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Use pre-trained models to rapidly transfer existing knowledge and insight for more efficient machine learning applications, even when you have limited or weak datasets.
In Transfer Learning in Action you will learn:
Transfer Learning in Action shows you how using pre-trained models can massively improve the accuracy and performance of your machine learning projects. Focused on the real-world applications of transfer learning, you’ll explore how to enhance everything from computer vision to natural language processing and beyond. Master hands-on techniques taken from the latest research, and discover how you can customize open source models for your specific needs.
In Transfer Learning in Action you will learn:
- Fundamental concepts of transfer learning
- Real world applications of transfer learning
- Improving performance of computer vision models with state-of-the-art pre-trained models
- Transfer learning for natural language processing
- Optimizing audio classification and generation Leveraging TensorFlow-Hub, HuggingFace transformers, and other frameworks
Transfer Learning in Action shows you how using pre-trained models can massively improve the accuracy and performance of your machine learning projects. Focused on the real-world applications of transfer learning, you’ll explore how to enhance everything from computer vision to natural language processing and beyond. Master hands-on techniques taken from the latest research, and discover how you can customize open source models for your specific needs.
about the technology
Humans learn new things more easily when they build on existing related knowledge. Transfer learning applies this same principle to machine learning. This technique takes powerful pre-trained models and shares their existing knowledge to help improve new machine learning applications. The result is a massive performance boost to your models, especially when your training data is limited, unbalanced, or poorly annotated.about the book
Transfer Learning in Action teaches the fundamental concepts of transfer learning and how you can easily apply them to your projects. It’s filled with insightful illustrations that demystify the latest research and make this cutting-edge technique easily accessible. You’ll start with the basics, gradually building your confidence and understanding until you’re ready to handle advanced techniques. Each chapter explores a different real-world application of transfer learning, such as image classification and style transfer, automatic question answering, and even speech recognition.about the reader
For data scientists experienced with machine learning and deep learning, Python, PyTorch, and TensorFlow.about the authors
Dipanjan Sarkar is a data scientist who has worked across numerous Fortune 500 companies, including Applied Materials, Intel, IBM, and Red Hat. Dipanjan is a Google Developer Expert in Machine Learning, and the author of multiple books and articles on machine learning.Raghav Bali is a Senior Data Scientist at Optum (UnitedHealth Group), one the world's largest health care organizations. He has over ten years experience in the research and development of enterprise-level solutions using machine learning, deep learning, and natural language processing. He is the author of multiple machine learning papers, books, and patents.
An enlightening tool to improve the way we approach intelligent systems.
If you are looking for a balanced presentation of transfer learning techniques with hands-on examples look no further!
The best collection of transfer learning examples you'll find anywhere.
A refreshing new book loaded with practical examples to implement transfer learning.
