This is the code repository for Hands-On One-shot Learning with Python, published by Packt.
Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch
This book is a step by step guide to one-shot learning using Python-based libraries. It is designed to help you understand and design models that can learn information about your data from one, or only a few, training examples. You will also learn to apply these techniques with real-world examples and datasets for classification and regression.
This book covers the following exciting features:
- Get to grips with the fundamental concepts of one- and few-shot learning
- Work with different deep learning architectures for one-shot learning
- Understand when to use one-shot and transfer learning, respectively
- Study the Bayesian network approach for one-shot learning
- Implement one-shot learning approaches based on metrics, models, and optimization in PyTorch
- Discover different optimization algorithms that help to improve accuracy even with smaller volumes of data
- Explore various one-shot learning architectures based on classification and regression
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
# import small dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target
Following is what you need for this book: If you're an AI researcher or a machine learning or deep learning expert looking to explore one-shot learning, this book is for you. It will help you get started with implementing various one-shot techniques to train models faster. Some Python programming experience is necessary to understand the concepts covered in this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-4).
| Chapter | Software required | OS required |
|---|---|---|
| 1 - 6 | Python 3.7, Anaconda Distibution, Jupyter Notebook, GPU (preferable) | Any OS (Linux prefered) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Shruti Jadon is currently working as a Machine Learning Software Engineer at Juniper Networks, Sunnyvale and visiting Researcher at Rhode Island Hospital (Brown University). She has obtained her master's degree in Computer Science from University of Massachusetts, Amherst. Her research interests include deep learning architectures, computer vision, and convex optimization. In the past, she has worked at Autodesk, Quantiphi, SAP Labs, and Snapdeal.
Ankush Garg is currently working as a Software Engineer in the auto-translation team at Google, Mountain View. He has obtained his master's degree in Computer Science from the University of Massachusetts, Amherst and Bachelor's at NSIT, Delhi. His research interests include language modeling, model compression, and optimization. In the past, he has worked as a Software Engineer at Amazon, India.
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