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CS 7643 Quiz 4 – Concepts|Actual 2026 Update
with complete solutions|Georgia Institute Of
Technology CS 7643 Quiz 4 – Concepts|Notes.
Used this to pass in quiz 4
Covers Structured Representations (Lesson 11), Language Models (Lesson 12), and
Embeddings (Lesson 13).
Conceptual Questions:
• RNNs and LSTMs, how their update rules differ, and what
problems they each have or solve
RNN – Recurrent NNs, are designed to model sequences
• Many to Many: Input a sequence -> output a sequence ; aka sequence
CS7643 QUIZ 4 REAL
, 2|Page
transduction. Other setups know as Encoder - Decoder
OCR – given an image of a text, split that up into individual characters and try
to recognize each one.
• Many to one: sequence as input, one output
Sentiment Analysis – given a piece of text, classify if the author was feeling
positive or negative when writing.
• One to many: one input, sequence as output (eg. Image captioning
model)
• One to one: no sequence involved, typical regression problems.
RNNs solve the problems MLP (Multilayer Perceptron) have when used to model
sequences.
CS7643 QUIZ 4 REAL
CS 7643 Quiz 4 – Concepts|Actual 2026 Update
with complete solutions|Georgia Institute Of
Technology CS 7643 Quiz 4 – Concepts|Notes.
Used this to pass in quiz 4
Covers Structured Representations (Lesson 11), Language Models (Lesson 12), and
Embeddings (Lesson 13).
Conceptual Questions:
• RNNs and LSTMs, how their update rules differ, and what
problems they each have or solve
RNN – Recurrent NNs, are designed to model sequences
• Many to Many: Input a sequence -> output a sequence ; aka sequence
CS7643 QUIZ 4 REAL
, 2|Page
transduction. Other setups know as Encoder - Decoder
OCR – given an image of a text, split that up into individual characters and try
to recognize each one.
• Many to one: sequence as input, one output
Sentiment Analysis – given a piece of text, classify if the author was feeling
positive or negative when writing.
• One to many: one input, sequence as output (eg. Image captioning
model)
• One to one: no sequence involved, typical regression problems.
RNNs solve the problems MLP (Multilayer Perceptron) have when used to model
sequences.
CS7643 QUIZ 4 REAL
