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
Contributors
Preface
Index

Bias and Variance


Variance and Bias is another way of saying overfitting and underfitting respectively, as discussed in Chapter 2Deep Learning and Convolutional Neural Networks. We can diagnose the problem of "underfitting" and "overfitting" using the train set, dev set and test set errors.

Consider the following scenario where we have data coming from two different distributions named as Distribution 1 and Distribution 2. Distribution 2 represents the target application which we care about. The question is, how do we define train, dev and test sets on such distributions.

The best way to do so is to split it according to the preceding figure. Distribution 1 is split in to train set and part of it is used as the dev set. Here we are calling it the "Train-Dev set" ( because the dev set has same distribution as train set). Distribution 1 is used mainly for training as it is a large dataset. Distribution 2 is split into test set and dev set which are independent of either sets from Distribution...