| Description |
1 online resource (412 pages) |
| Contents |
Cover; FM; Copyright; Table of Contents; Preface; Chapter 1: Introduction to Machine Learning with Keras; Introduction; Data Representation; Tables of Data; Loading Data; Exercise 1: Loading a Dataset from the UCI Machine Learning Repository; Data Preprocessing; Exercise 2: Cleaning the Data; Appropriate Representation of the Data; Exercise 3: Appropriate Representation of the Data; Life Cycle of Model Creation; Machine Learning Libraries; scikit-learn; Keras; Advantages of Keras; Disadvantages of Keras; More than Building Models; Model Training; Classifiers and Regression Models |
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Classification TasksRegression Tasks; Training and Test Datasets; Model Evaluation Metrics; Exercise 4: Creating a Simple Model; Model Tuning; Baseline Models; Exercise 5: Determining a Baseline Model; Regularization; Cross-Validation; Activity 1: Adding Regularization to the Model; Summary; Chapter 2: Machine Learning versus Deep Learning; Introduction; Advantages of ANNs over Traditional Machine Learning Algorithms; Advantages of Traditional Machine Learning Algorithms over ANNs; Hierarchical Data Representation; Linear Transformations; Scalars, Vectors, Matrices, and Tensors |
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Tensor AdditionExercise 6: Perform Various Operations with Vectors, Matrices, and Tensors; Reshaping; Matrix Transposition; Exercise 7: Matrix Reshaping and Transposition; Matrix Multiplication; Exercise 8: Matrix Multiplication; Exercise 9: Tensor Multiplication; Introduction to Keras; Layer Types; Activation Functions; Model Fitting; Activity 2: Creating a Logistic Regression Model Using Keras; Summary; Chapter 3: Deep Learning with Keras; Introduction; Building Your First Neural Network; Logistic Regression to a Deep Neural Network; Activation Functions |
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Forward Propagation for Making PredictionsLoss Function; Backpropagation for Computing Derivatives of Loss Function; Gradient Descent for Learning Parameters; Exercise 10: Neural Network Implementation with Keras; Activity 3: Building a Single-Layer Neural Network for Performing Binary Classification; Model Evaluation; Evaluating a Trained Model with Keras; Splitting Data into Training and Test Sets; Underfitting and Overfitting; Early Stopping; Activity 4: Diabetes Diagnosis with Neural Networks; Summary; Chapter 4: Evaluate Your Model with Cross-Validation using Keras Wrappers; Introduction |
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Cross-ValidationDrawbacks of Splitting a Dataset Only Once; K-Fold Cross-Validation; Leave-One-Out Cross-Validation; Comparing the K-Fold and LOO Methods; Cross-Validation for Deep Learning Models; Keras Wrapper with scikit-learn; Exercise 11: Building the Keras Wrapper with scikit-learn for a Regression Problem; Cross-Validation with scikit-learn; Cross-Validation Iterators in scikit-learn; Exercise 12: Evaluate Deep Neural Networks with Cross-Validation; Activity 5: Model Evaluation Using Cross-Validation for a Diabetes Diagnosis Classifier; Model Selection with Cross-validation |
| Summary |
Applied Deep Learning with Keras takes you from a basic knowledge of machine learning and Python to an expert understanding of applying Keras to develop efficient deep learning solutions. This book teaches you new techniques to handle neural networks, and in turn, broadens your options as a data scientist |
| Notes |
Cross-Validation for Model Evaluation versus Model Selection |
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Print version record |
| Subject |
Python (Computer program language)
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Machine learning.
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Artificial intelligence.
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Neural networks & fuzzy systems.
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Programming & scripting languages: general.
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COMPUTERS -- Programming Languages -- Python.
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Machine learning
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Python (Computer program language)
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| Form |
Electronic book
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| Author |
Abdolahnejad, Mahla
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Moocarme, Matthew
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| ISBN |
1838554548 |
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9781838554545 |
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