Why is deep learning important in computer vision?

Deep learning methods can achieve state-of-the-art results on challenging computer vision problems such as image classification, object detection, and face recognition. In this new Ebook written in the friendly Machine Learning Mastery style that you’re used to, skip the math and jump straight to getting results.

Why do deep learning methods outperform everyone else in computer vision in most tasks?

Deep learning techniques learn by creating a more abstract representation of data as the network grows deeper, as a result the model automatically extracts features and yields higher accuracy results.

What is Computer deep learning?

Deep learning is a subset of machine learning, a branch of artificial intelligence that configures computers to perform tasks through experience. Deep learning, an advanced artificial intelligence technique, has become increasingly popular in the past few years, thanks to abundant data and increased computing power.

Which deep learning schemes are used in computer vision?

This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders.

What is deep learning and how does it work?

Deep learning is a rich family of methods, encompassing neural networks, hierarchical probabilistic models, and a variety of unsupervised and supervised feature learning algorithms.

How many chapters are in this book on deep learning?

This book presents a collection of eleven chapters, where each chapter explains deep learning principles for a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community.

How effective is knowledge transfer in deep learning?

Experimental evidence demonstrates that the proposed technique is effective in different settings, including knowledge-transfer to smaller models, transfer between different deep architectures and harnessing knowledge from external data, producing models with increased accuracy compared to a typical training.