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MATLAB for Machine Learning

You're reading from   MATLAB for Machine Learning Unlock the power of deep learning for swift and enhanced results

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781835087695
Length 374 pages
Edition 2nd Edition
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Getting Started with Matlab
2. Chapter 1: Exploring MATLAB for Machine Learning FREE CHAPTER 3. Chapter 2: Working with Data in MATLAB 4. Part 2: Understanding Machine Learning Algorithms in MATLAB
5. Chapter 3: Prediction Using Classification and Regression 6. Chapter 4: Clustering Analysis and Dimensionality Reduction 7. Chapter 5: Introducing Artificial Neural Network Modeling 8. Chapter 6: Deep Learning and Convolutional Neural Networks 9. Part 3: Machine Learning in Practice
10. Chapter 7: Natural Language Processing Using MATLAB 11. Chapter 8: MATLAB for Image Processing and Computer Vision 12. Chapter 9: Time Series Analysis and Forecasting with MATLAB 13. Chapter 10: MATLAB Tools for Recommender Systems 14. Chapter 11: Anomaly Detection in MATLAB 15. Index 16. Other Books You May Enjoy

Training and fine-tuning pretrained deep learning models in MATLAB

Transfer learning is a machine learning approach wherein a model created for a particular task is repurposed as the initial foundation for a model addressing a second task. This technique entails leveraging knowledge acquired from one problem and applying it to a distinct yet related problem. Transfer learning is particularly useful in deep learning and neural networks, where pretrained models can be fine-tuned or used as feature extractors for new tasks.

In pretrained models, you start with a pretrained model that has been trained on a large dataset for a specific task, such as image classification, natural language processing, or speech recognition. These pretrained models are often complex neural networks with many layers. In many cases, you can use the layers of the pretrained model as feature extractors. You remove the final classification layer(s) and use the activations from the earlier layers as features...

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