人工智慧：深度學習與強化學習工作流程

演講摘要

AI人工智慧，已經成為一股推動電腦在我們生活或工作中扮演角色轉變的強勢力量。目前AI有兩個最新的工作流程，一是深度學習，一是強化學習，能為產業帶來轉型，並提升現有的應用技術，像是醫學症狀的診斷、駕駛無人車輛與機器人控制等等。本演講將探討MATLAB®能同時支援深度學習以及強化學習(Reinforcement Learning)工作流程，包含：

• 訓練用資料準備和標記的自動化
• 與開源的深度學習架構之間能互相溝通
• 能訓練包含影像、訊號、文字等類型資料的深度神經網路
• 可以調整超參數(hyper-parameter)加快訓練時間及增進網絡的準確度
• 可產生多核目標的程式碼，實現在NVIDIA®、Intel®、ARM®的目標硬體上

---

Deep Learning and Reinforcement Learning Workflows in AI

AI, or artificial intelligence, is powering a massive shift in the roles that computers play in our personal and professional lives. Two new workflows, deep learning and reinforcement learning, are transforming industries and improving applications such as diagnosing medical conditions, driving autonomous vehicles, and controlling robots.

This talk dives into how MATLABR supports deep learning and reinforcement workflows, including:

• Automating preparation and labeling of training data
• Interoperability with open source deep learning frameworks
• Training deep neural networks on image, signal, and text data
• Tuning hyper-parameters to accelerate training time and increase network accuracy
• Generating multi-target code for NVIDIA®, Intel®, and ARM®