes familiarity with Python programming and machine learning basics, making it suitable for those ready to level up their theoretical foundation. If you're driven to bridge the gap between coding and theory, you'll find this book invaluable.