In this section, we will learn about the Modified National Institute of Standards and Technology (MNIST) database data and build a simple classification model. The objective of this section is to learn the general framework for deep learning and use TensorFlow for the same. First, we will build a perceptron or logistic regression model. Then, we will train a CNN to achieve better accuracy. We will also see how TensorBoard helps visualize the training process and understand the parameters.
Deep Learning for Computer Vision
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Deep Learning for Computer Vision
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Overview of this book
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.
Table of Contents (12 chapters)
Preface
Free Chapter
Getting Started
Image Classification
Image Retrieval
Object Detection
Semantic Segmentation
Similarity Learning
Image Captioning
Generative Models
Video Classification
Deployment
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