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sewak m practical convolutional neural networks python 2018

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Textbook in PDF format Preface 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 Index

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