Hands-On Convolutional Neural Networks with TensorFlow

Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification image recognition object detection and many computer vision challenges.Key FeaturesLearn the fundamentals of Convolutional Neural NetworksHarness Python and Tensorflow to train CNNsBuild scalable deep learning models that can process millions of itemsBook DescriptionConvolutional 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. What you will learnTrain machine learning models with TensorFlowCreate systems that can evolve and scale during their life cycleUse CNNs in image recognition and classificationUse TensorFlow for building deep learning models Train popular deep learning modelsFine-tune a neural network to improve the quality of results with transfer learningBuild TensorFlow models that can scale to large datasets and systemsWho this book is forThis book is for Software Engineers Data Scientists or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts linear algebra and Python will help. About the Author 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 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.Richard Burton graduated from the University of Leicester with a master's degree in mathematics. After graduating he worked as a research engineer at the University of Leicester for a number of years where he developed deep learning object detection models for their industrial partners. Now he is working as a software engineer in the industry where he continues to research the applications of deep learning in computer vision.Nimesh Patel graduated from the University of Leicester with an MSc in applied computation and numerical modeling. During this time a project collaboration with one of University of Le