Book Image

Hands-On Convolutional Neural Networks with TensorFlow

By : Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo
Book Image

Hands-On Convolutional Neural Networks with TensorFlow

By: Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo

Overview of this book

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! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell


Research has shown that feature extraction in convolutional network weights trained on ImageNet outperforms the conventional feature extraction methods such as SURF, Deformable Part Descriptors (DPDs), Histogram of Oriented Gradients (HOG), and bag of words (BoW). This means that convolutional features can be used equally well wherever the conventional visual representations work, with the only drawback being that deeper architectures might require a longer time to extract the features.

When a deep convolutional neural network is trained on ImageNet the visualization of convolution filters in the first layers (refer to the following illustration) shows that they learn low-level features similar to edge detection filters, while the convolution filters at the last layers learn high-level features that capture the class-specific information. Hence, if we extract the features for ImageNet after the first pooling layer and embed them into a 2D space (using, for example, t-SNE), the visualization...