
The tutorial:
Deep learning is one of the fastest growing branches in machine learning,
due to its spectacular performance in human cognitive tasks.
The neural network has many layers (‘deep’), and its main implementation is through ‘convolutional neural networks (CNNs)’. In 2012 the challenge to classify the images of the ImageNet database with 14 million images was won with a strikingly better performance that earlier methods.
The deep structure of many convolutional layers is also recognized in our human visual perception.
CNNs find applications in scene recognition, selfdriving cars, medical
diagnosis, translation etc.
The technology is feasible, as today we have abundant computing power, and
access to big data.
It is embraced by the biggest companies (Apple, Google, Facebook, Baidu), and is
rapidly transforming many areas of our technological society.
A convolutional neural network 
This course will give a stepbystep introduction to deep neural networks. We will discuss the terminology of many concepts, study the famous papers by the inventors, and implement our first steps in CNNs on some instructive toy databases, such as MNIST for handwritten digit recognition. As this field is also known as ‘braininspired computing’, attention is also paid to models of human visual perception. In the course, many realworld examples will be discussed and explained. We will discuss a number of wellestablished mathematical modeling techniques in detail, in particular multiscale and multiorientation differential geometry, models for selforganization and plasticity, and geometric neural feedback, leading to effective adaptive operations. We present the theory in an axiomatic, intuitive and fundamentally understood way.
The lectures will be in the morning, in the afternoon we practice all
concepts in a computer lab.
The course is concluded with a written exam.
This is a short intensive course of three full days, where each morning of
lectures is followed by a computer lab in the afternoon, to bring the concepts
to life (all software code is supplied).
We learn to design everything interactively with the powerful design language
Mathematica
version 11 (www.wolfram.com).
Students will work in small groups on the assignments.
Time: 30th Aug. 2017 (Wed.) 1nd Sep. 2017 (Fri.) 09:1012:10 &13:2017:00 (第2,3,4節&第6,7,8,9節) (Every day)
Location lectures: NTUST TR212
Location computer lab: NTUST TR212
Each day consists of 3 lectures of 45 minutes in the morning, and in the
afternoon we practice the concepts in a computer lab.
The tutor:
Prof. Bart M. ter Haar Romeny, PhD Eindhoven University of Technology Department of Biomedical Engineering Biomedical Image Analysis Den Dolech 2 – GEMZ 2.106 NL5600 MB Eindhoven, the Netherlands Tel. +31402475537, mob.: +31624235693 Email: B.M.terHaarRomeny@tue.nl Homepage: http://bmia.bmt.tue.nl/people/BRomeny/index.html 
Objectives of the course:
The introduction of deep learning and its applications.
To give a base of some mathematically well funded computer vision methods.
The course will focus on modern and robust applications, in particular on
medical imaging.
We will also introduce the notion of geometric reasoning', enabling us to
express tasks on images.
This requires some mathematical insight, which will be discussed and explained
extensively.
This course invites to ‘play with the math’ during the interactive computerlab
sessions.
The methods are inspired by the stunning performance of human visual perception.
We shortly discuss modern findings in brain and visual system research, and how
they can be exploited in our algorithms.
The reason we use Mathematica 11 in the computer lab and all lectures, is
that is is is a highlevel software language
with unequalled possibilities for design, with extensive visualization of the
results, and interactive manipulation of parameters.
It integrates symbolics, fast numerics (now faster than Matlab) and excellent
graphics.
Lectures overview:
Material:
Lecture of this morning: NTUST 2017 Deep Learning Introduction.pptx (PPT)
Deep learning by Geoffrey Hinton (PPT)
Task 1: Study the document An Introduction to Convolutional Neural Networks  Teach.pdf
Task 2: Study the PPT: NTUST 2017 Deep Learning Introduction.pptx (130MB), or pdf (3.4MB)
Task 3: Get to know a little bit of Mathematica 11.
<< Benchmarking`; (PS: don't forget the backquote `)


Second day: Thursday 31 August 2017, room NTUST TR212Rehearsal and discussion of the most important units in a CNN
The human visual system
12:1014:00 Lunch14:0017:00 Computer labPart I: Study the PPT by Geoffrey Hinton: HintonIntroDeepLearning.pptx Study in Wikipedia (only the beginning of the pages):
Study the PPT: Physiology of Vision.pptx Study the PPT: Learning Receptive Fields.pptx Study the instructive examples of Deep Networks (and the visualizations!) here: http://cs.stanford.edu/people/karpathy/convnetjs/ Part II: Now we know the basis notions, you can study the profossional (and
famous) packages
There are many entries into Deep Learning. www.deeplearning.net.PS: for the exam requirements: see below. Third day: Friday 1 September 2017, room NTUST TR212· Differential structure of images, shape detectionConvolution and Fourier Transform Example of a clinical application:


ExamThe exam will be on Friday 1 September 2017, 15:0016:00 in NTUST room T2511. The topics you should study:
Success!! 

Mathematica 11:
We design all our algorithms in Mathematica 11 (Wolfram
Inc., Champaign, Ill.). Mathematica is a powerful software environment for symbolic and fast numerical computing. Everything can be made interactive with a single line of code, enabling easy 'playing with complex math'. 
The executable of Mathematica 11 can be downloaded from
here, or you can download a
free trial version from Wolfram's website.
Many universities and institutes offer it as part of their campus license
software.
Bring your own laptop and do all experiments directly yourself!
Prof. Bart M. ter Haar Romeny, PhD
Eindhoven University of Technology, Department of Biomedical Engineering
Biomedical Image Analysis
B.M.terHaarRomeny@tue.nl