This book is mostly for software developers who want to use machine learning on photographs. It’s for developers who want to solve typical computer vision problems with Tensor Flow and Keras. This book will teach you how to use popular, well-tested prebuilt models written in Tensor Flow and Keras to create ML architectures for computer vision tasks and train models. You’ll also pick out some tips on how to increase your accuracy and explain ability. This book also includes a complete end-to-end guide to deep learning. It can be used as a stepping stone to deeper learning domains like natural language processing. The goal of this book is to provide clear explanations of the machine learning architectures that underpin this rapidly evolving field, as well as practical code to use these models to solve problems involving classification, measurement, detection, segmentation, representation, generation, and counting. Finally, you’ll learn how to create, implement, and tune end-to-end machine learning pipelines for image interpretation tasks.


Machine Learning:

Machine learning is the study of computer algorithms that can learn and develop on their own with experience and data. It’s considered a type of artificial intelligence. This can also be defined as the usage and development of computer systems that can learn and adapt without explicit instructions by analyzing and drawing inferences from data patterns utilizing algorithms and statistical models.

Computer Vision:

Computer vision is a branch of artificial intelligence that allows computers and systems to extract useful information from digital photos, videos, and other visual inputs, as well as to conduct actions or make recommendations based on that data. If artificial intelligence allows computers to think, computer vision allows them to see, watch, and comprehend.

The Relationship between Machine learning and computer vision:

Machine learning and computer vision are two fields that have grown increasingly inextricably linked. Computer vision has advanced in terms of recognition and tracking thanks to machine learning. It provides efficient capture, image processing, and object focus methods for computer vision.

Machine Learning for Computer Vision:

The goal of using computer vision technologies in machine learning and artificial intelligence is to construct a model that can work without human involvement. The entire procedure include obtaining data, processing, analyzing, and comprehending digital images in order to use them in a real-world context. Computer vision is used in deep learning to examine various sorts of data sets using annotated images that show the object of interest in the image. It can discover patterns in visual data to help supervised machine learning algorithms learn from thousands or millions of photos that have been labelled.

Organization of the Book:

  • In Chapter 2, we cover how to read images with machine learning, as well as how to train, assess, and predict with ML models. The models we examine in Chapter two are generic and hence don’t function well on photos, but the concepts covered here are crucial for the rest of the book.

  • We introduce various machine learning models that operate well on photos in Chapter 3. We begin with transfer learning and fine-tuning before moving on to a range of convolutional models that become more sophisticated as we progress through the chapter.

  • In Chapter 4, we look at how computer vision can help with object detection and image segmentation. In Chapter 4, any of the backbone designs introduced in Chapter 3 can be employed.

  • We go into the mechanics of constructing production computer vision machine learning models in Chapters 5 through 9. We look at dataset creation in Chapter 5, preprocessing in Chapter 6, training in Chapter 7, monitoring and evaluation in Chapter 8, and deployment in Chapter 9 of the standard ML pipeline.

  • Three emerging tendencies are discussed in Chapter 10. We then try out a no-code image classification system that can be used for quick prototyping and as a benchmark for more custom models by connecting all of the procedures covered in Chapters 5 through 9 into an end-to-end, containerized ML pipeline. Finally, we demonstrate how image model predictions can be made more explainable.

  • We show how the fundamental building blocks of computer vision are utilized to tackle a range of issues in Chapters 11 and 12, including image production, counting, pose identification, and more. These sophisticated use scenarios are also covered by implementations.

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