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Artificial Intelligence By Example

You're reading from   Artificial Intelligence By Example Acquire advanced AI, machine learning, and deep learning design skills

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839211539
Length 578 pages
Edition 2nd Edition
Languages
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (23) Chapters Close

Preface 1. Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning 2. Building a Reward Matrix – Designing Your Datasets FREE CHAPTER 3. Machine Intelligence – Evaluation Functions and Numerical Convergence 4. Optimizing Your Solutions with K-Means Clustering 5. How to Use Decision Trees to Enhance K-Means Clustering 6. Innovating AI with Google Translate 7. Optimizing Blockchains with Naive Bayes 8. Solving the XOR Problem with a Feedforward Neural Network 9. Abstract Image Classification with Convolutional Neural Networks (CNNs) 10. Conceptual Representation Learning 11. Combining Reinforcement Learning and Deep Learning 12. AI and the Internet of Things (IoT) 13. Visualizing Networks with TensorFlow 2.x and TensorBoard 14. Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA) 15. Setting Up a Cognitive NLP UI/CUI Chatbot 16. Improving the Emotional Intelligence Deficiencies of Chatbots 17. Genetic Algorithms in Hybrid Neural Networks 18. Neuromorphic Computing 19. Quantum Computing 20. Answers to the Questions 21. Other Books You May Enjoy
22. Index

Training a CNN model

Training a CNN model involves four phases: compiling the model, loading the training data, loading the test data, and running the model through epochs of loss evaluation and parameter-updating cycles.

In this section, the choice of theme for the training dataset will be an example from the food-processing industry. The idea here is not only to recognize an object but to form a concept. We will explore concept learning neural networks further in Chapter 10, Conceptual Representation Learning. For the moment, let's train our model.

The goal

The primary goal of this model consists of detecting production efficiency flaws on a food-processing conveyor belt. The use of CIFAR-10 (images) and MNIST (a handwritten digit database) proves useful to understand and train some models. However, in this example, the goal is not to recognize objects but a concept.

The following image shows a section of the conveyor belt that contains an acceptable ...

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