Beginning deep learning with TensorFlow work with Keras, MNIST data sets, and advanced neural networks

Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learn...

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Detalles Bibliográficos
Otros Autores:	Long, Liangqu, author (author), Zeng, Xiangming, author
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	New York, New York : Apress L. P. [2022]
Materias:	TensorFlow. Machine learning.
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009645681706719

Tabla de Contenidos:

Intro
Table of Contents
About the Authors
About the Technical Reviewer
Acknowledgments
Chapter 1: Introduction to Artificial Intelligence
1.1 Artificial Intelligence in Action
1.1.1 Artificial Intelligence Explained
1.1.2 Machine Learning
1.1.3 Neural Networks and Deep Learning
1.2 The History of Neural Networks
1.2.1 Shallow Neural Networks
1.2.2 Deep Learning
1.3 Deep Learning Characteristics
1.3.1 Data Volume
1.3.2 Computing Power
1.3.3 Network Scale
1.3.4 General Intelligence
1.4 Deep Learning Applications
1.4.1 Computer Vision
1.4.2 Natural Language Processing
1.4.3 Reinforcement Learning
1.5 Deep Learning Framework
1.5.1 Major Frameworks
1.5.2 TensorFlow 2 and 1.x
1.5.3 Demo
1.6 Development Environment Installation
1.6.1 Anaconda Installation
1.6.2 CUDA Installation
1.6.3 TensorFlow Installation
1.6.4 Common Editor Installation
1.7 Summary
1.8 Reference
Chapter 2: Regression
2.1 Neuron Model
2.2 Optimization Method
2.3 Linear Model in Action
2.4 Summary
2.5 References
Chapter 3: Classification
3.1 Handwritten Digital Picture Dataset
3.2 Build a Model
3.3 Error Calculation
3.4 Do We Really Solve the Problem?
3.5 Nonlinear Model
3.6 Model Complexity
3.7 Optimization Method
3.8 Hands-On Handwritten Digital Image Recognition
3.8.1 Build the Network
3.8.2 Model Training
3.9 Summary
3.10 Reference
Chapter 4: Basic TensorFlow
4.1 Data Types
4.1.1 Numeric
4.1.2 String
4.1.3 Boolean
4.2 Numerical Precision
4.3 Tensors to Be Optimized
4.4 Create Tensors
4.4.1 Create Tensors from Arrays and Lists
4.4.2 Create All-0 or All-1 Tensors
4.4.3 Create a Customized Numeric Tensor
4.4.4 Create a Tensor from a Known Distribution
4.4.5 Create a Sequence.
4.5 Typical Applications of Tensors
4.5.1 Scalar
4.5.2 Vector
4.5.3 Matrix
4.5.4 Three-Dimensional Tensor
4.5.5 Four-Dimensional Tensor
4.6 Indexing and Slicing
4.6.1 Indexing
4.6.2 Slicing
4.6.3 Slicing Summary
4.7 Dimensional Transformation
4.7.1 Reshape
4.7.2 Add and Delete Dimensions
4.7.3 Swap Dimensions
4.7.4 Copy Data
4.8 Broadcasting
4.9 Mathematical Operations
4.9.1 Addition, Subtraction, Multiplication and Division
4.9.2 Power Operations
4.9.3 Exponential and Logarithmic Operations
4.9.4 Matrix Multiplication
4.10 Hands-On Forward Propagation
Chapter 5: Advanced TensorFlow
5.1 Merge and Split
5.1.1 Merge
5.1.2 Split
5.2 Common Statistics
5.2.1 Norm
5.2.2 Max, Min, Mean, and Sum
5.3 Tensor Comparison
5.4 Fill and Copy
5.4.1 Fill
5.4.2 Copy
5.5 Data Limiting
5.6 Advanced Operations
5.6.1 tf.gather
5.6.2 tf.gather_nd
5.6.3 tf.boolean_mask
5.6.4 tf.where
5.6.5 tf.scatter_nd
5.6.6 tf.meshgrid
5.7 Load Classic Datasets
5.7.1 Shuffling
5.7.2 Batch Training
5.7.3 Preprocessing
5.7.4 Epoch Training
5.8 Hands-On MNIST Dataset
Chapter 6: Neural Networks
6.1 Perceptron
6.2 Fully Connected Layer
6.2.1 Tensor Mode Implementation
6.2.2 Layer Implementation
6.3 Neural Network
6.3.1 Tensor Mode Implementation
6.3.2 Layer Mode Implementation
6.3.3 Optimization
6.4 Activation function
6.4.1 Sigmoid
6.4.2 ReLU
6.4.3 LeakyReLU
6.4.4 Tanh
6.5 Design of Output Layer
6.5.1 Common Real Number Space
6.5.2 [0, 1] Interval
6.5.3 [0,1] Interval with Sum 1
6.5.4 (-1, 1) Interval
6.6 Error Calculation
6.6.1 Mean Square Error Function
6.6.2 Cross-Entropy Error Function
6.7 Types of Neural Networks
6.7.1 Convolutional Neural Network
6.7.2 Recurrent Neural Network.
6.7.3 Attention Mechanism Network
6.7.4 Graph Convolutional Neural Network
6.8 Hands-On of Automobile Fuel Consumption Prediction
6.8.1 Dataset
6.8.2 Create a Network
6.8.3 Training and Testing
6.9 References
Chapter 7: Backward Propagation Algorithm
7.1 Derivatives and Gradients
7.2 Common Properties of Derivatives
7.2.1 Common Derivatives
7.2.2 Common Property of Derivatives
7.2.3 Hands-On Derivative Finding
7.3 Derivative of Activation Function
7.3.1 Derivative of Sigmoid Function
7.3.2 Derivative of ReLU Function
7.3.3 Derivative of LeakyReLU Function
7.3.4 Derivative of Tanh Function
7.4 Gradient of Loss Function
7.4.1 Gradient of Mean Square Error Function
7.4.2 Gradient of Cross-Entropy Function
7.5 Gradient of Fully Connected Layer
7.5.1 Gradient of a Single Neuron
7.5.2 Gradient of Fully Connected Layer
7.6 Chain Rule
7.7 Back Propagation Algorithm
7.8 Hands-On Optimization of Himmelblau
7.9 Hands-On Back Propagation Algorithm
7.9.1 Dataset
7.9.2 Network Layer
7.9.3 Network model
7.9.4 Network Training
7.9.5 Network Performance
7.10 References
Chapter 8: Keras Advanced API
8.1 Common Functional Modules
8.1.1 Common Network Layer Classes
8.1.2 Network Container
8.2 Model Configuration, Training, and Testing
8.2.1 Model Configuration
8.2.2 Model Training
8.2.3 Model Testing
8.3 Model Saving and Loading
8.3.1 Tensor Method
8.3.2 Network Method
8.3.3 SavedModel method
8.4 Custom Network
8.4.1 Custom Network Layer
8.4.2 Customized Network
8.5 Model Zoo
8.5.1 Load Model
8.6 Metrics
8.6.1 Create a Metrics Container
8.6.2 Write Data
8.6.3 Read Statistical Data
8.6.4 Clear the Container
8.6.5 Hands-On Accuracy Metric
8.7 Visualization
8.7.1 Model Side
8.7.2 Browser Side.
8.8 Summary
Chapter 9: Overfitting
9.1 Model Capacity
9.2 Overfitting and Underfitting
9.2.1 Underfitting
9.2.2 Overfitting
9.3 Dataset Division
9.3.1 Validation Set and Hyperparameters
9.3.2 Early Stopping
9.4 Model Design
9.5 Regularization
9.5.1 L0 Regularization
9.5.2 L1 Regularization
9.5.3 L2 Regularization
9.5.4 Regularization Effect
9.6 Dropout
9.7 Data Augmentation
9.7.1 Rotation
9.7.2 Flip
9.7.3 Cropping
9.7.4 Generate Data
9.7.5 Other Methods
9.8 Hands-On Overfitting
9.8.1 Build the Dataset
9.8.2 Influence of the Number of Network Layers
9.8.3 Impact of Dropout
9.8.4 Impact of Regularization
9.9 References
Chapter 10: Convolutional Neural Networks
10.1 Problems with Fully Connected N
10.1.1 Local Correlation
10.1.2 Weight Sharing
10.1.3 Convolution Operation
10.2 Convolutional Neural Network
10.2.1 Single-Channel Input and Single Convolution Kernel
10.2.2 Multi-channel Input and Single Convolution Kernel
10.2.3 Multi-channel Input and Multi-convolution Kernel
10.2.4 Stride Size
10.2.5 Padding
10.3 Convolutional Layer Implementation
10.3.1 Custom Weights
10.3.2 Convolutional Layer Classes
10.4 Hands-On LeNet-5
10.5 Representation Learning
10.6 Gradient Propagation
10.7 Pooling Layer
10.8 BatchNorm Layer
10.8.1 Forward Propagation
10.8.2 Backward Propagation
10.8.3 Implementation of BatchNormalization layer
10.9 Classical Convolutional Network
10.9.1 AlexNet
10.9.2 VGG Series
10.9.3 GoogLeNet
10.10 Hands-On CIFAR10 and VGG13
10.11 Convolutional Layer Variants
10.11.1 Dilated/Atrous Convolution
10.11.2 Transposed Convolution
o + 2p − k = n * s
o + 2p − k ≠n * s
Matrix Transposition
Transposed Convolution Implementation
10.11.3 Separate Convolution.
10.12 Deep Residual Network
10.12.1 ResNet Principle
10.12.2 ResBlock Implementation
10.13 DenseNet
10.14 Hands-On CIFAR10 and ResNet18
10.15 References
Chapter 11: Recurrent Neural Network
11.1 Sequence Representation Method
11.1.1 Embedding Layer
11.1.2 Pre-trained Word Vectors
11.2 Recurrent Neural Network
11.2.1 Is a Fully Connected Layer Feasible?
11.2.2 Shared Weight
11.2.3 Global Semantics
11.2.4 Recurrent Neural Network
11.3 Gradient Propagation
11.4 How to Use RNN Layers
11.4.1 SimpleRNNCell
11.4.2 Multilayer SimpleRNNCell Network
11.4.3 SimpleRNN Layer
11.5 Hands-On RNN Sentiment Classification
11.5.1 Dataset
11.5.2 Network Model
11.5.3 Training and Testing
11.6 Gradient Vanishing and Gradient Exploding
11.6.1 Gradient Clipping
11.6.2 Gradient Vanishing
11.7 RNN Short-Term Memory
11.8 LSTM Principle
11.8.1 Forget Gate
11.8.2 Input Gate
11.8.3 Update Memory
11.8.4 Output Gate
11.8.5 Summary
11.9 How to Use the LSTM Layer
11.9.1 LSTMCell
11.9.2 LSTM layer
11.10 GRU Introduction
11.10.1 Reset Door
11.10.2 Update Gate
11.10.3 How to Use GRU
11.11 Hands-On LSTM/GRU Sentiment Classification
11.11.1 LSTM Model
11.11.2 GRU model
11.12 Pre-trained Word Vectors
11.13 Pre-trained Word Vectors
11.14 References
Chapter 12: Autoencoder
12.1 Principle of Autoencoder
12.2 Hands-On Fashion MNIST Image Reconstruction
12.2.1 Fashion MNIST Dataset
12.2.2 Encoder
12.2.3 Decoder
12.2.4 Autoencoder
12.2.5 Network Training
12.2.6 Image Reconstruction
12.3 Autoencoder Variants
12.3.1 Dropout Autoencoder
12.3.2 Adversarial Autoencoder
12.4 Variational Autoencoder
12.4.1 Principle of VAE
12.4.2 Reparameterization Trick
12.5 Hands-On VAE Image Reconstruction
12.5.1 VAE model.
12.5.2 Reparameterization Trick.

Beginning deep learning with TensorFlow work with Keras, MNIST data sets, and advanced neural networks

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