Deep Learning for Computer Vision

<Deep Learning for Computer Vision> ▶Book Description
Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning.

In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.

▶What You Will Learn
⦁ Set up an environment for deep learning with Python, TensorFlow, and Keras
⦁ Define and train a model for image and video classification
⦁ Use features from a pre-trained Convolutional Neural Network model for image retrieval
⦁ Understand and implement object detection using the real-world Pedestrian Detection scenario
⦁ Learn about various problems in image captioning and how to overcome them by training images and text together
⦁ Implement similarity matching and train a model for face recognition
⦁ Understand the concept of generative models and use them for image generation
⦁ Deploy your deep learning models and optimize them for high performance

▶Key Features
⦁ Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision
⦁ Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more
⦁ Includes tips on optimizing and improving the performance of your models under various constraints

▶Who This Book Is For
The reader wants to know how to apply deep learning to computer vision problems such as classification, detection, retrieval, segmentation, generation, captioning, and video classification. The reader also wants to understand how to achieve good accuracy under various constraints such as less data, imbalanced classes, and noise. Then the reader also wants to know how to deploy trained models on various platforms (AWS, Google Cloud, Raspberry Pi, and mobile phones). After completing this book, the reader should be able to develop code for problems of person detection, face recognition, product search, medical image segmentation, image generation, image captioning, video classification, and so on.

▶What this book covers
⦁ Chapter 1, Getting Started, introduces the basics of deep learning and makes the readers familiar with the vocabulary. The readers will install the software packages necessary to follow the rest of the chapters.
⦁ Chapter 2, Image Classification, talks about the image classification problem, which is labeling an image as a whole. The readers will learn about image classification techniques and train a deep learning model for pet classification. They will also learn methods to improve accuracy and dive deep into variously advanced architectures.
⦁ Chapter 3, Image Retrieval, covers deep features and image retrieval. The reader will learn about various methods of obtaining model visualization, visual features, inference using TensorFlow, and serving and using visual features for product retrieval.
⦁ Chapter 4, Object Detection, talks about detecting objects in images. The reader will learn about various techniques of object detection and apply them for pedestrian detection. The TensorFlow API for object detection will be utilized in this chapter.
⦁ Chapter 5, Semantic Segmentation, covers segmenting of images pixel-wise. The readers will earn about segmentation techniques and train a model for segmentation of medical images.
⦁ Chapter 6, Similarity Learning, talks about similarity learning. The readers will learn about similarity matching and how to train models for face recognition. A model to train facial landmark is illustrated.
⦁ Chapter 7, Image Captioning, is about generating or selecting captions for images. The readers will learn natural language processing techniques and how to generate captions for images using those techniques.
⦁ Chapter 8, Generative Models, talks about generating synthetic images for various purposes. The readers will learn what generative models are and use them for image generation applications, such as style transfer, training data, and so on.
⦁ Chapter 9, Video Classification, covers computer vision techniques for video data. The readers will understand the key differences between solving video versus image problems and implement video classification techniques.
⦁ Chapter 10, Deployment, talks about the deployment steps for deep learning models. The reader will learn how to deploy trained models and optimize for speed on various platforms.