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Dancing with Python

You're reading from   Dancing with Python Learn to code with Python and Quantum Computing

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Product type Paperback
Published in Aug 2021
Publisher Packt
ISBN-13 9781801077859
Length 744 pages
Edition 1st Edition
Languages
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Author (1):
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Robert S. Sutor Robert S. Sutor
Author Profile Icon Robert S. Sutor
Robert S. Sutor
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Table of Contents (29) Chapters Close

Preface 1. Chapter 1: Doing the Things That Coders Do FREE CHAPTER 2. Part I: Getting to Know Python
3. Chapter 2: Working with Expressions 4. Chapter 3: Collecting Things Together 5. Chapter 4: Stringing You Along 6. Chapter 5: Computing and Calculating 7. Chapter 6: Defining and Using Functions 8. Chapter 7: Organizing Objects into Classes 9. Chapter 8: Working with Files 10. PART II: Algorithms and Circuits
11. Chapter 9: Understanding Gates and Circuits 12. Chapter 10: Optimizing and Testing Your Code 13. Chapter 11: Searching for the Quantum Improvement 14. PART III: Advanced Features and Libraries
15. Chapter 12: Searching and Changing Text 16. Chapter 13: Creating Plots and Charts 17. Chapter 14: Analyzing Data 18. Chapter 15: Learning, Briefly 19. References
20. Other Books You May Enjoy
21. Index
Appendices
1. Appendix A: Tools 2. Appendix B: Staying Current 3. Appendix C: The Complete UniPoly Class
4. Appendix D: The Complete Guitar Class Hierarchy
5. Appendix E: Notices 6. Appendix F: Production Notes

15.8 Concepts of neural networks

Now let’s generalize what we just saw. We keep the three input nodes and call this the input layer. These feed into the hidden layer, and the results are in the output layer.

Neural network with 3 inputs, 4 hidden nodes, and 2 outputs
Figure 15.15: Neural network with 3 inputs, 4 hidden nodes, and 2 outputs

Processing in a neural network moves from left to right, and we call this the “forward direction.”

Figure 15.15 shows the three input nodes, four nodes in the hidden layer, and two output nodes. I’ve also shown the weights wi,1 for the input nodes going to the first hidden node h1. We also have weights relating the input nodes to h2, h3, and h4, and relating those to the output nodes. I have not shown them to keep the diagram less cluttered.

Another name for a node is neuron, and Figure 15.15 shows a neural network. More precisely, this is ...

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