Principles and Labs for Deep Learning

Principles and Labs for Deep Learning
Copyright
Preface
Environment installation
	Python installation
		Windows environment
		Ubuntu environment
	TensorFlow installation
		Windows environment
		Ubuntu environment
	Python extension installation
	Jupyter notebook
		Jupyter notebook installation
		Setup and create new project
		Jupyter Notebook operation
	PyCharm IDE
		PyCharm installation
		Setup and create new project
		PyCharm keyboard shortcuts
	GitHub labs
		Download source codes
		Open and run source code
Introduction to TensorFlow 2
	Deep learning
		Introduction to deep learning
		Deep learning toolkits
	Introduction to TensorFlow
	Improvement of TensorFlow 2
	Eager execution
		Introduction to eager execution
		Basic TensorFlow operations
	Keras
		Introduction to Keras
		Sequential model
		Functional model
	Tf.data
		Introduction to tf.data
		Basic functions of tf.data API
	References
Neural networks
	Introduction to neural networks
		A brief history of neural networks
		Principle of neural networks
		Training neural networks
	Introduction to Kaggle
		Kaggle platform
		House sales in King County dataset
	Experiment 1: House price prediction
		Preparing dataset
		Building and training network model
		Displaying training results
	Introduction to TensorBoard
	Experiment 2: Overfitting problem
		Introduction to overfitting
		Code examples
		Visualization with TensorBoard
	References
Binary classification problem
	Machine learning algorithms
	Binary classification problem
		Introduction to binary classification
		Binary classification model
	Experiment: Pokémon combat prediction
		Introduction to Pokémon-Weedles cave dataset
		Code examples
	References
Multi-category classification problem
	Convolutional neural network
		Introduction to convolutional neural network
		Building a convolutional neural network
		Operation of convolutional neural network
	Multi-category classification
		Introduction to multi-category classification
		Multi-category classification model
		Data augmentation
	Experiment: CIFAR-10 image classification
		Introduction to CIFAR-10 dataset
		TensorFlow datasets
		Code examples
	References
Training neural network
	Backpropagation
		Introduction to backpropagation
		Vanishing gradient problem
	Weight initialization
		Normal Distribution
		Glorot initialization
		He initialization
	Batch normalization
		Introduction to batch normalization
		Neural network with batch normalization
	Experiment 1: Verification of three weight initialization methods
		Code examples
		Visualizing weight distribution with TensorBoard
	Experiment 2: Verification of batch normalization
	Comparison of different neural networks
	References
Advanced TensorFlow
	Advanced TensorFlow
		Custom network layer
		Custom loss function
		Custom metric function
		Custom callback function
	Using high-level keras API and custom API of TensorFlow
		Network layer
		Loss function
		Metric function
		Callback function
	Experiment: implementation of two network models using high-level keras API and custom API
Advanced TensorBoard
	Advanced TensorBoard
		tf.summary.scalar
		tf.summary.image
		tf.summary.text
		tf.summary.audio
		tf.summary.histogram
	Experiment 1: Using tf.summary.image API to visualize training results
	Experiment 2: Hyperparameter tuning with TensorBoard HParams
		HPARAMS dashboard on TensorBoard
		Code examples
Convolutional neural network architectures
	Popular convolutional neural network architectures
		LeNet
		AlexNet
		VGG
		GoogLeNet
		ResNet
		Comparison of network architectures
	Experiment: Implementation of inception-v3 network
		Keras applications
		TensorFlow Hub
	References
Transfer learning
	Transfer learning
		Introduction to transfer learning
		Transfer learning strategies
	Experiment: Using Inception-v3 for transfer learning
		Introduction to Dogs vs. Cats dataset
		Code examples
	References
Variational auto-encoder
	Introduction to auto-encoder
	Introduction to variational auto-encoder
		Introduction to VAE
		Operation of VAE
		Variational auto-encoder loss function
	Experiment: Implementation of variational auto-encoder model
		Create project
		Introduction to the dataset
		Building a Variational auto-encoder model
	References
Generative adversarial network
	Generative adversarial network
		Introduction to generative adversarial network
		Training generative adversarial network
	Introduction to WGAN-GP
		Drawbacks of generative adversarial network
		Introduction to Wasserstein distance
		Training WGAN-GP
	Experiment: Implementation of WGAN-GP
		Create project
		Introduction to dataset
		Building WGAN-GP model
	References
Object detection
	Computer vision
	Introduction to object detection
	Object detection methods
		R-CNN
		Fast R-CNN
		Faster R-CNN
		YOLO-v1
		SSD
		YOLO-v2
		Feature pyramid networks
		RetinaNet
		YOLO-v3
		CFF-SSD
		DSNet
	Experiment: Implementation of YOLO-v3
		Load YOLO-v3 project
		Introduction to dataset
		Building YOLO-v3 model
	References
Index
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