Develop essential engineering skills with expert instruction and practical examples.
Convolutional neural networks with mathematical derivations and practical applications is the second course in my Neural Networks and deep learning series, after the first course in the series named "College-Level Neural Networks With Mathematical Derivations". As the title implies, This course is focused on Convolutional neural networks, a special kind of neural networks mainly used for visual recognition in images and videos, yet not limited to that. In this course, I mainly focus on concepts, intuitions, mathematical derivations, and practical applications.
The course is mainly divided into 4 chapters: Chapter 1 focuses on the conceptual basics and intuitions of CNNs. What are CNNs . How do they operate.
Why are they suitable for visual recognition . and so on. Chapter 2 takes a step deeper into the CNN mathematical derivations.
What are forward and backward propagation equations through CNNs. How are they derived . How do they change with changes in hyperparameters like kernel sizes, strides, and pooling.
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