Practical Applications of Deep Learning (Sommersemester 2019)

Lecturer: Dr. Haojin Yang (Internet-Technologien und -Systeme) , Joseph Bethge (Internet-Technologien und -Systeme)

General Information

• Weekly Hours: 4
• Credits: 6
• Graded: yes
• Enrolment Deadline: 26.04.2019
• Teaching Form: Seminar
• Enrolment Type: Compulsory Elective Module
• Course Language: German
• Maximum number of participants: 12

Programs, Module Groups & Modules

Description

Artificial intelligence (AI) is the intelligence exhibited by computer. This term is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". Currently researchers and developers in this field are making efforts to AI and machine learning algorithms which intend to train the computer to mimic some human skills such as "reading", "listening", "writing" and "making inference" etc. From the year 2006 "Deep Learning" (DL) has attracted more and more attentions in both academia and industry. Deep learning or deep neural networks is a branch of machine learning based on a set of algorithms that attempt to learn representations of data and model their high level abstractions. In a deep network, there are multiple so-called "neural layers" between the input and output. The algorithm is allowed to use those layers to learn higher abstraction, composed of multiple linear and non-linear transformations. Recently DL gives us break-record results in many novel areas as e.g., beating human in strategic game systems like Go (Google’s AlphaGo), self-driving cars, achieving dermatologist-level classification of skin cancer etc. In our current research we focus on video analysis and multimedia information retrieval (MIR) by using Deep-Learning techniques.

Course language: German and English

Topics in this seminar:

• Text Detection: AI systems based on reinforcement learning model the behaviour and interactions of an agent with respect to a given but unknown environment. Popular Examples of recent breakthroughs with deep reinforcement learning are the breakthroughs of Google's AlphaGo system or the recent advances in playing Star Craft with a deep model. In this seminar topic we want to use deep reinforcement learning for the localization of text in images. We will develop and train an agent that is able to predict a series of transformations. These pedicted transformations will be applied to a given image in order to extract text lines from that image.
• Binary Neural Networks:  Convolutional neural networks have achieved astonishing results in different application areas. Various methods which allow us to use these models on mobile and embedded devices have been proposed. Especially Binary Neural Networks (BNNs) seem to be a promising approach for devices with low computational power or applications which have real-time requirements. In this topic you are going to develop an application, which utilizes BNNs, to be able to run a machine learning model independent of a processing server or a network connection on a low-powered device, e.g. a smartphone or a RaspberryPi. We have possible application ideas prepared, but would like to encourage you to think about your own ideas and will discuss the specific application together at the beginning of the project.

Requirements

• Strong interests in video/image processing, machine learning (Deep Learning) and/or computer vision

• Software development in C/C++ or Python

• Experience with OpenCV and machine learning applications as a plus

Literature

Books

• Alex Smola, Mu Li et al., Dive into deep learning
• Ian J. Goodfellow and Yoshua Bengio and Aaron Courville, "Deep Learning", online version
• Pedro Domingos “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World”
• Christopher M. Bishop “Pattern Recognition and Machine Learning” google it

Online courses:

• cs231n tutorials: Convolutional Neural Networks for Visual Recognition
• Deep Learning courses at Coursera

Deep Learning frameworks:

• MXNet
• Tensorflow
• PyTorch
• Caffe
• Chainer

Examination

The final evaluation will be based on:

• Initial implementation / idea presentation, 10%

• Final presentation, 20%

• Report/Documentation, 12-18 pages, 30%

• Implementation, 40%

• Participation in the seminar (bonus points)

Dates

Montag, 15.15-16.45

Room H-2.57

08.04.2019 15:15-16:45	Vorstellung der Seminar Themen (Topics1&2: binary neural network, topic 3&4: text detection)
08.04 - 15.04.2019	Wahl der Themen (Inform your preferred and secondary topics by email: haojin.yang(at)hpi.de)
17.04.2019	Bekanntgabe der Themen- und Gruppenzuordnung
wöchentlich	Individuelle Meetings mit dem Betreuer
27.05.2019	Technologievorträge und geführte Diskussion (je 15+5min)
15.07.2019	Präsentation der Endergebnisse (je 15+5min)
bis Ende August 2019	Abgabe von Implementierung und Dokumentation (Latex template)
bis Ende September 2019	Bewertung der Leistungen

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