and Mathematicians looking for fundamental properties and a "from-scratch" understanding.
The book focuses on helping practitioners and students understand the "inner workings" of neural networks through a series of case studies: Applied Deep Learning: A Case-Based Approach to...
The book by Umberto Michelucci (published by Apress) is a practical guide designed to bridge the gap between complex mathematical theory and hands-on application. Core Content & Structure and Mathematicians looking for fundamental properties and a
Encourages learning by doing, including implementing logistic regression from scratch using NumPy before moving to libraries like TensorFlow . A significant portion is dedicated to diagnosing common
A significant portion is dedicated to diagnosing common training problems such as variance , bias , and overfitting . It also explores hyperparameter tuning using methods like Grid Search and Bayesian Optimization .
By building models from scratch (NumPy), you learn to appreciate the efficiency of modern frameworks like TensorFlow.
Covers essential topics like activation functions (ReLU, sigmoid, Swish), linear and logistic regression, and neural network architectures.