Applied Deep Learning with Keras
Ritesh Bhagwat Mahla Abdolahnejad Matthew Moocarme更新时间：2021-06-11 13:41:45
最新章节：Chapter 9: Sequential Modeling with Recurrent Neural Networks封面
版权页
Preface
About the Book
Chapter 1 Introduction to Machine Learning with Keras
Introduction
Data Representation
Data Preprocessing
Machine Learning Libraries
scikit-learn
Keras
Model Training
Model Tuning
Summary
Chapter 2 Machine Learning versus Deep Learning
Introduction
Linear Transformations
Introduction to Keras
Summary
Chapter 3 Deep Learning with Keras
Introduction
Building Your First Neural Network
Model Evaluation
Summary
Chapter 4 Evaluate Your Model with Cross-Validation using Keras Wrappers
Introduction
Cross-Validation
Cross-Validation for Deep Learning Models
Model Selection with Cross-validation
Summary
Chapter 5 Improving Model Accuracy
Introduction
Regularization
L1 and L2 Regularization
Dropout Regularization
Other Regularization Methods
Hyperparameter Tuning with scikit-learn
Summary
Chapter 6 Model Evaluation
Introduction
Accuracy
Imbalanced Datasets
Confusion Matrix
Summary
Chapter 7 Computer Vision with Convolutional Neural Networks
Introduction
Computer Vision
Convolutional Neural Networks
Architecture of a CNN
Image Augmentation
Summary
Chapter 8 Transfer Learning and Pre-Trained Models
Introduction
Pre-Trained Sets and Transfer Learning
Fine-Tuning a Pre-Trained Network
Summary
Chapter 9 Sequential Modeling with Recurrent Neural Networks
Introduction
Sequential Memory and Sequential Modeling
Recurrent Neural Networks (RNNs)
Long Short-Term Memory (LSTM)
Summary
Appendix
Chapter 1: Introduction to Machine Learning with Keras
Chapter 2: Machine Learning versus Deep Learning
Chapter 3: Deep Learning with Keras
Chapter 4: Evaluate Your Model with Cross-Validation with Keras Wrappers
Chapter 5: Improving Model Accuracy
Chapter 6: Model Evaluation
Chapter 7: Computer Vision with Convolutional Neural Networks
Chapter 8: Transfer Learning and Pre-trained Models
Chapter 9: Sequential Modeling with Recurrent Neural Networks
更新时间：2021-06-11 13:41:45