LibGuides: CSC685: Module 4

Module 4 Optional Resources

The following resources provide additional insight into the topics covered in Lessons 7 and 8. The links provided take you to the full text. Locate and review the specific chapters listed to support your learning.

Lesson 7

Deep Learning by Andrew Glassner
Publication Date: 2021

Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you’re ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going.

Read:

Part III: Deep Learning Basics

Lesson 8

Hands-On Genetic Algorithms with Python by Eyal Wirsansky
ISBN: 1838557741

Publication Date: 2020-01-31

Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key Features.

Read:

Section 1- Chapter 1: An Introduction to Genetic Algorithms 
Section 1- Chapter 2: Understanding the Key Components of Genetic Algorithms

Computational Intelligence by Nazmul Siddique; Hojjat Adeli
ISBN: 1118534824

Publication Date: 2013-04-22

Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks, and Evolutionary Computing presents an introduction to some of the cutting-edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems, and evolutionary neural systems

Read:

Chapter 8: Evolutionary Fuzzy Systems
Chapter 9: Evolutionary Neural Networks