Familiar with the basics and ready to apply deep learning with MATLAB®? Get started with the hands-on examples in this ebook. You'll learn three approaches to training neural networks for image classification:
Training a network from scratch
Using transfer learning to train an existing network
Adapting a pretrained network for semantic segmentation
You'll also see two examples showing how deep learning models can be applied to time series or signal data.
In this ebook, you will learn:
Create and configure network layers
Adapt network architectures, including convolutional neural network (CNN), directed acyclic graph (DAG), and long short-term memory (LSTM)
Select the best training options and algorithms
Use data augmentation and Bayesian optimization to improve training accuracy
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