Introduction - Deep Learning

Feature learning for image classification Kai Yu and Andrew Ng

Andrew Ng

Computer vision is hard

Andrew Ng

Machine learning and feature representations

pixel 1

Learning algorithm

Input

pixel 2

pixel 2

Input space

Motorbikes “Non”-Motorbikes

pixel 1 Andrew Ng

Machine learning and feature representations

handle

wheel

Feature representation

Learning algorithm

Input Feature space

pixel 2

“handle”

Input space

Motorbikes “Non”-Motorbikes

pixel 1

“wheel” Andrew Ng

How is computer perception done? Object detection Image

Low-level vision features

Recognition

Audio classification

Audio

Low-level audio features

Speaker identification

Helicopter control Helicopter

Low-level state features

Action Andrew Ng

Learning representations

Sensor

Feature Representation

Learning algorithm

Andrew Ng

Computer vision features

SIFT

HoG

Textons

Spin image

RIFT

GLOH Andrew Ng

Audio features

Spectrogram

MFCC

Problems of hand-tuned features 1. Needs expert knowledge 2. Time-consuming and expensive Flux Rolloff ZCR to other domains 3. Does not generalize Andrew Ng

Computer vision is more than pictures

Images Video

Thermal Infrared

3d range scans (flash lidar)

Camera array

Audio

Can we automatically learn good feature representations? Andrew Ng

Learning representations

Sensor

Feature Representation

Learning algorithm

Andrew Ng

Sensor representation in the brain

Seeing with your tongue

Human echolocation (sonar)

Auditory cortex learns to see. Auditory Cortex [BrainPort; Martinez et al; RoeAndrew et al.]Ng

Unsupervised feature learning

Find a better way to represent images than pixels. Andrew Ng

The goal of Unsupervised Feature Learning

Unlabeled images

Learning algorithm

Feature representation

Andrew Ng

Tutorial outline

1. Current methods.

2. Sparse coding for feature learning. — Break —

3. Advanced classification. 4. Advanced concepts & deep learning.

Andrew Ng