Temporary Propping of Deep Excavations

Learn to safely navigate the deep anonymous segment of the internet. Become a Deep Web Expert!

This book introduces the basic principles and implementation process of deep learning in a simple way, and uses python's numpy library to build its own deep learning library from scratch instead of using existing deep learning libraries.

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems.