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|a Bergel, Alexandre.
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|a Agile artificial intelligence in Pharo
|b implementing neural networks, genetic algorithms, and neuroevolution
|c Alexandre Bergel
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|a [United States]
|b Apress
|c 2020
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|a 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?
|
| 505 |
8 |
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|a 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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8 |
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|a 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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|a 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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8 |
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|a 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
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Bergel, Alexandre. |
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Bergel, Alexandre. |
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Bergel, Alexandre. |
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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?, 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, 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, 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, 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 |
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[United States], Apress, 2020 |
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Bergel, Alexandre., Agile artificial intelligence in Pharo implementing neural networks, genetic algorithms, and neuroevolution Alexandre Bergel, [United States] Apress 2020, 1 online resource., text rdacontent, computer rdamedia, online resource rdacarrier, Includes index., 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?, 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, 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, 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, 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, Artificial intelligence., Agile software development., Electronic books., ELD ebook, LDL ebooks ONIX to marcxml transformation using Record_Load-eBooks_Legal_Deposit_onix2marc_v2-1.xsl 20200622 com.springer.onix.9781484253847 Uk, (OCoLC)1162845751 |
| spellingShingle |
Bergel, Alexandre., Agile artificial intelligence in Pharo: implementing neural networks, genetic algorithms, and neuroevolution, 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?, 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, 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, 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, 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, Artificial intelligence., Agile software development., Electronic books. |
| title |
Agile artificial intelligence in Pharo: implementing neural networks, genetic algorithms, and neuroevolution |
| title_auth |
Agile artificial intelligence in Pharo implementing neural networks, genetic algorithms, and neuroevolution |
| title_full |
Agile artificial intelligence in Pharo implementing neural networks, genetic algorithms, and neuroevolution Alexandre Bergel |
| title_fullStr |
Agile artificial intelligence in Pharo implementing neural networks, genetic algorithms, and neuroevolution Alexandre Bergel |
| title_full_unstemmed |
Agile artificial intelligence in Pharo implementing neural networks, genetic algorithms, and neuroevolution Alexandre Bergel |
| title_short |
Agile artificial intelligence in Pharo |
| title_sort |
agile artificial intelligence in pharo implementing neural networks genetic algorithms and neuroevolution |
| title_sub |
implementing neural networks, genetic algorithms, and neuroevolution |
| topic |
Artificial intelligence., Agile software development., Electronic books. |
| topic_facet |
Artificial intelligence., Agile software development., Electronic books. |