Book Image

Practical Convolutional Neural Networks

By : Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari
Book Image

Practical Convolutional Neural Networks

By: Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari

Overview of this book

Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.
Table of Contents (11 chapters)

Feature extraction approach

In a feature extraction approach, we train only the top level of the network; the rest of the network remains fixed. Consider a feature extraction approach when the new dataset is relatively small and similar to the original dataset. In such cases, the higher-level features learned from the original dataset should transfer well to the new dataset.

Consider a fine-tuning approach when the new dataset is large and similar to the original dataset. Altering the original weights should be safe because the network is unlikely to overfit the new, large dataset.

Let us consider a pre-trained convolutional neural network, as shown in the following diagram. Using this we can study how the transfer of knowledge can be used in different situations:

When should we use transfer learning? Transfer learning can be applied in the following situations, depending...