Qualcomm AI Training Webinar // 高通AI線上訓練課程
近年AI已進入人類的生活，跨領域的應用也蓬勃興起。為協助台灣產業因應AI帶來的挑戰，台北市電腦商業同業公會(TCA)與Qualcomm高通公司合作辦理AI訓練課程，針對台灣中小企業與新創團隊提供一系列的三天的AI免費線上訓練課程，2022年4月12日至4月14日，推廣AI技術的基本知識應用，協助台灣中小企業與新創企業領先於這波先進技術中的浪潮！
課程資訊
時間：9:30AM to 5:30PM，2022年4月12日(二)-4月14日(四); 共三天
對象：中小企業、新創團隊
費用：免費
語言：中文
主辦單位：Qualcomm高通公司
協辦單位：台北市電腦商業同業公會
聯絡人：台北市電腦公會 02-25774249 分機847 羅小姐
註1. 主辦單位將審核您的報名，您需收到報名確認信才算報名成功。主辦單位會再寄發線上課程網址。未入選學員不另行通知。
註2. 本次共開放35個名額參加，主辦單位保有學員篩選與培訓內容調整之權利。
註3. 學員需至少參與1次報到點名，並填寫問卷，
註4. 報名時請務必填寫您的中文姓名、公司名稱以及Email。
4月12-4月14日 三日議程:
Day1: Convolution Neural Network and SNPE
Date | Agenda | Time | Duration | |
April 12th Day 1
| Opening Session | Opening Speech delivered by Qualcomm | 9:30-9:35 am | 5 mins |
Agenda Brief | 9:35-9:40 am | 5 mins | ||
1.0 Self-introduction by teachers and students | Teachers introduce themselves | 9:40-10:00 am | 20 mins | |
Attendees give briefs of who they are and what they are working on. | ||||
1.1 Convolution Neural Network I | 1.1.1 Basic neuron layers | 10:00-10:30 am | 30 mins | |
1.1.2 Loss functions | ||||
1.1.3 Optimizer | ||||
1.1.4 Prevent Over-fitting in Deep Learning | ||||
1.1.5 Basic concepts in Machine Learning & Deep Learning | ||||
1.2 Convolution Neural Network II | 1.2.1 Classic models | 10:30-12:00 pm | 90 mins | |
1.2.2 Hyper-parameters & Tuning Tricks | ||||
1.2.3 Parameter Initialization | ||||
1.2.4 Fine-tune & Transfer Learning | ||||
1.2.5 Data pre-processing & Data augmentation | ||||
Lunch 12:00-1:00 pm | ||||
Q&A, Open Discussion 1:00-1:30 pm | ||||
1.3 SNPE | 1.3.1 Overview | 1:30-1:40 pm | 10 mins | |
1.3.2 Hardware & Software Preparation | 1:40-2:00 pm | 10 mins | ||
1.3.3 User-defined Operations (UDO) | 3:00-3:25 pm | 25 mins | ||
1.3.4 Running on Different Systems | 3:25-3:45 pm | 20 mins | ||
Tea break 3:45 - 3:55 pm | ||||
1.3.5 SNPE Tools | 3:55-4:15 pm | 20 mins | ||
1.3.6 Snapdragon Profiler | 4:15-4:30 pm | 15 mins | ||
1.3.7 Android AI Demo Implementation | 4:30-4:40 pm | 10 mins | ||
1.3.8 SNPE Skills | 4:30-5:00 pm | 30 mins | ||
| Q&A, Open Discussion 5:00 pm~ | |||
Day 2: Image Classification and Hands-on
Date | Agenda | Time | Duration | |
April 13th Day2
| 2.1 Getting started with Image Classification | 2.1.1 Overview: What is Image Classification? | 9:30-12:30 am | 10 mins |
2.1.2 Performance metrics (Precision, Recall, Accuracy, FPR) | 50 mins | |||
2.1.3 PyTorch Introduction | 60 mins | |||
2.1.4 Dataset Annotation and Preparation | 60 mins | |||
Lunch 12:30-1:30 pm | ||||
Q&A, Open Discussion 1:30-2:00 pm | ||||
2.2 Hands on Image Classification | Part I | 2:00-3:30 pm | 90 mins | |
Tea break 3:30 - 3:40 pm | ||||
Part II | 3:40-5:10 pm | 90 mins | ||
Day 3: Object Detection and Hands-on
Date | Agenda | Time | Duration | |
April 14th Day 3 | 3.1 Getting started with Object Detection | 3.1.1 Overview: What is Object Detection? | 9:30-12:30 pm | 30 mins |
3.1.2 Performance metrics: mAP | 30 mins | |||
3.1.3 Pre-processing: Data augmentation, box encoding | 30 mins | |||
3.1.4 Post-processing: Non-Maximum Suppression (NMS) | 30 mins | |||
3.1.5 Backbone, Neck, Head | 30 mins | |||
3.1.6 Object detection comparison | 30 mins | |||
Lunch 12:30-1:30 pm | ||||
Q&A, Open Discussion 1:30-2:00 pm | ||||
3.2 Hands on Object Detection - YOLOX | 3.2.1 Code details | 2:00-3:00 pm | 30 mins | |
3.2.2 Start training: Apply YOLOX on custom dataset | 30 mins | |||
3.2.3 Design your own model | 3:00-4:00 pm | 30 mins | ||
3.2.4 Convert trained model to SNPE DLC | 30 mins | |||
Tea break 4:00 - 4:10 pm | ||||
3.2.5 Deploy model to Android app | 4:10-5:10 pm | 60 mins | ||
Q&A, Open Discussion 5:10 pm~ | ||||
