Practical Convolutional Neural Networks: Implement advanced deeplearning 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'sbest practices to implement smart ConvNet ...

Target dataset is small but different from the original trainingdataset.

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 inthe Keras model; Handwritten number recognition with Keras and MNIST.

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.

Dataset description a…