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's best practices to implement smart ConvNet ...

Target dataset is small but different from the original training dataset.

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 draw…