Q&A210 Exam Questions — All Modules
30 questions per module · Answers hidden behind click · Covers every formula, benchmark, and concept from all 7 modules. Start here the night before the exam.
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Convolution Output Size
W_out = floor((W_in - K + 2P) / S) + 1
K = kernel, P = padding, S = stride
Gradient Descent Update
w ← w − η · ∂L/∂w
η = learning rate, L = loss function
LSTM Cell Update
C_t = f_t ⊙ C_{t-1} + i_t ⊙ C̃_t
f = forget, i = input, C̃ = candidate
LSTM Output
h_t = o_t ⊙ tanh(C_t)
o_t = output gate
TF-IDF
TF-IDF(t,d) = TF(t,d) × log(N/df(t))+1
N = total docs, df(t) = docs with term t
Scaled Dot-Product Attention
Attention(Q,K,V) = softmax(QKᵀ/√d_k)V
Q/K/V = query, key, value matrices
RNN Hidden State
h_t = f(W_h·h_{t-1} + U·x_t + b)
Shared weights at every time step
LoRA Weight Update
W' = W + (α/r)·AB
r = rank (tiny), A∈R^{d×r}, B∈R^{r×k}