The book focuses on helping practitioners and students understand the "inner workings" of neural networks through a series of case studies:
By building models from scratch (NumPy), you learn to appreciate the efficiency of modern frameworks like TensorFlow. Applied Deep Learning: A Case-Based Approach to...
It includes tips for writing high-performance Python code, such as vectorizing loops . Context in the Series The book focuses on helping practitioners and students
Covers essential topics like activation functions (ReLU, sigmoid, Swish), linear and logistic regression, and neural network architectures. Encourages learning by doing
Encourages learning by doing, including implementing logistic regression from scratch using NumPy before moving to libraries like TensorFlow .
The book emphasizes the importance of how to split datasets into train, dev, and test sets to solve real-world problems effectively.
and Mathematicians looking for fundamental properties and a "from-scratch" understanding.