CS7643 Exam 4 2025/2026 Exam
Questions with 100% Correct Answers |
Latest Update
Embedding - 🧠ANSWER ✔✔A learned map from entities to vectors that
encodes similarity
Graph Embedding - 🧠ANSWER ✔✔Optimize the objective that connected
nodes have more similar embeddings than unconnected nodes.
Task: convert nodes to vectors
- effectively unsupervised learning where nearest neighbors are similar
- these learned vectors are useful for downstream tasks
,Multi-layer Perceptron (MLP) pain points for NLP - 🧠ANSWER ✔✔- Cannot
easily support variable-sized sequences as inputs or outputs
- No inherent temporal structure
- No practical way of holding state
- The size of the network grows with the maximum allowed size of the input
or output sequences
Truncated Backpropagation through time - 🧠ANSWER ✔✔- Only
backpropagate a RNN through T time steps
Recurrent Neural Networks (RNN) - 🧠ANSWER ✔✔h(t) = activation(U*input
+ V*h(t-1) + bias)
y(t) = activation(W*h(t) + bias)
- activation is typically the logistic function or tanh
- outputs can also simply be h(t)
- family of NN architectures for modeling sequences
Training Vanilla RNN's difficulties - 🧠ANSWER ✔✔- Vanishing gradients
, - Since dx(t)/dx(t-1) = w^t
- if w > 1: exploding gradients
- if w < 1: vanishing gradients
Long Short-Term Memory Network Gates and States - 🧠ANSWER ✔✔- f(t)
= forget gate
- i(t) = input gate
- u(t) = candidate update gate
- o(t) = output gate
- c(t) = cell state
- c(t) = f(t) * c(t - 1) + i(t) * u(t)
- h(t) = hidden state
- h(t) = o(t) * tanh(c(t))
Perplexity(s) - 🧠ANSWER ✔✔= product( 1 / P(w(i) | w(i-1), ...) ) ^ (1 / N)
= b ^ (-1/N sum( log(b) (P(w(i) | w(i-1), ...) ) )
COPYRIGHT©JOSHCLAY 2025/2026. YEAR PUBLISHED 2025. COMPANY REGISTRATION NUMBER: 619652435. TERMS OF USE. PRIVACY
3
STATEMENT. ALL RIGHTS RESERVED
Questions with 100% Correct Answers |
Latest Update
Embedding - 🧠ANSWER ✔✔A learned map from entities to vectors that
encodes similarity
Graph Embedding - 🧠ANSWER ✔✔Optimize the objective that connected
nodes have more similar embeddings than unconnected nodes.
Task: convert nodes to vectors
- effectively unsupervised learning where nearest neighbors are similar
- these learned vectors are useful for downstream tasks
,Multi-layer Perceptron (MLP) pain points for NLP - 🧠ANSWER ✔✔- Cannot
easily support variable-sized sequences as inputs or outputs
- No inherent temporal structure
- No practical way of holding state
- The size of the network grows with the maximum allowed size of the input
or output sequences
Truncated Backpropagation through time - 🧠ANSWER ✔✔- Only
backpropagate a RNN through T time steps
Recurrent Neural Networks (RNN) - 🧠ANSWER ✔✔h(t) = activation(U*input
+ V*h(t-1) + bias)
y(t) = activation(W*h(t) + bias)
- activation is typically the logistic function or tanh
- outputs can also simply be h(t)
- family of NN architectures for modeling sequences
Training Vanilla RNN's difficulties - 🧠ANSWER ✔✔- Vanishing gradients
, - Since dx(t)/dx(t-1) = w^t
- if w > 1: exploding gradients
- if w < 1: vanishing gradients
Long Short-Term Memory Network Gates and States - 🧠ANSWER ✔✔- f(t)
= forget gate
- i(t) = input gate
- u(t) = candidate update gate
- o(t) = output gate
- c(t) = cell state
- c(t) = f(t) * c(t - 1) + i(t) * u(t)
- h(t) = hidden state
- h(t) = o(t) * tanh(c(t))
Perplexity(s) - 🧠ANSWER ✔✔= product( 1 / P(w(i) | w(i-1), ...) ) ^ (1 / N)
= b ^ (-1/N sum( log(b) (P(w(i) | w(i-1), ...) ) )
COPYRIGHT©JOSHCLAY 2025/2026. YEAR PUBLISHED 2025. COMPANY REGISTRATION NUMBER: 619652435. TERMS OF USE. PRIVACY
3
STATEMENT. ALL RIGHTS RESERVED
