Math and Architectures of Deep Learning (MEAP V10)

The mathematical paradigms that underlie deep learning typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. You’ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.
 
What's inside
• Math, theory, and programming principles side by side
• Linear algebra, vector calculus and multivariate statistics for deep learning
• The structure of neural networks
• Implementing deep learning architectures with Python and PyTorch
• Troubleshooting underperforming models
• Working code samples in downloadable Jupyter notebooks