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.