Shin, Y: Deep Learning 101 for Scientists and Engineers

Are you ready to revolutionize your scientific research and engineering projects with cutting-edge AI technology?

Deep Learning 101 for Scientists and Engineers is your hands-on guide to mastering deep learning without getting lost in complex math. This book is designed for scientists, engineers, and researchers eager to leverage adaptive deep learning models for real-world applications.

• Clear, Insight-Oriented Explanations: Grasp deep learning concepts through computational graphs, not dense equations.

• Practical, Hands-On Learning: Dive into real coding examples using PyTorch and Google Colab.

• Focus on Adaptive Transformers: Learn how adaptive models can dynamically adjust to real-time data in fields like biomedical engineering, autonomous systems, and industrial automation.

• Comprehensive Coverage: From basics like gradient descent to building advanced transformer models, everything you need is here.

• Researchers and Academics in biology, chemistry, physics, and engineering.

• Students eager to explore AI applications in their fields.

• Industry Professionals looking to enhance their systems with adaptive deep learning models.

• Focused on adaptive deep learning models that evolve with your data.

• Tools and Frameworks guide for seamless implementation.

• Hands-on coding examples tailored to scientists and engineers.