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Practical Convolutional Neural Networks : Implement advanced deep learning models using Python /
This book helps you master CNN, from the basics to the most advanced concepts in CNN such as GANs, instance classification and attention mechanism for vision models and more. You will implement advanced CNN models using complex image and video datasets. By the end of the book you will learn CNN'...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Deep Neural Networks â#x80;#x93; 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 dataFlattened data; Visualizing the training data; Building the network; Training the network; Testing; Understanding backpropagation ; Summary; Chapter 2: 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 â#x80;#x93; image classification; Image augmentation; Summary; Chapter 3: Build Your First CNN and Performance Optimization.
- CNN architectures and drawbacks of DNNsConvolutional 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 â#x80;#x93; Loading the required packages; Step 2 â#x80;#x93; Loading the training/test images to generate train/test set.
- Step 3- Defining CNN hyperparametersStep 4 â#x80;#x93; Constructing the CNN layers; Step 5 â#x80;#x93; Preparing the TensorFlow graph; Step 6 â#x80;#x93; Creating a CNN model; Step 7 â#x80;#x93; Running the TensorFlow graph to train the CNN model; Step 8 â#x80;#x93; 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 preprocessingCreating the CNN model; Training and evaluating the network; Summary; Chapter 4: 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; Chapter 5: Transfer Learning; Feature extraction approach; Target dataset is small and is similar to the original training dataset.