Name: Qualcomm AI Training
Start: 2020-12-02T09:30:00+08:00
End: 2020-12-04T17:30:00+08:00

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，2020年12月2日至12月4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，2020年12月2日-4日(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825 李小姐、分機847 陳先生

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，主辦單位會再將寄發線上課程網址。
註2. 本次開放共35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. 主辦單位將向成功報名的學員收取新台幣2,000元訂金，並至少參與1次報到點名，並填寫問卷者，主辦單位將於30工作天內全數退還訂金。
註4. 報名時請務必填寫您的中文姓名以及公司Email。

12月2-4日三日議程:

Day1	Opening Session	Opening Speech delivered by Qualcomm	9：30-9：35	5
	Opening Session	Agenda Brief	9：35-9：40	5
	1.0 Self-introduction by teachers and students	Teachers introduce themselves Attendees give briefs of who they are and what they are working on.	9：40-10：00	20
	1.1 Introduction to AI	1.1.1 AI: What? How? Where?	10:00 - 10:30 a.m.	30
		1.1.2 Qualcomm AI
		1.1.3 AI vs. Machine learning vs. Deep Learning
		1.1.4 Different types of Machine Learning
		1.1.5 Basic concepts in Machine Learning & Deep Learning
	1.2 Quick Tour of Deep Learning	1.2.1 From ML to DL: What is deep learning?	10:30 - 11:10 a.m	20


		1.2.2 Datasets of Deep learning: From public to custom		20
		1.2.2 Datasets of Deep learning: From public to custom		20
		Tea break 11:10 - 11:20 a.m.
		1.2.3 Infrastructure of Deep learning: From hardware to software	11:20 - 12:00 a.m	40



		Lunch 12:00-1:00
		Q&A, Open Discussion 13:00-13:30
		1.2.4 History, Present and Future	1:30 - 2:30 p.m.	10
				10
				10
				10
				20



				20
				20
		Tea break 2:30 - 2:40 p.m.
	1.3.1 Review	Review	2:40-2:50p.m	10
	1.3.2 Model Conversion and Demo	Hardware Preparation: AI Kit	2:50-3:00p.m	10
		SoftWare Preparation: Part 1 SNPE SDK Development Environment Setup	3:00-3:25p.m	25
		SoftWare Preparation: Part 2 SNPE Application Development Tools	3:25-3:45p.m	20
		Tea break 3:45 - 3:55 p.m.
		AI Demos - Object Detector Demo Converting Model	3:55-4:15p.m	20
		AI Demos - Object Detector Run Demo	4:15-4:30p.m	15
	1.3.3 Solve AI Problem Skills	Solve AI Problem Skills	4:30-4:40p.m	10
	1.3.4 Homework, Q & A	Homework, Q & A	4:40~	-

Day2	2.1 Foundation of Deep Learning	2.1.1 Perceptron & Multilayer Perceptron	9:30 - 10:00 a.m.	10
		2.1.1 Perceptron & Multilayer Perceptron	9:30 - 10:00 a.m.	10
		2.1.2 Basic neuron layers	10:00 - 10:30 a.m.	20
		2.1.3 Loss functions	10:30 - 11:00 a.m	15
		2.1.4 Optimizer		20
		2.1.4 Optimizer		10
		2.1.5 Prevent Over-fitting in Deep Learning	11:00 - 12:00 a.m	20
		2.1.5 Prevent Over-fitting in Deep Learning	11:00 - 12:00 a.m	30
		Lunch 12:00-13:00
		Q&A, Open Discussion 13:00-13:30
	2.2 Building Deep Learning Model	2.2.1 Classic models	1:30 - 2:20 p.m	10
				30
				10
		2.2.2 Hyper-parameters & Tuning Tricks	2:20 - 2:50 p.m	10
		2.2.2 Hyper-parameters & Tuning Tricks		10
		2.2.3 Fine-tune & Transfer Learnin		10
		2.2.4 Data pre-process & Data augmentation		10


		2.2.5 Hands-on - Keras_MNIST	2:50-3:00p.m	10
		Tea break 3:00 - 3:15 p.m
	2.3.1 Review	Review	3:15-3:55p.m	10
	2.3.2 SNPE Training Part 2	An Image Classifiers Demo		10
		SNPE Introduction		10
		SNPE Workflow		10
		*Supported Chipsets / Supported Network Layers*	3:55-4:40p.m	5
		*User-defined Operations (UDO) Workflow*		30
		*Limitations*		5
		*CPU vs GPU vs DSP*		5
		Tea break 4:40 - 4:50 p.m
		*Run SNPE on Linux Machine*	4:50-5:20p.m	10
		Building and Running the C++ Application on ARM Android		10
		Thermal Measurement		5
		Solve Problem with SNPE		5
	2.3.3 Q & A	Q & A	5:20~	-

Day3	3.1 Getting Started with TensorFlow	3.1.1 TensorFlow Overview	9:30 - 10:30 a.m.	10
		3.1.2 Low level API		10
		3.1.3 Middle level API		10
		3.1.4 High level API: Keras		10
		3.1.4 High level API: Keras		20
	3.2 Basic Knowledge of Object Detection	3.2.1 Overview	10:30 - 11:00 a.m	10
		3.2.2 Performance metrics	10:30 - 11:00 a.m	20
		3.2.3 Traditional methods for Object Detection	11:00 - 12:00 a.m	20
		3.2.4 Two-stage detection		20
		3.2.5 One-stage detection		20
		Lunch 12:00-13:00
		Q&A, Open Discussion 13:00-13:30
		3.2.6 Hands-on Object Detection	1:30 - 2:30 p.m	60
	3.3 SNPE Training Part 3	Review	2:30 - 3:00 p.m	5
		SNPE Benchmarking	2:30 - 3:00 p.m	25
		AI demos – Object Detector Demo Part 1 : Get Camera Stream by Camera2 Part 2 : JNI Interface Part 3 : Integrating SNPE	3:00 - 3:15 p.m	15
		Tea break 3:15 - 3:25p.m
		AI demos – Face Recognition	3:25 - 4:00 p.m	25
		Solve SNPE Problem Skills	3:25 - 4:00 p.m	10
		Q & A, Others	4:00 ~

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