Course Summary on the topic of Deep Learning

1. DEEP LEARNING
ITP810 : KEY TECHNOLOGIES FOR 4TH INDUSTRIAL REVOLUTION, PROF. JONGHEUNG PARK
PREPARED BY : RIRI KUSUMARANI / 20155636

2. REPORT CONTENT
 Concept of Deep Learning
 How Deep Learning Works
 Architectures
 ImageNet Competition
 Application of Deep Learning
 Reinforcement Learning

3. DEEP LEARNING
 Deep learning is strongly related in machine learning in
which it shows us an upward trend since early 2005
 There are at least 4 people who are also known as the big
four in Deep Learning area. These people are : Geoffrey
Hinton, Yan LeCunn,Andrew Ng and Yosua Bengio.
 The basic idea about deep learning is the availability of
computational power which allow machines to recognize
objects and translate speech in real time. If we relate this
with previous topic in ITP810, it means deep learning also
allows artificial intelligence to get smarter each day.
Figure 1 : Trends on Deep Learning & The Big Four
Source : ITP810 Class Materials

4. How Deep Learning Works?
 Programmers would train a neural network to
detect an object or phoneme by blitzing the
network with digitized versions of images
containing those objects or sound waves
containing those phonemes.
 If the network didn’t accurately recognize a
particular pattern, an algorithm would adjust the
weights.
 The eventual goal of this training was to get the
network to consistently recognize the patterns in
speech or sets of images that we humans know
say, the phoneme “d” or the image of a dog.
Figure 2 : Machine Learning Workflow
Source : ITP810 Class Materials

5. DEEP LEARNING ARCHITECTURES
 There are many well-known deep learning architectures. Some of these are Convolutional Neural
Network , Recurrent Neural Network and Deep Belief Network.
 Convolutional Neural Network might be the most famous architecture amongst all and it’s simple to
understand. CNN mostly being used for image processing..

6. IMAGENET COMPETITION
 ImageNet is an image dataset organized according to the WordNet hierarchy.
ImageNet aim to provide on average 1000 images to illustrate each synset. Images
of each concept are quality-controlled and human-annotated. ImageNet aims to
offer tens of millions of cleanly sorted images for most of the concepts in the
WordNet hierarchy.
 In accordance with this, ImageNet starts a competition which is done each year
since 2010. This competition is also known as ImageNet Large Scale Visual
Recognition Challenge (ILSVRC).
 The idea of this competition is to allow researchers to compare progress in
detection across a wider variety of objects - taking advantage of the quite
expensive labeling effort. Another motivation is to measure the progress of
computer vision for large scale image indexing for retrieval and annotation.
Figure 3 : Exampel of ImageNet Competition
Source : Imagenet.net

7. APPLICATION OF DEEP LEARNING
 Deep learning allows the application of it to many aspects in daily life.
 For example, by using CNN Architecture, machine can detect the movement of human through video, image
captioning, visual question answering, semantic segmentation and automatic colorization of black and white
images.

8. REINFORCEMENT LEARNING
What?
 Reinforcement Learning is another branch of machine
learning in which it doesn’t rely on examples of correct
behavior, goal oriented , maximize a reward signal and
there’s existence of trade-off between exploration and
exploitation.
 The picture on the right summarizes the difference
between reinforcement learning with other branches of
machine learning such as supervised learning and
unsupervised learning.

9. How Reinforcement Learning Works?
 There will be at least three main factors in RL:
Environment, agent , reward and policy. The main
goal for RL is to maximize reward.
Figure 4 &5 : How RL Works
Source : ITP810 Class Materials

10. ALPHA-GO
 If we discuss about artificial intelligence and deep learning, we can’t avoid the recent topic of AlphaGo
which happens to be the renowned Deep Learning application.
 Prof.Lee spent last hour of his lecture to discuss the logic of AlphaGo which I found very much
interesting and relatable with the topic that we’ve been discussing for the last 2 weeks.
 During his class, he explain in a way that students who don’t have basic knowledge on deep learning,
understand how AlphaGo operates.
 The concept of reducing search space is said to be the main feauture and strength of AlphaGo . Even this
reduce space sounds simple, the technical requirement behind is complicated.
 AlphaGo itself is exposed to millions of games and board position.