Course intro

Word Meaning and Representation

denotational semantics

wordnet (nltk): word meanings, synonym, relationships, hierarchical

pb: missing nuance, missing new meanings, required human labor, can't compute word similarity

Traditional NLP (untill 2012):

  • each words are discrete symbols — "localist representation"
  • use one-hot vectors for encoding

  • pbs with one-hot vecotrs:
  • large ...

This week: seq2seq.

I-Various sequence to sequence architectures

Basic Models

e.g. Machine translation
encoder network: many-to-one RNN
decoder network: one-to-many RNN

This architecture also works for image captioning: use ConvNet as encoder

Difference between seq2seq and generating new text with language model: seq2seq don't randomly choose a translation ...





I - Introduction to Word Embeddings

Word representation
So far: representing words with one-hot encoding → word relationships are not generalized.
⇒ want to learn a featurized representatin for each word as a high-dim vector

→ visualize word embeddings in 2-dim space, e.g. via t-SNE

Using word embeddings

example: NER
transfer learning: using ...

week1

Created Friday 02 February 2018

Why sequence models

examples of seq data (either input or output):

  • speech recognition
  • music generation
  • sentiment classification
  • DNA seq analysis
  • Machine translation
  • video activity recognition
  • name entity recognition (NER)

→ in this course: learn models applicable to these different settings.

Notation

motivating example: NER (Each ...

This week: two special application of ConvNet.

I-Face Recognition

What is face recognition

Face verification & face recognition

  • verification: input = image and ID → output whether the image and ID are the same.
  • recognition: database = K persons, input = image → output = ID of the image among the K person or "not recognized".

→ the ...

Object Localization

Classification VS. Localization VS. Detection

classification with localization
Apart from softmax output (for classification), add 4 more outputs of bounding box: b_x, b_y, b_h, b_w.

Defining target label y in localization
label format:
P_c indicating if there's any object
bounding box: b_x, b_y, b_h, b_w
class proba ...



I-Case studies

Why look at case studies?

Good way to get intuition of different component of CNN: case study & reading paper.
Outline

  • classic networks:
    • LeNet-5
    • AlexNet
    • VGG
  • ResNet (152-layer NN)
  • Inception

Classic Networks

LeNet-5(1998)

Goal: recognize hand-written digits.
image → 2 CONV-MEANPOOL layers, all CONV are valid (without padding) → 2 ...


I-Error Analysis

Carrying out error analysis

"Error analysis": manually examine the mistakes → get insight of what's next.

"ceiling on performance"

example:
cat classification, found some false-positives of dog pictures. → should you try to make ML system better on dog or not ?
→ error analysis:

  • get ~100 false positive examples
  • count ...

I-Introduction to ML Strategy

Why ML Strategy

A lot of ideas of improving ML performance: strategy on how to choose.

→ how to figure out which ones to pursue and which ones to discard ?

Orthogonalization

How to tune hyperparams & what to expect.

TV tuning example: each knob does only one thing ...