Keyes R. Zefs Guide to Deep Learning 2022 torrent

Textbook in PDF format

Zefs Guide to Deep Learning is a short guide to the most important concepts in Deep Learning, the technique at the center of the current Artificial Intelligence (AI) revolution. It will give you a strong understanding of the core ideas and most important methods and applications. All in around only 150 pages!
This book presents the foundational concepts behind Machine Learning, neural networks, and the recent major advancements in architectures and training techniques in an easy to understand way. It also covers the most important applications of deep neural networks, including Computer Vision, natural language processing (NLP}, and beyond. Your time is valuable, Zefs Guide to Deep Learning will get you up to speed in around only 150 pages!
This book is about Deep Learning, a set of machine learning methods that have sparked a huge amount of interest in applying computational and predictive models to everything from whimsical face filters to medical imaging to generating computer code itself. Deep learning is at the core of the current “AI revolution”. While based on techniques that can be traced back more than half a century, only in the past decade have these techniques really come into their own and they now dominate the predictive modeling space for an increasingly large number of use cases. This book aims to help you get a better high-level, conceptual understanding of how deep learning works, its central concepts, applications, limitations, and possibilities.
Why deep learning?
Deep Learning is a name applied to a class of neural networks with many “layers”, allowing them to be trained to perform certain kinds of tasks that traditional modeling techniques have not been able to do nearly as well. In some cases these deep neural networks can even outperform humans on these tasks, which has fueled the high level of excitement around this family of methods.
Machine Learning
Neural Networks
The rise of deep learning
Computer vision and convolutional neural networks
Natural language processing and sequential data techniques
Advanced techniques and practical considerations
Notes