Transformers for Machine Learning: A Deep Dive (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
$42.03
Description
Book Synopsis:
Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers.Key Features:
- A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers.
- 60+ transformer architectures covered in a comprehensive manner.
- A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision.
- Practical tips and tricks for each architecture and how to use it in the real world.
- Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab.
The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students, practitioners, and professionals as it allows for quick experimentation and lowers the barrier to entry into the field.
Details
Transformers for Machine Learning: A Deep Dive is the ultimate guide for anyone looking to master the power of transformers in the world of artificial intelligence. Whether you're a postgraduate student or a seasoned researcher, this comprehensive book offers in-depth explanations of every algorithm and technique related to transformers. From NLP and speech recognition to time series and computer vision, this book covers it all.
With over 60 transformer architectures covered in detail, this book leaves no stone unturned. You'll gain a deep understanding of how to apply transformer techniques in various domains, allowing you to unlock new possibilities in your projects. Plus, practical tips and tricks for each architecture will help you navigate real-world challenges with ease.
What sets this book apart is the inclusion of hands-on case studies and code snippets, all ready to run in Google Colab. You'll be able to delve into theory while also getting your hands dirty with real-world analysis. This practical approach ensures that you're not just learning concepts, but also gaining the skills to apply them effectively.
Whether you're an academic researcher or a working professional, Transformers for Machine Learning: A Deep Dive is a must-have resource. Its theoretical explanations of state-of-the-art transformer architectures will satisfy those seeking a deep understanding, while the practical case studies and code make it accessible to beginners. Don't miss out on this opportunity to elevate your machine learning capabilities.
Get your copy of Transformers for Machine Learning: A Deep Dive today and embark on a transformative journey in the world of artificial intelligence.
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