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Deep Learning Product List

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Deep Learning for Signal Processing with MATLAB

Deep Learning for Signal Processing with MATLAB

[White Paper Presentation] Explanation of Signal Processing Using Deep Learning

This white paper explains the basics of deep learning, as well as three examples of signal processing (voice command recognition, remaining useful life prediction, and signal noise reduction). Through these examples, it describes how deep learning using MATLAB can help perform signal processing tasks more quickly and achieve more accurate results. MATLAB is software that combines a desktop environment suitable for iterative analysis and design processes with a programming language that directly represents matrix and array mathematics. [Contents (excerpt)] ■ Introduction ■ Basics of Deep Learning ■ Deep Learning Networks ■ Choosing a Network ■ Considerations Regarding Signal Data *For more details, please refer to the PDF document or feel free to contact us.

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Deep Learning with MATLAB

Deep Learning with MATLAB

This provides a concise explanation of the techniques that form the basis of deep learning.

Deep learning is one of the methods of machine learning, where the model learns classification methods directly from images, text, and audio. In this ebook, we provide a concise explanation of the fundamental techniques. Deep learning is not difficult at all, and you can start right away even if you are not an expert. [Contents (excerpt)] ■ What is deep learning? ■ Application areas of deep learning ■ How deep neural networks work ■ Learning methods for deep neural networks ■ About convolutional neural networks *For more details, please refer to the PDF materials or feel free to contact us.

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AI and Statistics Terminology Glossary: "Deep Learning"

AI and Statistics Terminology Glossary: "Deep Learning"

Automatic feature extraction by machines! Introducing the differences between deep learning and machine learning.

Deep learning is a form of machine learning that involves machines learning from large amounts of data to extract features from that data. Deep learning differs from traditional machine learning in that, in traditional machine learning, the manual extraction of features is the initial step, and models are built using those features. However, in deep learning, features are automatically extracted by the machine. *For detailed content of the glossary, please refer to the related links. For more information, feel free to contact us.*

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