Practical Deep Learning Examples with MATLAB

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:

  1. Training a network from scratch

  2. Using transfer learning to train an existing network

  3. 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.

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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

  • Incorporate spectrograms for speech recognition

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