Applied Deep Learning with Keras :: Solve Complex Real-Life Problems with the Simplicity of Keras.
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 sci...
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| Main Author: | |
|---|---|
| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing, Limited,
2019.
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| 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. |
| Item Description: | Cross-Validation for Model Evaluation versus Model Selection |
| Physical Description: | 1 online resource (412 pages) |
| ISBN: | 1838554548 9781838554545 |
Staff View
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| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 500 | |a Cross-Validation for Model Evaluation versus Model Selection | ||
| 520 | |a 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. | ||
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| 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 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 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 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 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 |
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| indexdate | 2025-04-11T08:46:53Z |
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| spelling | Bhagwat, Ritesh. Applied Deep Learning with Keras : Solve Complex Real-Life Problems with the Simplicity of Keras. Birmingham : Packt Publishing, Limited, 2019. 1 online resource (412 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. 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 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 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 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 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 Cross-Validation for Model Evaluation versus Model Selection 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. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Langage de programmation) Apprentissage automatique. Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Programming & scripting languages: general. bicssc COMPUTERS Programming Languages Python. bisacsh Machine learning fast Python (Computer program language) fast Abdolahnejad, Mahla. Moocarme, Matthew. Print version: Bhagwat, Ritesh. Applied Deep Learning with Keras : Solve Complex Real-Life Problems with the Simplicity of Keras. Birmingham : Packt Publishing, Limited, ©2019 9781838555078 |
| spellingShingle | Bhagwat, Ritesh Applied Deep Learning with Keras : Solve Complex Real-Life Problems with the Simplicity of Keras. 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 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 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 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 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 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Langage de programmation) Apprentissage automatique. Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Programming & scripting languages: general. bicssc COMPUTERS Programming Languages Python. bisacsh Machine learning fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh85079324 |
| title | Applied Deep Learning with Keras : Solve Complex Real-Life Problems with the Simplicity of Keras. |
| title_auth | Applied Deep Learning with Keras : Solve Complex Real-Life Problems with the Simplicity of Keras. |
| title_exact_search | Applied Deep Learning with Keras : Solve Complex Real-Life Problems with the Simplicity of Keras. |
| title_full | Applied Deep Learning with Keras : Solve Complex Real-Life Problems with the Simplicity of Keras. |
| title_fullStr | Applied Deep Learning with Keras : Solve Complex Real-Life Problems with the Simplicity of Keras. |
| title_full_unstemmed | Applied Deep Learning with Keras : Solve Complex Real-Life Problems with the Simplicity of Keras. |
| title_short | Applied Deep Learning with Keras : |
| title_sort | applied deep learning with keras solve complex real life problems with the simplicity of keras |
| title_sub | Solve Complex Real-Life Problems with the Simplicity of Keras. |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Langage de programmation) Apprentissage automatique. Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Programming & scripting languages: general. bicssc COMPUTERS Programming Languages Python. bisacsh Machine learning fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Machine learning. Python (Langage de programmation) Apprentissage automatique. Artificial intelligence. Neural networks & fuzzy systems. Programming & scripting languages: general. COMPUTERS Programming Languages Python. Machine learning |
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