深度學習揭密:影像/影片篇

演講摘要

如果你是深度學習的入門者,想要知道怎麼把深度學習應用到日常工作中嗎?目前深度學習在許多類似人類行為的任務中,可以達到幾乎是頂級工藝的準確度,比如像是辨識畫面中物體名稱,或者在環境中辨認出光跡等等。

深度學習主要包含了:組裝大量的資料集、建立一個類神經網路、訓練、視覺化、評估不同的模型、使用特定的硬體(通常需要特殊程式編撰知識)等任務。這些任務通常因為其背後的複雜理論變得更有挑戰性。

在本段演講,我們將展示MATLAB簡化上述任務,並介紹可以減低您進行低階編程語言的新功能。藉此,我們將解碼深度學習領域的實用知識,再來建立並訓練神經網路來進行像是辨識手寫文字、分類畫面中的食物、分類訊號、及找出城市環境中的可駕駛區域等任務。


Demystifying Deep Learning: Image/Video Focus

Are you new to deep learning and want to learn how to use it in your work? Deep learning can achieve state-of-the-art accuracy in many humanlike tasks such as naming objects in a scene or recognizing optimal paths in an environment.

The main tasks are to assemble large data sets, create a neural network, to train, visualize, and evaluate different models, using specialized hardware - often requiring unique programming knowledge. These tasks are frequently even more challenging because of the complex theory behind them.

In this session, we’ll demonstrate new MATLAB features that simplify these tasks and eliminate the low-level programming. In doing so, we’ll decipher practical knowledge of the domain of deep learning. We’ll build and train neural networks that recognize handwriting, classify food in a scene, classify signals, and figure out the drivable area in a city environment.