
Principles and Labs for Deep Learning
Description
Key Features
- Introduces readers to the usefulness of neural networks and Deep Learning methods
- Provides readers with in-depth understanding of the architecture and operation of Deep Convolutional Neural Networks
- Demonstrates the visualization needed for designing neural networks
- Provides readers with an in-depth understanding of regression problems, binary classification problems, multi-category classification problems, Variational Auto-Encoder, Generative Adversarial Network, and Object detection
Readership
Table of Contents
Chapter 0: Environment Installation
0.1. Python Installation
0.2. TensorFlow Installation
0.3. Python extension installation
0.4. Jupyter Notebook
0.5. GitHub code
0.6. PyCharm IDEChapter 1: Introduction to TensorFlow2.0
1.1. What is Deep Learning
1.2. Create a new project
1.3. Introduction to TensorFlow
1.4. Eager Execution
1.4.1. Introduction to Eager Execution
1.4.2. Basic operations
1.5. Keras
1.5.1. Introduction to Keras
1.5.2. Sequential Model
1.5.3. Functional API
1.6. tf.data
1.6.1. Introduction to tf.data
1.6.2. Basic OperationsChapter 2: Regression Problem
2.1. Deep Neural Network
2.1.1. A brief history of neural networks
2.1.2. Principle of Neural Network
2.1.3. Fully connected
2.1.4. MSE and MAE Loss functions
2.1.5. Neural network weight update
2.1.6. Neural network training steps
2.2. Introduction to Kaggle
2.3. Experiment 1: House price prediction model
2.3.1. Introduction to the dataset
2.3.2. Create Project
2.3.3. Source code
2.4. Introduction to TensorBoard
2.5. Experiment 2: Overfitting
2.5.1. Introduction to the dataset
2.5.2. Create Project
2.5.3. Source codeChapter 3: Binary classification problem
3.1. Machine Learning Algorithms
3.2. Binary Classification Problem
3.2.1. Logistic Regression
3.2.2. Sigmoid
3.2.3. Binary Cross-Entropy
3.2.4. One-hot Encoding
3.3. Experiment: Pokémon Battle Prediction
3.3.1. Introduction to the dataset
3.3.2. Create Project
3.3.3. Source codeChapter 4: Multi-category Classification Problem
4.1. Convolutional Neural Network
4.1.1. Introduction to Convolutional Neural Network
4.1.2. The architecture of Convolutional Neural Network
4.1.3. Principle of Convolutional Neural Network
4.2. Multi-category Classification Problem
4.2.1. Softmax
4.2.2. Categorical Cross-Entropy
4.2.3. Data enhancement
4.3. Experiment: CIFAR-10 image recognition
4.3.1. Introduction to the dataset
4.3.2. TensorFlow Datasets
4.3.3. Create Project
4.3.4. Source codeChapter 5: Training Neural Network
5.1. Backpropagation
5.2. Weight Initialization
5.2.1. Normal Distribution
5.2.2. Xavier/Glorot initialization
5.2.3. He initialization
5.3. Batch Normalization
5.3.1. Introduction to Batch Normalization
5.3.2. Neural Network with Batch Normalization
5.4. Experiment 1: Using CIFAR-10 dataset to verify three weight initialization methods
5.5. Experiment 2: Using CIFAR-10 dataset to verify Batch Normalization method
5.6. Performance Comparison of Different Neural NetworksChapter 6: Advanced TensorFlow2.0
6.1. Advanced TensorFlow
6.1.1. Customized network layer
6.1.2. Customized loss function
6.1.3. Customized metric function
6.1.4. Customized callback function
6.2. Comparison of high-level Keras API and customized API
6.2.1. Network Layer
6.2.2. Loss Function
6.2.3. Metric Function
6.2.4. Callback Function
6.3. Experiment: Implementation of two network models using high-level Keras API and Customized API
6.3.1. Create Project
6.3.2. Source CodeChapter 7: Advanced TensorBoard
7.1. Advanced TensorBoard
7.1.1. tf.summary
7.1.2. tf.summary.scalar
7.1.3. tf.summary.image
7.1.4. tf.summary.text
7.1.5. tf.summary.audio
7.1.6. tf.summary.histogram
7.2. Experiment 1: Using tf.summary.image to visualize training results
7.2.1. Create a project
7.2.2. Source Code
7.3. Experiment 2: Using hyperparameter tuning with TensorBoard HParams to analyze multiple training models 7.3.1. Open TensorBoard
7.3.2. Source CodeChapter 8: Convolutional Neural Network Architectures
8.1. Most Popular Convolutional Neural Network Architectures
8.1.1. LeNet
8.1.2. AlexNet
8.1.3. VGG
8.1.4. GoogLeNet
8.1.5. ResNet
8.1.6. Comparison of network architectures
8.2. Experiment: Implementation of InceptionV3 network architecture
8.2.1. Create a project
8.2.2. Keras Applications
8.2.3. TensorFlow HubChapter 9: Transfer Learning
9.1. Transfer Learning
9.1.1. Introduction to Transfer Learning
9.1.2. Transfer learning methods
9.2. Experiment: Using Inception V3 for Transfer Learning
9.2.1. Create Project
9.2.2. Introduction to the Dataset
9.2.3. Source CodeChapter 10: Variational Auto-Encoder
10.1. Introduction to Auto-Encoder
10.2. Introduction to Variational Auto-Encoder
10.3. Variational Auto-Encoder loss function
10.4. Experiment: Implementation of Variational Auto-Encoder Model
10.4.1. Create Project
10.4.2. Introduction to Dataset
10.4.3. Building Variational Auto-Encoder model.
10.4.4. Training Variational Auto-Encoder modelChapter 11: WGAN-GP
11.1. Generative Adversarial Network
11.1.1. Introduction to Generative Adversarial Network
11.1.2. Generative Adversarial Network Training and Loss Function
11.2. Evaluation of GAN, WGAN, and WGAN-GP
11.2.1. Problems of Generative Adversarial Network
11.2.2. Introduction to Wasserstein distance
11.2.3. WGAN-GP Loss Function
11.3. Experiment:Implementation of WGAN-GP
11.3.1. Create Project
11.3.2. Introduction to Dataset
11.3.3. Explanation of WGAN-GP ProjectChapter 12: Object Detection
12.1. Computer Vision
12.2. Introduction to Object Detection
12.3. Historical Progress of Object Detection
12.3.1. R-CNN
12.3.2. Fast R-CNN
12.3.3. Faster R-CNN
12.3.4. YOLO v1
12.3.5. SSD
12.3.6. YOLO v2
12.3.7. FPN
12.3.8. RetinaNet
12.3.9. YOLO v3 12.3.10. CFF-SSD
12.3.11. DSNet
12.4. Experiment: Implementation of YOLO v3
12.4.1. Load project
12.4.2. Introduction to Dataset
12.4.3. Explanation of YOLO v3 project
12.4.4. YOLO v3 Training and Testing
Product details
- No. of pages: 366
- Language: English
- Copyright: © Academic Press 2021
- Published: June 25, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780323901987
- eBook ISBN: 9780323901994
About the Authors
Shih-Chia Huang
Affiliations and Expertise
Trung-Hieu Le
Affiliations and Expertise
Ratings and Reviews
Latest reviews
(Total rating for all reviews)
Anderson Wed Aug 18 2021
Impressive works
This book provides a balance between theory and implementation. the source code for each topic is explained in detail in each chapter and available on Github for download.
Jeff Wed Aug 18 2021
nice book
If you are looking for a practical book to explain how to get results quickly in designing deep learning models, this is a book that you are in need.
mahmood Thu Jul 22 2021
Detailed Coverage with Examples
I am a master's student studying computer vision. This book is very useful for me, the contents and examples are clear and intuitive.
Yu L. Thu Jul 22 2021
Good !!!
The book has 12 chapters, each chapter has one corresponding Lab. The source codes can run step-by-step using Jupyter Notebook, it is very clear and easy to understand.
Simon Mon Jul 05 2021
Recommended
This book explains in detail the knowledge and skills of deep learning, so that readers can thoroughly understand the truth.
Sam Mon Jul 05 2021
Amazing book!
I'm very happy with the book and its contents. This book is a resource to learn how to actually implement deep learning models or gain intuition on how they work in practice.
lms83549@cuoly.com Sat Jul 03 2021
Good
This book is for someone who is interested in Deep Learning. If you are interested in implementation details and writing code, it is suggested to read this book.
Hoang Q. Fri Jul 02 2021
Excellence Deep learning book
Loved the way book is written, the contents are explained clear and intuitively through pictures, flowchart, and step-by-step source codes.
Viet Fri Jul 02 2021
Great book for Deep learning and Computer Vision
I want to learn deep learning from a comprehensive source, and easy to understand. This book is which I am looking for.
Scott L. Thu Jul 01 2021
Great contents written by excellent scholars
This is a superb book for people learning deep learning by practical and academic perspective. A comprehensive technical and theory book from how to create the development. environment, basic CNN,transfer learning, further to advance topics such as GAN, Object detection. It's good coverage for beginners, intermediate, and advance. Strongly recommend to buy and read it now!