A restricted Boltzmann machine (RBM) is an unsupervised model. As an undirected graphical model with two layers (observed and hidden), it is useful to learn a different representation of input data along with the hidden layer. This was the first structural building block of deep learning, particularly when the computational resources to learn about a deep neural net with backpropagation were not available (a stacked RBM was used instead). It restricts the connectivity of the network (only allowing a bipartite graph in between the hidden and observed set of nodes) to make inference easy. It is an energy-based model; the joint distribution is modeled using the energy function. To infer the most probable observation, we need to choose the one with the least energy. This model is generally trained on binary images...
Python Image Processing Cookbook
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Python Image Processing Cookbook
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Overview of this book
With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing.
With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems.
By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.
Table of Contents (11 chapters)
Preface
Image Manipulation and Transformation
Free Chapter
Image Enhancement
Image Restoration
Binary Image Processing
Image Registration
Image Segmentation
Image Classification
Object Detection in Images
Face Recognition, Image Captioning, and More
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