深度學習揭密：訊號篇

演講摘要

本段演講將帶你了解MATLAB如何幫助和簡化執行訊號資料的深度學習。一開始，我們先探索幾個重要的功能，像是資料標記、前處理，以及大量訊號資料集的整理。接下來，我們將評估幾種用來進行深度學習的常見網路，以及如何應用這些網路來解決真實世界的訊號問題。

我們將透過範例逐步探索涵蓋以下兩種不同的網路的完整工作流程：

• 利用卷積神經網路(Convolution Neural Networks，CNN)，以聲音訊號進行人聲指令的辨識
• 利用長短期記憶(Long Short-Term Memory，LSTM)網路進行ECG訊號分類

演講的最後，我們將展示如何佈署這些經過訓練的模型，讓這些模型能夠在GPU或嵌入式處理器上面執行。

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Demystifying Deep Learning: Signal Focus

Learn how MATLAB enables and simplifies the process of performing deep learning with signal data. We’ll start off by looking at key capabilities for labeling, pre-processing, and sorting large signal data sets. Then we'll examine the key types of networks used for deep learning and how they are applied to solve real-world signal problems.

We’ll walk through full workflow examples covering two different types of networks:

• Voice command recognition of audio signals using Convolution Neural Networks (CNN)
• Classify ECG signals using Long Short-Term Memory (LSTM) networks

We will wrap up by showing how these trained models can be deployed to run on a GPU or embedded processor.