Neural Networks for Statistical Modeling by Murray Smith
About Course
This book provides a detailed, yet accessible technical explanation of the inner workings of backpropagation of errors, the most commonly used training method for feedforward neural networks.
This book is for the programmer who wishes to implement backprop, or for the data miner who wishes to understand backprop. Also This book is not well-suited for people without a technical (math) background.
Pros:
-Pseudocode is provided
-Explanation advances from plain vanilla backprop to variants including enhancements like adaptive step size and momentum
-Application of neural networks is cover ed, including practical issues such as appropriate data representation and avoidance of over-fitting
-A few alternative architectures are explain ed (briefly) at the end of the book. Also
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Course Features
- Lectures 0
- Quizzes 0
- Duration 50 hours
- Skill level All levels
- Language English
- Students 66
- Assessments Yes