This week: neural net fundamentals

Classification Setup and Notation

training data:

softmax classifier

(linear classifier — hyperplane):

ith row of the param W: weight vector for class i to compute logits:

prediction = softmax of f_y:

cross-entropy

goal: for (x, y), maximize p(y|x) ⇒ loss for (x, y) = -log p(y …

More Matrix Gradients

⇒

Deriving Gradients wrt Words

pitfall in tetraining word vectors: if some word is not in training data, but other synonyms are present ⇒ only the synonyms word vectors are moved

takeaway:

Backpropagation

backprop:

• apply (generalized) chain rule
• re-use shared stuff

computation graph

⇒ Go backwards along edges, pass along …

More on Word2Vec

parameters θ : matrix U and V (each word vec is a row):

and the predictions don't take into account the distance between center word c and outside word o. ⇒ all word vecs predict high for the stopwords.

Optimization Basics

min loss function: J(θ)

gradient descent

direction …

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 dimension …

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, but …

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: c_1 …

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 …