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Practical Convolutional Neural Networks
Thisbookisfordatascientists,machinelearninganddeeplearningpractitioners,CognitiveandArtificialIntelligenceenthusiastswhowanttomoveonestepfurtherinbuildingConvolutionalNeuralNetworks.Gethands-onexperiencewithextremedatasetsanddifferentCNNarchitecturestobuildefficientandsmartConvNetmodels.BasicknowledgeofdeeplearningconceptsandPythonprogramminglanguageisexpected.
目錄(166章)
倒序
- coverpage
- Title Page
- Packt Upsell
- Why subscribe?
- PacktPub.com
- Contributors
- About the authors
- About the reviewer
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- Deep Neural Networks – Overview
- Building blocks of a neural network
- Introduction to TensorFlow
- Installing TensorFlow
- For macOS X/Linux variants
- TensorFlow basics
- Basic math with TensorFlow
- Softmax in TensorFlow
- Introduction to the MNIST dataset
- The simplest artificial neural network
- Building a single-layer neural network with TensorFlow
- Keras deep learning library overview
- Layers in the Keras model
- Handwritten number recognition with Keras and MNIST
- Retrieving training and test data
- Flattened data
- Visualizing the training data
- Building the network
- Training the network
- Testing
- Understanding backpropagation
- Summary
- Introduction to Convolutional Neural Networks
- History of CNNs
- Convolutional neural networks
- How do computers interpret images?
- Code for visualizing an image
- Dropout
- Input layer
- Convolutional layer
- Convolutional layers in Keras
- Pooling layer
- Practical example – image classification
- Image augmentation
- Summary
- Build Your First CNN and Performance Optimization
- CNN architectures and drawbacks of DNNs
- Convolutional operations
- Pooling stride and padding operations
- Fully connected layer
- Convolution and pooling operations in TensorFlow
- Applying pooling operations in TensorFlow
- Convolution operations in TensorFlow
- Training a CNN
- Weight and bias initialization
- Regularization
- Activation functions
- Using sigmoid
- Using tanh
- Using ReLU
- Building training and evaluating our first CNN
- Dataset description
- Step 1 – Loading the required packages
- Step 2 – Loading the training/test images to generate train/test set
- Step 3- Defining CNN hyperparameters
- Step 4 – Constructing the CNN layers
- Step 5 – Preparing the TensorFlow graph
- Step 6 – Creating a CNN model
- Step 7 – Running the TensorFlow graph to train the CNN model
- Step 8 – Model evaluation
- Model performance optimization
- Number of hidden layers
- Number of neurons per hidden layer
- Batch normalization
- Advanced regularization and avoiding overfitting
- Applying dropout operations with TensorFlow
- Which optimizer to use?
- Memory tuning
- Appropriate layer placement
- Building the second CNN by putting everything together
- Dataset description and preprocessing
- Creating the CNN model
- Training and evaluating the network
- Summary
- Popular CNN Model Architectures
- Introduction to ImageNet
- LeNet
- AlexNet architecture
- Traffic sign classifiers using AlexNet
- VGGNet architecture
- VGG16 image classification code example
- GoogLeNet architecture
- Architecture insights
- Inception module
- ResNet architecture
- Summary
- Transfer Learning
- Feature extraction approach
- Target dataset is small and is similar to the original training dataset
- Target dataset is small but different from the original training dataset
- Target dataset is large and similar to the original training dataset
- Target dataset is large and different from the original training dataset
- Transfer learning example
- Multi-task learning
- Summary
- Autoencoders for CNN
- Introducing to autoencoders
- Convolutional autoencoder
- Applications
- An example of compression
- Summary
- Object Detection and Instance Segmentation with CNN
- The differences between object detection and image classification
- Why is object detection much more challenging than image classification?
- Traditional nonCNN approaches to object detection
- Haar features cascading classifiers and the Viola-Jones algorithm
- Haar Features
- Cascading classifiers
- The Viola-Jones algorithm
- R-CNN – Regions with CNN features
- Fast R-CNN – fast region-based CNN
- Faster R-CNN – faster region proposal network-based CNN
- Mask R-CNN – Instance segmentation with CNN
- Instance segmentation in code
- Creating the environment
- Installing Python dependencies (Python2 environment)
- Downloading and installing the COCO API and detectron library (OS shell commands)
- Preparing the COCO dataset folder structure
- Running the pre-trained model on the COCO dataset
- References
- Summary
- GAN: Generating New Images with CNN
- Pix2pix - Image-to-Image translation GAN
- CycleGAN
- Training a GAN model
- GAN – code example
- Calculating loss
- Adding the optimizer
- Semi-supervised learning and GAN
- Feature matching
- Semi-supervised classification using a GAN example
- Deep convolutional GAN
- Batch normalization
- Summary
- Attention Mechanism for CNN and Visual Models
- Attention mechanism for image captioning
- Types of Attention
- Hard Attention
- Soft Attention
- Using attention to improve visual models
- Reasons for sub-optimal performance of visual CNN models
- Recurrent models of visual attention
- Applying the RAM on a noisy MNIST sample
- Glimpse Sensor in code
- References
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-24 18:59:19
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