Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python
Authors: Iffat Zafar – Giounona Tzanidou – Richard Burton – Nimesh Patel – Leonardo Araujo
ISBN-10: 1789130336
ISBN-13: 9781789130331
Publication Date 出版日期: 2018-08-28
Print Length 页数: 272 pages
Book Description
By finelybook
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.
Contents
1: SETUP AND INTRODUCTION TO TENSORFLOW
2: DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS
3: IMAGE CLASSIFICATION IN TENSORFLOW
4: OBJECT DETECTION AND SEGMENTATION
5: VGG,INCEPTION MODULES,RESIDUALS,AND MOBILENETS
6: AUTOENCODERS,VARIATIONAL AUTOENCODERS,AND GENERATIVE ADVERSARIAL NETWORKS
7: TRANSFER LEARNING
8: MACHINE LEARNING BEST PRACTICES AND TROUBLESHOOTING
9: TRAINING AT SCALE
What You Will Learn
Train machine learning models with TensorFlow
Create systems that can evolve and scale during their life cycle
Use CNNs in image recognition and classification
Use TensorFlow for building deep learning models
Train popular deep learning models
Fine-tune a neural network to improve the quality of results with transfer learning
Build TensorFlow models that can scale to large datasets and systems
Authors
Iffat Zafar
Iffat Zafar was born in Pakistan. She received her Ph.D. from the Loughborough University in Computer Vision and Machine Learning in 2008. After her Ph.D. in 2008,she worked as research associate at the Department of Computer Science,Loughborough University,for about 4 years. She currently works in the industry as an AI engineer,researching and developing algorithms using Machine Learning and Deep Learning for object detection and general Deep Learning tasks for edge and cloud-based applications.
Giounona Tzanidou
Giounona Tzanidou is a PhD in computer vision from Loughborough University,UK,where she developed algorithms for runtime surveillance video analytics. Then,she worked as a research fellow at Kingston University,London,on a project aiming at prediction detection and understanding of terrorist interest through intelligent video surveillance. She was also engaged in teaching computer vision and embedded systems modules at Loughborough University. Now an engineer,she investigates the application of deep learning techniques for object detection and recognition in videos.
