Hands-On Convolutional Neural Networks with TensorFlow : Solve Computer Vision Problems with Modeling in TensorFlow and Python.
Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!
Record details
- ISBN: 9781789132823
- ISBN: 1789132827
- Physical Description: 1 online resource (264 pages)
- Publisher: Birmingham : Packt Publishing Ltd, 2018.
Content descriptions
General Note: | Substituting the 3x3 convolution |
Formatted Contents Note: | Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Setup and Introduction to TensorFlow; The TensorFlow way of thinking; Setting up and installing TensorFlow; Conda environments; Checking whether your installation works; TensorFlow API levels; Eager execution; Building your first TensorFlow model; One-hot vectors; Splitting into training and test sets; Creating TensorFlow graphs; Variables; Operations; Feeding data with placeholders; Initializing variables; Training our model; Loss functions; Optimization; Evaluating a trained model The sessionSummary; Chapter 2: Deep Learning and Convolutional Neural Networks; AI and ML; Types of ML; Old versus new ML; Artificial neural networks; Activation functions; The XOR problem; Training neural networks; Backpropagation and the chain rule; Batches; Loss functions; The optimizer and its hyperparameters; Underfitting versus overfitting; Feature scaling; Fully connected layers; A TensorFlow example for the XOR problem; Convolutional neural networks; Convolution; Input padding; Calculating the number of parameters (weights); Calculating the number of operations Converting convolution layers into fully connected layersThe pooling layer; 1x1 Convolution; Calculating the receptive field; Building a CNN model in TensorFlow; TensorBoard; Other types of convolutions; Summary; Chapter 3: Image Classification in TensorFlow; CNN model architecture; Cross-entropy loss (log loss); Multi-class cross entropy loss; The train/test dataset split; Datasets; ImageNet; CIFAR; Loading CIFAR; Image classification with TensorFlow; Building the CNN graph; Learning rate scheduling; Introduction to the tf.data API; The main training loop; Model Initialization Do not initialize all weights with zerosInitializing with a mean zero distribution; Xavier-Bengio and the Initializer; Improving generalization by regularizing; L2 and L1 regularization; Dropout; The batch norm layer; Summary; Chapter 4: Object Detection and Segmentation; Image classification with localization; Localization as regression; TensorFlow implementation; Other applications of localization; Object detection as classification -- Sliding window; Using heuristics to guide us (R-CNN); Problems; Fast R-CNN; Faster R-CNN; Region Proposal Network; RoI Pooling layer Conversion from traditional CNN to Fully ConvnetsSingle Shot Detectors -- You Only Look Once; Creating training set for Yolo object detection; Evaluating detection (Intersection Over Union); Filtering output; Anchor Box; Testing/Predicting in Yolo; Detector Loss function (YOLO loss); Loss Part 1; Loss Part 2; Loss Part 3; Semantic segmentation; Max Unpooling; Deconvolution layer (Transposed convolution); The loss function; Labels; Improving results; Instance segmentation; Mask R-CNN; Summary; Chapter 5: VGG, Inception Modules, Residuals, and MobileNets; Substituting big convolutions |
Restrictions on Access Note: | NLC staff and students only. |
Source of Description Note: | Print version record. |
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Subject: | Neural networks (Computer science) > Computer simulation. |