| Description |
1 online resource (xxi, 386 pages) : illustrations |
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text file rdaft |
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PDF |
| Note |
Includes index. |
| Summary |
Cover classical algorithms commonly used as artificial intelligence techniques and program agile artificial intelligence applications using Pharo. This book takes a practical approach by presenting the implementation details to illustrate the numerous concepts it explains. Along the way, youll learn neural net fundamentals to set you up for practical examples such as the traveling salesman problem and cover genetic algorithms including a fun zoomorphic creature example. Furthermore, Practical Agile AI with Pharo finishes with a data classification application and two game applications including a Pong-like game and a Flappy Bird-like game. This book is informative and fun, giving you source code to play along with. Youll be able to take this source code and apply it to your own projects. You will: Use neurons, neural networks, learning theory, and more Work with genetic algorithms Incorporate neural network principles when working towards neuroevolution Include neural network fundamentals when building three Pharo-based applications. |
| Contents |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Part I: Neural Networks -- Chapter 1: The Perceptron Model -- 1.1 Perceptron as a Kind of Neuron -- 1.2 Implementing the Perceptron -- 1.3 Testing the Code -- 1.4 Formulating Logical Expressions -- 1.5 Handling Errors -- 1.6 Combining Perceptrons -- 1.7 Training a Perceptron -- 1.8 Drawing Graphs -- 1.9 Predicting and 2D Points -- 1.10 Measuring the Precision -- 1.11 Historical Perspective -- 1.12 Exercises -- 1.13 What Have We Seen in This Chapter? |
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1.14 Further Reading About Pharo -- Chapter 2: The Artificial Neuron -- 2.1 Limit of the Perceptron -- 2.2 Activation Function -- 2.3 The Sigmoid Neuron -- 2.4 Implementing the Activation Functions -- 2.5 Extending the Neuron with the Activation Functions -- 2.6 Adapting the Existing Tests -- 2.7 Testing the Sigmoid Neuron -- 2.8 Slower to Learn -- 2.9 What Have We Seen in This Chapter? -- Chapter 3: Neural Networks -- 3.1 General Architecture -- 3.2 Neural Layer -- 3.3 Modeling a Neural Network -- 3.4 Backpropagation -- 3.4.1 Step 1: Forward Feeding -- 3.4.2 Step 2: Error Backward Propagation |
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3.4.3 Step 3: Updating Neuron Parameters -- 3.5 What Have We Seen in This Chapter? -- Chapter 4: Theory on Learning -- 4.1 Loss Function -- 4.2 Gradient Descent -- 4.3 Parameter Update -- 4.4 Gradient Descent in Our Implementation -- 4.5 Stochastic Gradient Descent -- 4.6 The Derivative of the Sigmoid Function -- 4.7 What Have We Seen in This Chapter? -- 4.8 Further Reading -- Chapter 5: Data Classification -- 5.1 Training a Network -- 5.2 Neural Network as a Hashmap -- 5.3 Visualizing the Error and the Topology -- 5.4 Contradictory Data -- 5.5 Classifying Data and One-Hot Encoding |
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5.6 The Iris Dataset -- 5.7 Training a Network with the Iris Dataset -- 5.8 The Effect of the Learning Curve -- 5.9 Testing and Validation -- 5.10 Normalization -- 5.11 Integrating Normalization into the NNetwork Class -- 5.12 What Have We Seen in This Chapter? -- Chapter 6: A Matrix Library -- 6.1 Matrix Operations in C -- 6.2 The Matrix Class -- 6.3 Creating the Unit Test -- 6.4 Accessing and Modifying the Content of a Matrix -- 6.5 Summing Matrices -- 6.6 Printing a Matrix -- 6.7 Expressing Vectors -- 6.8 Factors -- 6.9 Dividing a Matrix by a Factor -- 6.10 Matrix Product |
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6.11 Matrix Subtraction -- 6.12 Filling the Matrix with Random Numbers -- 6.13 Summing the Matrix Values -- 6.14 Transposing a Matrix -- 6.15 Example -- 6.16 What Have We Seen in This Chapter? -- Chapter 7: Matrix-Based Neural Networks -- 7.1 Defining a Matrix-Based Layer -- 7.2 Defining a Matrix-Based Neural Network -- 7.3 Visualizing the Results -- 7.4 Iris Flower Dataset -- 7.5 What Have We Seen in This Chapter? -- Part II: Genetic Algorithms -- Chapter 8: Genetic Algorithms -- 8.1 Algorithms Inspired from Natural Evolution -- 8.2 Example of a Genetic Algorithm -- 8.3 Relevant Vocabulary |
| Bibliography |
Includes bibliographical references. |
| Subject |
Artificial intelligence.
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Agile software development.
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Artificial Intelligence |
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Intelligence artificielle. |
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Méthodes agiles (Développement de logiciels) |
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artificial intelligence. |
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Agile software development |
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Artificial intelligence |
| Other Form: |
Print version: Bergel, Alexandre. Agile artificial intelligence in Pharo. [Berkeley, CA] : Apress, 2020 1484253833 (OCoLC)1113899386 |
| ISBN |
9781484253847 (electronic bk.) |
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1484253841 (electronic bk.) |
| Standard No. |
10.1007/978-1-4842-5384-7. doi |
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10.1007/978-1-4842-5 |
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