Applied Deep Learning: A Case-based Approach To... -

and Mathematicians looking for fundamental properties and a "from-scratch" understanding.

Each method is paired with real-world examples to demonstrate theoretical concepts in action. Target Audience 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. and Mathematicians looking for fundamental properties and a

It includes tips for writing high-performance Python code, such as vectorizing loops . Context in the Series It includes tips for writing high-performance Python code,

This 2018 title was followed by (2019), which builds on these foundations to cover specialized topics like object detection with Keras. ICAART 2021 - tutorials

The book focuses on helping practitioners and students understand the "inner workings" of neural networks through a series of case studies:

Encourages learning by doing, including implementing logistic regression from scratch using NumPy before moving to libraries like TensorFlow .

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