C1 / 深度學習揭密:影像/影片篇
Demystifying Deep Learning: Image/Video Focus

Abhijit Bhattacharjee / MathWorks Inc.




  • 管理龐大的影像資料集
  • 網路的視覺化,並從具有黑盒子特性的深度網路中獲得洞見
  • 透過拖和放(drag-and-drop)介面,從草稿建立神經網路
  • 執行圖片和訊號的分類,和在圖片上進行畫素層級(pixel-level)的語意分割(semantic segmentation)
  • 從GoogLeNet和ResNet等網路匯入訓練資料集
  • 從TensorFlow Keras、Caffe、以及ONNX Model 格式匯入模型
  • 透過在電腦叢集的平行運算加速網路訓練
  • 把需要大量人工作業的真實地面標記(ground truth labeling)變成自動化的工作
  • 自動為嵌入式目標硬體產生開源程式碼

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.

    Along the way, you will see MATLAB features that make it easy to:
  • Manage extremely large sets of images
  • Visualize networks and gain insight into the black box nature of deep networks
  • Build networks from scratch with a drag-and-drop interface
  • Perform classification tasks on image and signals, and pixel-level semantic segmentation on images
  • Import training data sets from networks such as GoogLeNet and ResNet
  • Import models from TensorFlow Keras, Caffe, and the ONNX Model format
  • Speed up network training with parallel computing on a cluster
  • Automate manual effort required to label ground truth
  • Automatically generate source code for embedded targets