The book focuses on helping practitioners and students understand the "inner workings" of neural networks through a series of case studies:
It includes tips for writing high-performance Python code, such as vectorizing loops . Context in the Series Applied Deep Learning: A Case-Based Approach to...
Covers essential topics like activation functions (ReLU, sigmoid, Swish), linear and logistic regression, and neural network architectures. The book focuses on helping practitioners and students
According to Umberto Michelucci's tutorials , the material is best suited for: linear and logistic regression