Introduction to Deep Learning and Self-Driving Cars

1. 강의 개요

1.1 주요 학습 주제

• Deep Reinforcement Learning
• Convolutional Neural Networks
• Recurrent Neural Networks

위 기술들을 이용하여 autonomous driving완성

• perception, localization, mapping, control, planning, driver state

1.2 목표

A. Project DeepTraffic

B. Project DeepTesla

2. 자동 주행시 필요한/해결해야하는 기술/정보들

• Localization and Mapping: Where am I?
• Scene Understanding: Where is everyone else?
• Movement Planning: How do I get from A to B?
• Driver State: What’s the driver up to?

History

• DARPA Grand Challenge II (2006) : Stanford’s Stanley wins
• DARPA Urban Challenge (2007) : CMU’s Boss (Tartan Racing) wins

3. 뉴런

• Human brain: ~100-1,000 trillion synapses
• (Artificial) neural network: ~1-10 billion synapses

3.1 Perceptron Algorithm

Provide training set of (input, output) pairs and run:

1. Initialize perceptron with random weights
2. For the inputs of an example in the training set, compute the Perceptron’s output
3. If the output of the Perceptron does not match the output that is known to be correct for the example:
   1. If the output should have been 0 but was 1, decrease the weights that had an input of 1.
   2. If the output should have been 1 but was 0, increase the weights that had an input of 1.
4. Go to the next example in the training set and repeat steps 2-4 until the Perceptron makes no more mistakes

뉴런의 기본 설명 : 입력 - weigh - Sum up - Activate - 출력

3.2 Neural Networks are Amazing

히든 레이어를 포함한 멀티 레이어 설명

• Special Purpose Intelligence : Supervised learning??
• General Purpose Intelligence : Unsupervised learning or Reinforcement Learning ??

핑퐁 게임을 예시로 설명

3.3 Current Drawback

• Lacks Reasoning:
  • Humans only need simple instructions: “You’re in control of a paddle and you can move it up and down, and your task is to bounce the ball past the other player controlled by AI.”
• Requires big data: inefficient at learning from data
• Requires supervised data: costly to annotate real-world data
• Need to manually select network structure
• Needs hyperparameter tuning for training:
  • Learning rate
  • Loss function
  • Mini-batch size
  • Number of training iterations
  • Momentum: gradient update smoothing
  • Optimizer selection
• Defining a good reward function is difficult…

3.4 What changed?

• Compute : CPUs, GPUs, ASICs
• Organized large(-ish) datasets : Imagenet
• Algorithms and research: Backprop, CNN, LSTM
• Software and Infrastructure : Git, ROS, PR2, AWS, Amazon Mechanical Turk, TensorFlow, …
• Financial backing of large companies : Google, Facebook, Amazon, …

3.5 Useful Deep Learning Terms

3.6 Traditional Machine Learning

• Edge, Corners, Contours등을 사용하는 방식. (이전 방식인가?)
• 각 항목을 사람이 이전에 정의 해주어야 함 (Hand Designed Feature)

3.7 Deep Learning Applications

색 칠하기, 고양이 구분, 번역등 여러 활용예 설명

할수 있는것도 있지만 못하는 것도 있음. Moravec’s Paradox: The “Easy” Problems are Hard 걷기, 중심 잡기등이 체스보다 더 어려움.

데이터 부족???

“Encoded in the large, highly evolved sensory and motor portions of the human brain is a
billion years of experience about the nature of the world and how to survive in it.…
Abstract thought, though, is a new trick, perhaps less than 100 thousand years old. We have
not yet mastered it. It is not all that intrinsically difficult; it just seems so when we do it.”
- Hans Moravec, Mind Children (1988)

• Visual perception: 540 millions years of data
• Bipedal movement: 230+ million years of data
• Abstract thought: 100 thousand years of data

그럼 자율 주행 자동차는 어려운가?

• Human performance(사고 확률): 1 fatality per 100,000,000 miles
• Error rate for AI to improve on: 0.000001%

Challenges:

• Snow
• Heavy rain
• Big open parking lots
• Parking garages
• Any pedestrian behaving irresponsibly or just unpredictably
• Reflections, dynamics blinding ones
• Merging into a high-speed stream of oncoming traffic

딥러닝은 완전하지 않다. 속이기 쉽다[1]. = 사고날 확률이 높다.

[1]: Nguyen et al. "Deep neural networks are easily fooled: High confidence predictions for unrecognizable images." 2015

3.8 What’s Next for Deep Learning? (5 year vision)

• Ilya Sutskever, Research Director of OpenAI: Deeper models, models that need fewer examples for training.
• Christian Szegedy, Senior Research Scientist at Google: Become so efficient that they will be able to run on cheap mobile devices.
• Pieter Abbeel, Associate Professor in Computer Science at UC Berkeley: Significant advances in deep unsupervised learning and deep reinforcement learning.
• Ian Goodfellow, Senior Research Scientist at Google: Neural networks that can summarize what happens in a video clip, and will be able to generate short videos. Neural networks that model the behavior of genes, drugs, and proteins and then used to design new medicines.
• Koray Kavukcuoglu & Alex Graves, Research Scientists at Google DeepMind: An increase in multimodal learning, and a stronger focus on learning that persists beyond individual datasets.
• Charlie Tang, Machine Learning group, University of Toronto: Deep learning algorithms ported to commercial products, much like how the face detector was incorporated into consumer cameras in the past 10 years.

3.9 딥러닝 라이브러리들