text stringlengths 3 8.33k | repo stringclasses 52
values | path stringlengths 6 141 | language stringclasses 35
values | sha stringlengths 64 64 | chunk_index int32 0 273 | n_tokens int32 1 896 |
|---|---|---|---|---|---|---|
,
answered: answeredQuestions.length,
correct,
missed: answeredQuestions.length - correct,
unanswered: questions.length - answeredQuestions.length,
accuracy: percent(correct, questions.length),
}
}
export function getMockReviewItems(
questions: Question[],
session: MockExamSession | null,
i... | hands-on-ai | lib/practice.ts | TypeScript | d9bcee5a3841388c9b3b4e678e154c87e521e0b995fd9ff7ad686165d48deea4 | 2 | 896 |
= progress.bookmarks.filter((questionId) =>
questionIds.has(questionId)
).length
return {
total: questions.length,
answered: answeredQuestions.length,
correct,
missed: answeredQuestions.length - correct,
bookmarked,
unanswered: questions.length - answeredQuestions.length,
coverage: ... | hands-on-ai | lib/practice.ts | TypeScript | c029b3a88ac94c232fac106381a7fa5f1fde592c2ceca1fdfac25f21f5ebdbee | 3 | 896 |
: []
if (!selectedChoiceIds.length && answer.isCorrect !== false) {
return null
}
return [
questionId,
{
selectedChoiceIds,
isCorrect: answer.isCorrect === true,
attempts:
typeof answer.attempts === "number" && ans... | hands-on-ai | lib/practice.ts | TypeScript | 91f1ae5fd1f1949e9262486012155f46e277553833568ec8ab6d3b83bea6c85b | 4 | 896 |
unknown
): Record<PracticeMode, string | null> {
const fallback = createDefaultProgress().currentByMode
if (!isRecord(value)) {
return fallback
}
return {
exam: normalizeQuestionId(value.exam) ?? fallback.exam,
practice: normalizeQuestionId(value.practice) ?? fallback.practice,
review: normali... | hands-on-ai | lib/practice.ts | TypeScript | 49b4edc8ee821e2e2163c00fd6e02705434b90aa2b58691f516c485f6b5b1b4a | 5 | 740 |
import type { Question } from "./data"
const sharedPredictionFields = {
origin: "generated_from_slides",
answerSource: "expert_verified",
confidence: 0.86,
examMode: "closed_book",
sourcePriority: "primary",
approvalStatus: "approved",
interactionType: "single_choice",
} as const
export const PREDICTED_... | hands-on-ai | lib/predicted-mock-questions.ts | TypeScript | ad1af229e6e3bb6d85075300ae9000b3b765930eb6c2320948ba90066f142094 | 0 | 896 |
Counting invalid moves as additional actions instead of available action choices.",
coreClosedBookLikely: true,
cognitiveLevel: "calculation",
stem: "A robot moves on a 4x4 grid. Each state has 4 possible actions: up, down, left, right. If the Q-table stores one Q-value per state-action pair, how many Q-val... | hands-on-ai | lib/predicted-mock-questions.ts | TypeScript | ec0e7fa033dda21af4ea303bc0affcf0cbedcfa1109d063e186c3fe3146aa1f4 | 1 | 896 |
lstm", "forget-gate"],
retrievalText: "LSTM forget gate previous cell state kept discarded",
},
{
...sharedPredictionFields,
id: "mock-2026-pred-q006",
sourceRefs: [
{
sourceId: "exam-1-2026-pdf",
note: "Predictive transformer question based on 2026 language-model coverage.",
... | hands-on-ai | lib/predicted-mock-questions.ts | TypeScript | 8a49449fa6cbc661815b555159dc0bb84274f61cbac4285414adcca031329944 | 2 | 896 |
", text: "They can only process text tokens, not images." },
],
correctChoiceIds: ["c1"],
explanation:
"Convolutions exploit locality and weight sharing, which makes them effective for spatial image patterns.",
tags: ["2026-prediction", "cnn", "convolution"],
retrievalText:
"convolution ... | hands-on-ai | lib/predicted-mock-questions.ts | TypeScript | f0b96fdc0220822c71e45817a4f7effaddc17901509c1c98403f26577568086b | 3 | 896 |
: "c2",
text: "Mean squared error is always required for classification.",
},
{ id: "c3", text: "The discount factor gamma." },
{
id: "c4",
text: "BLEU score as the training loss for every classifier.",
},
],
correctChoiceIds: ["c1"],
explanation:
"Softm... | hands-on-ai | lib/predicted-mock-questions.ts | TypeScript | a9f7739f98ab706b21d01a447072c5bd14f22cfb4c7d89eb9e3d9fd37544329a | 4 | 896 |
id: "c1",
text: "It helps represent rare or unseen words using smaller recurring pieces.",
},
{
id: "c2",
text: "It prevents the model from needing positional information.",
},
{
id: "c3",
text: "It converts every language task into reinforcement learning.... | hands-on-ai | lib/predicted-mock-questions.ts | TypeScript | 9b2c986ddd9db477085e2481c95cd47ba7a863187756eb672bbe238eaf072bc4 | 5 | 477 |
import questionBankJson from "./question-bank.json"
import slideIndexJson from "./slide-index.json"
import type { QuestionBank, SlideIndex } from "./schema"
export const questionBank = questionBankJson as QuestionBank
export const slideIndex = slideIndexJson as SlideIndex
export type {
AnswerSource,
Choice,
Co... | hands-on-ai | lib/data/index.ts | TypeScript | 5c8b93949d6c6a29c62293e8ad672fcd4a75af3febaa623ad6d5f349d18f8207 | 0 | 89 |
{
"version": "1.0.0",
"generatedAt": "2026-07-07T20:06:48.206801+00:00",
"course": {
"code": "HOAI2",
"title": "Hands On AI II",
"university": "Johannes Kepler University Linz",
"language": "en"
},
"defaults": {
"choicesPerMultipleChoice": 4,
"generatedQuestionStyle": "Reuse the traine... | hands-on-ai | lib/data/question-bank.json | JSON | 3caf7fa6411c6747fc683aa191208cec757e31b7eadea09f55232270b4dcef7d | 0 | 896 |
unit-06",
"kind": "lecture_slides",
"title": "Unit 6: Introduction to Reinforcement Learning",
"fileName": "Unit6.pdf",
"pageCount": 36,
"lectureNumber": 6
},
{
"id": "exam-1-2026-page-01",
"kind": "exam_image",
"title": "Exam 1 2026 screenshot page 1",
"fil... | hands-on-ai | lib/data/question-bank.json | JSON | a9c242d0696b8366725949cc2bafb63518e4458eda51483d72ad6acba710144f | 1 | 896 |
page-08",
"kind": "exam_image",
"title": "Exam 1 2026 screenshot page 8",
"fileName": "exam-1-2026-page-08.png",
"assetPath": "/sources/exam-1-2026/exam-1-2026-page-08.png",
"pageCount": 1,
"year": 2026,
"pageNumber": 8,
"sourceQuality": "primary_capture"
},
{
... | hands-on-ai | lib/data/question-bank.json | JSON | 8d8f69fec7a2973a6e9634e9859aa10aa7844f6dfc5cfe9133c2259ca1c7f4e4 | 2 | 896 |
{
"id": "group-a-page-05",
"kind": "exam_image",
"title": "Past paper Group A photo page 5",
"fileName": "group-a-page-05.png",
"assetPath": "/sources/past-paper-group-a/group-a-page-05.png",
"pageCount": 1,
"pageNumber": 5,
"sourceQuality": "primary_capture"
},
{... | hands-on-ai | lib/data/question-bank.json | JSON | 65fec019440ace60139cae466e0bae67e59b94e1f10ec182b101907768693805 | 3 | 896 |
": "exam-1-2026-pdf",
"pages": [
1
],
"questionNumber": "1"
}
],
"topicIds": [
"neural-networks"
],
"lectureIds": [
"summary-unit-01",
"summary-unit-02",
"unit-01",
"unit-02"
],
"difficulty": "e... | hands-on-ai | lib/data/question-bank.json | JSON | b2142e41eaaf55fe4fc30164892ceabb50879fcf905ed9b8f6d36c6482e83fea | 4 | 896 |
"During exploitation, the agent chooses actions that given a specific state of the environment seem best."
}
],
"correctChoiceIds": [
"c4"
],
"answerSource": "expert_verified",
"confidence": 0.96,
"examMode": "closed_book",
"sourcePriority": "primary",
"ap... | hands-on-ai | lib/data/question-bank.json | JSON | 434a31e4c433fa79c397b6a72d9bb26beb6ad4d2fe467ded9bf2014c1c85f259 | 5 | 896 |
learning scenario with a 2-dimensional grid of size 5x5. A robot is placed randomly on this grid and must move to a random target destination. The start and target location are guaranteed to be different. There are 4 possible movements: up, down, left and right. If the robot tries to go off the grid, it remains at the ... | hands-on-ai | lib/data/question-bank.json | JSON | 3b0c68cf3c57c12ba2e8c21d970e2704dfaac7ee5bd9274a34b3f90d33c69d4f | 6 | 896 |
real_closed_book_exam",
"sourceRefs": [
{
"sourceId": "exam-1-2026-page-02",
"pages": [
2
],
"questionNumber": "5"
},
{
"sourceId": "exam-1-2026-pdf",
"pages": [
2
],
"questionNumber": "... | hands-on-ai | lib/data/question-bank.json | JSON | 36dc1deb546d2c6fb6c9154387b96e7bffb1b28aea5745b2f399d76df06fdeef | 7 | 896 |
"correctChoiceIds": [
"c3"
],
"answerSource": "expert_verified",
"confidence": 0.96,
"examMode": "closed_book",
"sourcePriority": "primary",
"approvalStatus": "approved",
"selectionRationale": "Transcribed from the Exam 1 2026 screenshot and checked against the supplied... | hands-on-ai | lib/data/question-bank.json | JSON | 2cf56c3f464144234cdb97d8807e9e92d912ab6fec1c47b2a87fd7f88b5569fd | 8 | 896 |
],
"difficulty": "medium",
"examStyleConfidence": 0.95,
"commonTrap": "Separating convolution effects from pooling, normalization, and general image preprocessing.",
"coreClosedBookLikely": true,
"cognitiveLevel": "conceptual",
"interactionType": "single_choice",
"stem": "In co... | hands-on-ai | lib/data/question-bank.json | JSON | 96f4606b086302958392b622a1be5fc5cb7a062ec6df22f539f027adffcc1a1a | 9 | 896 |
"selectionRationale": "Transcribed from the Exam 1 2026 screenshot and checked against the supplied course material.",
"explanation": "Projecting to fewer dimensions generally discards information, even when the lower-dimensional representation is useful.",
"tags": [
"ai-recap-data",
"exam-2... | hands-on-ai | lib/data/question-bank.json | JSON | 199f637f1975573166294652d148331970ccae141bb5dd944a8bcdab0508688c | 10 | 896 |
up input length, output length, hidden state size, and feature dimension.",
"coreClosedBookLikely": true,
"cognitiveLevel": "conceptual",
"interactionType": "single_choice",
"stem": "Which of the following statements is true about sequence data input and standard, feed-forward neural networks?",... | hands-on-ai | lib/data/question-bank.json | JSON | b6b7108adcd707cb465633669c7883d6349e6b69842201801032b3efc584d391 | 11 | 896 |
and checked against the supplied course material.",
"explanation": "Back-propagation through time trains recurrent networks by unrolling the recurrent computation across sequence steps.",
"tags": [
"sequence-models",
"exam-2026",
"closed-book"
],
"retrievalText": "Back-pr... | hands-on-ai | lib/data/question-bank.json | JSON | 4c72b9de0dff429f3d4546867126fe559d78bf1821ca62c398c390cbdd5528c2 | 12 | 896 |
: "14"
}
],
"topicIds": [
"language-models"
],
"lectureIds": [
"summary-unit-02",
"unit-04"
],
"difficulty": "medium",
"examStyleConfidence": 0.95,
"commonTrap": "Confusing embeddings, encoders, decoders, and sampling settings.",
"cor... | hands-on-ai | lib/data/question-bank.json | JSON | 1236efccdc31f2094e3c0f0a97d2d56d25d243c97d2f8808c325af186c8bc112 | 13 | 896 |
course material.",
"explanation": "Q(s, a) estimates the expected return after taking action a in state s and then following a policy.",
"tags": [
"reinforcement-learning",
"exam-2026",
"closed-book"
],
"retrievalText": "Which of the following statements is true about Q-v... | hands-on-ai | lib/data/question-bank.json | JSON | de8d5233a92807802a08cb9bbc14e284b073dc7e35700a83d8ff7c3998f2a7c1 | 14 | 896 |
and policy choice.",
"coreClosedBookLikely": true,
"cognitiveLevel": "conceptual",
"interactionType": "single_choice",
"stem": "Which of the following statements is true about deep Q-learning?",
"choices": [
{
"id": "c1",
"text": "Deep Q-learning can only be use... | hands-on-ai | lib/data/question-bank.json | JSON | ce824b450654933541103f7b5de757ce2a2efe07a0ae4228a427f8956e0e38db | 15 | 896 |
layers or time steps can shrink gradients.",
"tags": [
"neural-networks",
"exam-2026",
"closed-book"
],
"retrievalText": "Which of the following statements is true about the vanishing gradient problem? The vanishing gradient problem can be avoided by rescaling the input values.... | hands-on-ai | lib/data/question-bank.json | JSON | 40fa62dea2b7d8500e91acaff069c983454dcd87d6d625daf9ec33a7328eb85f | 16 | 896 |
decoders, and sampling settings.",
"coreClosedBookLikely": true,
"cognitiveLevel": "conceptual",
"interactionType": "single_choice",
"stem": "What is the general data flow through a Transformer language model with a full encoder-decoder stack?",
"choices": [
{
"id": "c1",... | hands-on-ai | lib/data/question-bank.json | JSON | cd79c3a75453daf9ff7e86e4e1b00769123c6514fad916e3abd1dc9c81dffb2a | 17 | 896 |
,
"tags": [
"reinforcement-learning",
"exam-2026",
"closed-book"
],
"retrievalText": "Which of these is not part of a Markov decision process? States Rewards Actions Losses MDPs are described through states, actions, transition dynamics, and rewards. A loss is an optimization c... | hands-on-ai | lib/data/question-bank.json | JSON | 8b023fb3296cfe7244758119ad9e16dcc3161af73530bcb8f89c980b9eba7d06 | 18 | 896 |
: "passed through an element-wise non-linearity, multiplied by a weight matrix and added to bias weights."
},
{
"id": "c2",
"text": "multiplied by a weight matrix, added to bias weights and passed through an element-wise non-linearity."
},
{
"id": "c3",
... | hands-on-ai | lib/data/question-bank.json | JSON | 027bd39c18b99326fc019f70d3ae3090eaa3f57c3120388f0915b40fe03a9273 | 19 | 896 |
2026 screenshot and checked against the supplied course material.",
"explanation": "The sigmoid derivative is positive and at most 1/4. At input 45 it is also very close to zero, but still positive.",
"tags": [
"neural-networks",
"exam-2026",
"closed-book"
],
"retrievalTe... | hands-on-ai | lib/data/question-bank.json | JSON | 15614dd7ec8b2539e009d9f89d44158ae28278f11c1c5d61fe110e97c0ac0ff9 | 20 | 896 |
origin": "real_closed_book_exam",
"sourceRefs": [
{
"sourceId": "exam-1-2026-page-09",
"pages": [
9
],
"questionNumber": "26"
},
{
"sourceId": "exam-1-2026-pdf",
"pages": [
9
],
"questio... | hands-on-ai | lib/data/question-bank.json | JSON | 10f2d8f68a78c8f3c47f1dff5413e911e23af77cf3ba714e873fa221394b00b8 | 21 | 896 |
the same as regular feed-forward neural networks in case the sequence length is fixed."
},
{
"id": "c3",
"text": "They cannot process sequences of fixed length."
},
{
"id": "c4",
"text": "They are suitable for processing sequences of variable lengt... | hands-on-ai | lib/data/question-bank.json | JSON | cd99f1b3fde648439d24ac0309996bae1e25f75c10e14e7d3fa3040f747b67f3 | 22 | 896 |
],
"questionNumber": "29"
}
],
"topicIds": [
"language-models"
],
"lectureIds": [
"summary-unit-02",
"unit-04"
],
"difficulty": "medium",
"examStyleConfidence": 0.95,
"commonTrap": "Confusing embeddings, encoders, decoders, and sa... | hands-on-ai | lib/data/question-bank.json | JSON | a97be9b534d79c6eaa832f1e15147d986a23e6a4977cefce26b0a4aecb03a840 | 23 | 896 |
": "primary",
"approvalStatus": "approved",
"selectionRationale": "Transcribed from the Exam 1 2026 screenshot and checked against the supplied course material.",
"explanation": "Dropout randomly disables units during training. At inference time, the full network is used with the appropriate scaling b... | hands-on-ai | lib/data/question-bank.json | JSON | 1449858fe3ed2752266527b4ce00d98561c7fd36172b79dc4ea106f7b686480a | 24 | 896 |
,
"topicIds": [
"drug-discovery"
],
"lectureIds": [
"summary-unit-02",
"unit-05"
],
"difficulty": "medium",
"examStyleConfidence": 0.95,
"commonTrap": "Distinguishing structure/activity prediction from direct experimental molecule or protein measurement.... | hands-on-ai | lib/data/question-bank.json | JSON | 484a8006136c4c10e4cde2dacb6ae9cb79e71efdd49556ab3ec0167b695b7489 | 25 | 896 |
.",
"explanation": "Alpha is the learning-rate factor. Values between zero and one mix the old estimate with the new target.",
"tags": [
"reinforcement-learning",
"exam-2026",
"closed-book"
],
"retrievalText": "Consider the update function used during Q-learning: $Q(s_t,a... | hands-on-ai | lib/data/question-bank.json | JSON | 48e6da410f222a607460ea8d0dcf892f56c81ced1ca002a4b04a36ca40281f24 | 26 | 896 |
of the loss with respect to an RNN’s weight. is commonly used to train an RNN. is the method used to train recurrent neural networks. generates (potentially) very deep networks. to optimize the parameters of an RNN. Correct according to the supplied historical answer bank: is commonly used to train an RNN.; to optimize... | hands-on-ai | lib/data/question-bank.json | JSON | efe2cf22cf36fed0bf1d84dcf339202a759004580f234f02353ae14af3e0a61d | 27 | 896 |
"interactionType": "multi_select",
"stem": "Which of the following statements is/are true about the vanishing gradient problem?",
"choices": [
{
"id": "c1",
"text": "Repeated multiplication of gradients smaller than 1 leads to a vanishing gradient."
},
{
... | hands-on-ai | lib/data/question-bank.json | JSON | f84ec6705d2302991b6c9969dd4375b29a18ef0a3a2e54e2eef0b12d46554b36 | 28 | 896 |
"id": "c2",
"text": "rewards can be negative."
},
{
"id": "c3",
"text": "rewards can be positive."
},
{
"id": "c4",
"text": "rewards can be 0."
},
{
"id": "c5",
"text": "reward is associated with the tr... | hands-on-ai | lib/data/question-bank.json | JSON | 2d346b07de01ccd108751b691a23a2dc663c48fa3c04b74595fef706d3a46e3f | 29 | 896 |
exam marker(s): (24), (24 R2), (24 R1), (23), (23 R1), (22 R1).",
"explanation": "Correct according to the supplied historical answer bank: SMILES is not the only form of representing a molecule.; A molecule can have different SMILES representations.; 3D structure cannot be fully represented using SMILES.; A mole... | hands-on-ai | lib/data/question-bank.json | JSON | 5e64160cb164655d00c5c97012cc0f49acd54352d83e43c08961d868c472bebb | 30 | 896 |
position of a sequence.; They are suitable for processing sequences of constant dimensionality (i.e. number of features)..",
"tags": [
"sequence-models",
"historical-bank",
"repeat-6"
],
"retrievalText": "Which of the following statements is/are true about recurrent neural netw... | hands-on-ai | lib/data/question-bank.json | JSON | a1f63617ad2fbe11b514aad4c84c570955d86545716ee4c8cb882da60e777af8 | 31 | 896 |
can be multiple optimal policies. It maximizes the sum of rewards. Correct according to the supplied historical answer bank: There can be multiple optimal policies.; The optimal policy may be obtained via Q-learning.; It maximizes the cumulative reward.; Continuously choosing the actions with the highest immediate rewa... | hands-on-ai | lib/data/question-bank.json | JSON | b455ec4549ca979426229fba6a92b95a3adc706db2aa3a6e2418c423baa78f64 | 32 | 896 |
id": "hist-q080",
"origin": "real_open_book_exam_approved",
"sourceRefs": [
{
"sourceId": "docx-answer-bank",
"questionNumber": "80",
"note": "Section: WORD EMBEDDING; prior markers: (24 R2), (24 R1), (23 R1), (22), (22 R1)"
}
],
"topicIds": [
... | hands-on-ai | lib/data/question-bank.json | JSON | 53d73d0e447f95d103f61c1a330953744a997ac2dacf11c70c1f78d231ab4ef3 | 33 | 896 |
24 R1), (23), (23 R1), (22 R1)"
}
],
"topicIds": [
"neural-networks"
],
"lectureIds": [
"summary-unit-01",
"summary-unit-02",
"unit-01",
"unit-02"
],
"difficulty": "medium",
"examStyleConfidence": 0.72,
"commonTrap": "Rememb... | hands-on-ai | lib/data/question-bank.json | JSON | 7616f15580789f24a132bbc5ce8283c94ee8d71d6eda5bba83e68696da45df17 | 34 | 896 |
"interactionType": "multi_select",
"stem": "The 3D structure of a molecule...",
"choices": [
{
"id": "c1",
"text": "is ambiguous determined by a molecular graph."
},
{
"id": "c2",
"text": "determines the functionality to a high degree."
... | hands-on-ai | lib/data/question-bank.json | JSON | 4b705d7c16798e09bacb4261357d527c75ad6b428f68df4890fd4e8167ee13c1 | 35 | 896 |
bind/react with the target of interest..",
"tags": [
"drug-discovery",
"historical-bank",
"repeat-5"
],
"retrievalText": "Which of the following statements is true about virtual screening (VS)? In VS, a trained neural network predicts whether a molecule in the database is likel... | hands-on-ai | lib/data/question-bank.json | JSON | 877bc4e622d8ab5ec8661eb230889b8222439368e4faba59759fed4bf47ca2e1 | 36 | 896 |
",
"coreClosedBookLikely": false,
"cognitiveLevel": "conceptual",
"interactionType": "multi_select",
"stem": "Which of the following statements are true about gating/gates in an LSTM?",
"choices": [
{
"id": "c1",
"text": "The parameters of the gates are optimize... | hands-on-ai | lib/data/question-bank.json | JSON | 96088597f9276163ea2e72b9fcad23290417f69b734a1b93fa560c08adfdf6df | 37 | 896 |
"correctChoiceIds": [
"c1",
"c2",
"c3",
"c4",
"c5"
],
"answerSource": "explicit_pdf",
"confidence": 0.88,
"examMode": "open_book_reference",
"sourcePriority": "supporting",
"approvalStatus": "approved",
"selectionRationale": "Converted fr... | hands-on-ai | lib/data/question-bank.json | JSON | 4d142f72a716b1c3c615d50b17d76712e6ab4d62d15dbc81fa3a13b87fcae46f | 38 | 896 |
the identity function.; For positive inputs, ReLU is equivalent to Leaky ReLU.; The derivative is easy to compute..",
"tags": [
"neural-networks",
"historical-bank",
"repeat-4"
],
"retrievalText": "Which of the following statements are true about the ReLU activation function? F... | hands-on-ai | lib/data/question-bank.json | JSON | 3ef7f696112c9d4fd03e26c94527b009aa5f335565dd9a623d7b9f513eaf0e8e | 39 | 896 |
": "single_choice",
"stem": "Which of the following formulae is correct regarding the backward pass in a neural network layer, given some input vector x, weight matrix W, bias b and non-linear function f? You can assume that all vectors and matrices are in correct shape.",
"choices": [
{
"... | hands-on-ai | lib/data/question-bank.json | JSON | 896a2d3026429fea9f361bcb4cb2616c7835ad98cd7abf6a35117fce4272f5e0 | 40 | 896 |
responsible for going from the old cell state to the new cell state.; is responsible for countering the vanishing gradient problem.; is, together with the gating, the most important part of an LSTM..",
"tags": [
"sequence-models",
"historical-bank",
"repeat-4"
],
"retrievalText... | hands-on-ai | lib/data/question-bank.json | JSON | 2f1dc35466d8aefeace6e1a70ddc5732725a5fae046f504d4fa9d11fb7e8a32e | 41 | 896 |
"Consider the update function used duringQ-learning: Q(s , a ) ← (1 - α) * Q(s , a ) + α * (r + γ * max Q(s , a ))Which of the following statements are true? γ = 1 means to put a strong emphasis on future rewards. α is the learning rate factor. γ = 0 means to only take the immediate reward into account. α = 0 means tha... | hands-on-ai | lib/data/question-bank.json | JSON | 340d104d751093640260dd010edb55222f7c5f86bc7ec7c3011122a916e20878 | 42 | 896 |
. Language modeling is the task of predicting a word given a context. In order to generate text using an already trained RNN language model, one can sample from the output probability distribution. The sample word from this distribution is used as the next word to generate the word afterwards. Correct according to the ... | hands-on-ai | lib/data/question-bank.json | JSON | 151407fdd8f3a6974ae7a8e3b917519de4469f053bfd05913bf1ba889d549cfc | 43 | 896 |
": "The size of the weight matrix is independent of the sequence length."
}
],
"correctChoiceIds": [
"c1",
"c2"
],
"answerSource": "explicit_pdf",
"confidence": 0.88,
"examMode": "open_book_reference",
"sourcePriority": "supporting",
"approvalStatu... | hands-on-ai | lib/data/question-bank.json | JSON | 14aa1ca1fd33ea162ab9b5519de0d8abe207f2b6ca292eee70533c522e104cea | 44 | 896 |
-01",
"unit-02"
],
"difficulty": "easy",
"examStyleConfidence": 0.72,
"commonTrap": "Remember whether a statement is about the activation, the gradient, or the optimization objective.",
"coreClosedBookLikely": false,
"cognitiveLevel": "conceptual",
"interactionType": "m... | hands-on-ai | lib/data/question-bank.json | JSON | 1b49260837ec1cd6664a72d08098a4883ecf7c5f359769b5c3c5ede79c93a4e4 | 45 | 896 |
"text": "might have several local minima."
},
{
"id": "c5",
"text": "is common in deep learning."
}
],
"correctChoiceIds": [
"c1",
"c2",
"c3",
"c4",
"c5"
],
"answerSource": "explicit_pdf",
"confidence": 0.8... | hands-on-ai | lib/data/question-bank.json | JSON | 79b419140ad8598e3b9ac68655b1b77185bc63eb77933b99ad9a5c577d0bd3a3 | 46 | 896 |
: SUPERVISED MACHINE LEARNING; prior markers: (24), (23), (22 R1)"
}
],
"topicIds": [
"ai-recap-data"
],
"lectureIds": [
"summary-unit-01",
"unit-01"
],
"difficulty": "easy",
"examStyleConfidence": 0.72,
"commonTrap": "Confusing a useful pr... | hands-on-ai | lib/data/question-bank.json | JSON | 1c24df05afba0e512ff01bad32f12d9b7deb403356d61ee08f54ca3829477cd3 | 47 | 896 |
",
"repeat-3"
],
"retrievalText": "The goal of Q-learning is to... approximate the Q-values of the state-action pairs of an MDP (Markov decision process). Correct according to the supplied historical answer bank: approximate the Q-values of the state-action pairs of an MDP (Markov decision process).... | hands-on-ai | lib/data/question-bank.json | JSON | 9d0eee0d339b072e44f6436d3fedc3c87b0921504300f9ab5daf685d38c5e179 | 48 | 896 |
24 R2), (22), (22 R1).",
"explanation": "Correct according to the supplied historical answer bank: multiplied by a weight matrix, added to bias weights and passed through an element-wise non-linearity..",
"tags": [
"neural-networks",
"historical-bank",
"repeat-3"
],
"retr... | hands-on-ai | lib/data/question-bank.json | JSON | e54d48088d0f6a5fa72043d5a6a1d19b85b9d6027fe186feeaf392398e5c5b85 | 49 | 896 |
",
"text": "input -> encoder -> Self-Attention -> Feed-Forward -> decoder"
}
],
"correctChoiceIds": [
"c1",
"c2"
],
"answerSource": "explicit_pdf",
"confidence": 0.88,
"examMode": "open_book_reference",
"sourcePriority": "supporting",
"ap... | hands-on-ai | lib/data/question-bank.json | JSON | 45fb62444ce6c4a66d6e4ff10b204db66e895c3f28f34f0cac70af390136b226 | 50 | 896 |
displayed in line plots. Every data can theoretically be represented as a sequence. There are dedicated machine learning models that deal with sequence data. Typically, there is some sort of order in the data (time, position, etc.). Correct according to the supplied historical answer bank: Sequence data is typically di... | hands-on-ai | lib/data/question-bank.json | JSON | da66d4985ba66caf1418d32bd2614967dcb78f9d465c6649a9ca6d6068a44aed | 51 | 896 |
"id": "hist-q074",
"origin": "real_open_book_exam_approved",
"sourceRefs": [
{
"sourceId": "docx-answer-bank",
"questionNumber": "74",
"note": "Section: RNN TYPES; prior markers: (24), (22)"
}
],
"topicIds": [
"sequence-models"
],
... | hands-on-ai | lib/data/question-bank.json | JSON | 7f82cb0b0fc2932112b45b7f11995705d8369435cab94e01a98bcc2e3c42bb22 | 52 | 896 |
"summary-unit-02",
"unit-06"
],
"difficulty": "medium",
"examStyleConfidence": 0.72,
"commonTrap": "Separating immediate reward, cumulative return, Q-values, and policy choice.",
"coreClosedBookLikely": false,
"cognitiveLevel": "conceptual",
"interactionType": "multi_se... | hands-on-ai | lib/data/question-bank.json | JSON | ea55b5ec91e2dcef907be0253cc952154857e5be72c276b7df11f70aea3a428f | 53 | 896 |
current highest Q-value."
},
{
"id": "c4",
"text": "Depending on the environment, choosing a suboptimal action might lead to Q-learning getting stuck."
}
],
"correctChoiceIds": [
"c1",
"c2",
"c3",
"c4"
],
"answerSource":... | hands-on-ai | lib/data/question-bank.json | JSON | d1b97ab60cbf2501c07e9c3cdd660ab6fc19fe9c353a91aaff4c3468cf9d9109 | 54 | 896 |
historical-bank",
"repeat-2"
],
"retrievalText": "Which of the following statements are correct regarding generative models in the area of drug discovery? Generative models generate new molecules according to the target or desired bioactivity. Generative models are a promising addition since the che... | hands-on-ai | lib/data/question-bank.json | JSON | 5df668d81568936a7645e8931e3068c79e6a38bb3513dc37ba3a0379a927507a | 55 | 896 |
"unit-02"
],
"difficulty": "medium",
"examStyleConfidence": 0.72,
"commonTrap": "Remember whether a statement is about the activation, the gradient, or the optimization objective.",
"coreClosedBookLikely": false,
"cognitiveLevel": "conceptual",
"interactionType": "multi_select"... | hands-on-ai | lib/data/question-bank.json | JSON | 637fd9518514d1cf7f513be79217c53b8928ce8440044e47ddb1d501dfaf8b03 | 56 | 896 |
": [
"ai-recap-data",
"historical-bank",
"repeat-2"
],
"retrievalText": "Which of the following statements are correct regarding tabular data?... Columns in the table are called features. Rows in the table are called samples. Tabular data might contain duplicate rows. There can be mo... | hands-on-ai | lib/data/question-bank.json | JSON | 7c764e49b91180d4807649abdb24015d340f81635d17df3d2174d032f5542c04 | 57 | 896 |
-q009",
"origin": "real_open_book_exam_approved",
"sourceRefs": [
{
"sourceId": "docx-answer-bank",
"questionNumber": "9",
"note": "Section: SUPERVISED MACHINE LEARNING; prior markers: (24 R1), (23 R1)"
}
],
"topicIds": [
"ai-recap-data"
... | hands-on-ai | lib/data/question-bank.json | JSON | 15c27cdcaaf650167821efa52361480413ec2ecbd3c312b38147f42f3c57a777 | 58 | 896 |
"explanation": "Correct according to the supplied historical answer bank: It allows to get a better estimate of the generalization error.; It allows to detect overfitting..",
"tags": [
"ai-recap-data",
"historical-bank",
"repeat-2"
],
"retrievalText": "Why is it a good idea to ... | hands-on-ai | lib/data/question-bank.json | JSON | 4f996283f193ff80e2e49be2fceb418275560adf92881e184bb9e140ea9c7d13 | 59 | 896 |
Section: ACTIVATION FUNCTIONS; prior markers: (24), (23)"
}
],
"topicIds": [
"neural-networks"
],
"lectureIds": [
"summary-unit-01",
"summary-unit-02",
"unit-01",
"unit-02"
],
"difficulty": "medium",
"examStyleConfidence": 0.72,
... | hands-on-ai | lib/data/question-bank.json | JSON | 23560b199ae86870d54c975170510d45fd7848e8630b91e3c5caa7aa58d11845 | 60 | 896 |
hidden state (h0) is typically set to 0. The input sequence must be fed timestep by timestep to the RNN. Correct according to the supplied historical answer bank: The input sequence must be fed timestep by timestep to the RNN.; The initial hidden state (h0) is typically set to 0.; There is only one weight matrix that i... | hands-on-ai | lib/data/question-bank.json | JSON | 09dc5763b3f766e0532d0badff72f48c1f4cd3f764360d698011c1e97408fd52 | 61 | 896 |
layers."
}
],
"correctChoiceIds": [
"c1",
"c2"
],
"answerSource": "explicit_pdf",
"confidence": 0.88,
"examMode": "open_book_reference",
"sourcePriority": "supporting",
"approvalStatus": "approved",
"selectionRationale": "Converted from the s... | hands-on-ai | lib/data/question-bank.json | JSON | 50744a64e6c1554f992fe92b9e9945afdd9e84bd9c6ef3e043c63c80c6afd526 | 62 | 896 |
eases the computational demand.; can reduce the computational load and memory requirements.; is common in convolutional neural networks.."
},
{
"id": "hist-q002",
"origin": "real_open_book_exam_approved",
"sourceRefs": [
{
"sourceId": "docx-answer-bank",
"questionNu... | hands-on-ai | lib/data/question-bank.json | JSON | 34da3cf0fb59dcfb0255cf811df6bbceaf7dec71668a3cf26eb7e5e3a45544d0 | 63 | 896 |
the one-to-many RNN type?",
"choices": [
{
"id": "c1",
"text": "Given an input of length Tx = 1, the output/prediction length is Ty > 1."
}
],
"correctChoiceIds": [
"c1"
],
"answerSource": "explicit_pdf",
"confidence": 0.88,
"examMode... | hands-on-ai | lib/data/question-bank.json | JSON | 98a65a1991d413527d6b440d70291cabb99f2eaf255c98bee88b00090f891acf | 64 | 278 |
export type QuestionOrigin =
| "real_closed_book_exam"
| "real_open_book_exam_approved"
| "generated_from_slides"
export type AnswerSource =
| "explicit_pdf"
| "expert_verified"
| "inferred_from_slides"
| "inferred_from_exam_context"
| "generated"
export type InteractionType =
| "true_false"
| "si... | hands-on-ai | lib/data/schema.ts | TypeScript | 1b33f9006f93393eb27d900cac4489102082330fa6430d0bdd529dd61209cf54 | 0 | 475 |
{
"version": "1.0.0",
"generatedAt": "2026-07-07T20:06:48.206801+00:00",
"chunks": [
{
"id": "summary-pdf-p002",
"sourceId": "summary-pdf",
"lectureId": "summary-unit-02",
"page": 2,
"title": "Summary page 2",
"text": "Unit 1 – Recap of Hands-on AI I Tabular Data • Data org... | hands-on-ai | lib/data/slide-index.json | JSON | 2505e171a87e3d96d044734b1b4768385aedfc68df3d8607509a0db32314125d | 0 | 896 |
an. • Every dataset can theoretically be represented as a sequence. • Typically has an order: time, position, word order, etc. • Commonly visualized in line plots. • Dedicated ML models (RNNs, LSTMs) handle sequence input. • Feed-forward NNs: sequence relations are lost. • Variable-length sequences must be preprocessed... | hands-on-ai | lib/data/slide-index.json | JSON | 19ff0e19061237d0ea5c126d00aadd6f245a99993c5c1be510f12c2024c189e0 | 1 | 896 |
},
{
"id": "summary-pdf-p006",
"sourceId": "summary-pdf",
"lectureId": "summary-unit-02",
"page": 6,
"title": "Summary page 6",
"text": "Gradient Descent • Iterative optimization method. • Update rule: • Learning rate η: controls step size. • Moves in direction of negative gradie... | hands-on-ai | lib/data/slide-index.json | JSON | bad4c3657d7a0588d28ca778792b0d33be502eb0d51a3e4e27bafa03b568bb70 | 2 | 896 |
not shown in exam statement, but true). 2. 50×50 image, 4 channels, 2 kernels (3×3) • Input: 50×50, depth=4. • Kernel: 3×3×4.",
"keywords": [
"state",
"kernel",
"feature map",
"image"
],
"retrievalText": "Convolutional Networks. Let’s apply these rules: 1. 30×30 image, ... | hands-on-ai | lib/data/slide-index.json | JSON | 740ac553dd45291101d2928e08bf6c276dc1721c33348a61fe86522d36c68bdf | 3 | 896 |
. 5. 8×8 image, 1 channel, 2 kernels (4×4) • Input: 8×8, depth=1. • Kernel: 4×4×1. • 2 kernels → 2 feature maps. • Resolution: 8−4+1=58 - 4 + 1 = 58−4+1=5. • So → 2 maps of size 5×5. 6. 6×6 image, 3 channels, 1 kernel (3×3) • Input: 6×6, depth=3."
},
{
"id": "summary-pdf-p010",
"sourceId": "summary-... | hands-on-ai | lib/data/slide-index.json | JSON | 62ca1358c8bdf17680aaada5d9a20ba45c093be318f48487208b72aa1b671929 | 4 | 896 |
= 2.4 Hidden activations: [1.8, 2.4] Step 2: Calculate output y=h1∗w1+h2∗w2+bias Output neuron weights: w1=0.7, w2=0.5, bias=0.1 y = 1.8*0.7 + 2.4*0.5 + 0.1 = 2.56 • Predicted score = 2.56 Compare to true score (calculate error) • True score = 3 • Error = 3 - 2.56 = 0.44 • Loss function: Mean Squared Error (MSE) → (0.4... | hands-on-ai | lib/data/slide-index.json | JSON | b4cc3332e58dd3f985e3798a902643d9a9fe5e9d80084c9d2eec13297c568ba1 | 5 | 896 |
(since distributions change). o At test time, use statistics collected from the training set. o Noise introduced by batch statistics can help prevent overfitting. Regularization • Which of the following statements are true about L1 and L2 regularization? o L1 regularization: adds the sum of absolute weights to the loss... | hands-on-ai | lib/data/slide-index.json | JSON | 0b66e5564b46a4d0a8e51f43b35595b9573ae2082e8cadee8b2422f289f636b8 | 6 | 896 |
,
"title": "Summary page 16",
"text": "o A large learning rate helps exploration; a small one helps fine-tuning near minima. o Starting large and decaying the rate balances exploration vs. exploitation. o Common schemes include step decay, exponential decay, and linear decay. Convex vs. Non-convex Functions... | hands-on-ai | lib/data/slide-index.json | JSON | 97b2d95423d6b11336f5ac45ebe4bfb21786683ac4fb5c986e8b115d2ab451d9 | 7 | 896 |
p018",
"sourceId": "summary-pdf",
"lectureId": "summary-unit-02",
"page": 18,
"title": "Summary page 18",
"text": "UNIT 3 — Recurrent Neural Networks Sequence data • A sample s is a sequence of length T with D features at every timestep. • D (feature dimensionality) is constant across samp... | hands-on-ai | lib/data/slide-index.json | JSON | f00f842cd756fe25ca39834c2b3ca144de3d7207975a5ac203e783ac89fae18e | 8 | 896 |
be unrolled in time into a deep feed-forward graph with shared weights. • Training commonly uses Backpropagation Through Time (BPTT): compute gradients on the unrolled graph and update shared weights. • BPTT makes the effective depth proportional to sequence length, which can cause vanishing or exploding gradients. Van... | hands-on-ai | lib/data/slide-index.json | JSON | c696aac2902e455ef6857586b24f9b779263d2ba7062120df97cdaff13c37d64 | 9 | 896 |
1) — e.g., image captioning, music generation (single input → sequence output). • Many-to-One (Tx>1, Ty=1) — e.g., sentiment classification (sequence → single label).",
"keywords": [
"state",
"sequence",
"rnn",
"lstm",
"gate",
"gating",
"hidden state",
... | hands-on-ai | lib/data/slide-index.json | JSON | 0487e52b5a7e1977a1b0b2f429046a08b93e1535aa943faf3476054d674e0189 | 10 | 896 |
,
"transformer",
"sequence",
"rnn",
"lstm",
"hidden state",
"gradient"
],
"retrievalText": "Reinforcement Learning and Q-Learning. • Many-to-Many equal (Tx=Ty) — e.g., POS tagging, Named Entity Recognition (sequence → sequence, same length). • Many-to-Many dif... | hands-on-ai | lib/data/slide-index.json | JSON | ef56b9fb38a9c482859d83dfeb3952bf2541e67a014ab46dc80349ca052655ad | 11 | 896 |
losses over sequence/batch. Q: What is perplexity? • Perplexity = exp(L). • Lower = better model. • Interpreted as geometric mean of token probabilities. • Automatically normalized for sequence length. • Not a measure of semantic quality. Q: Which statements about text generation are true? • Sampling uses predicted pro... | hands-on-ai | lib/data/slide-index.json | JSON | fb8702d4e547bb73fd22d18c92699a2be96af3d1f3537be97c741bdead40f834 | 12 | 896 |
Which statements about word2vec are true? • Similar words map to similar regions. • Similarity measured via cosine similarity. Q: What is tokenization in modern LMs? • Tokens can be subwords/characters. • Handles out-of-vocabulary words. • Examples: BPE, SentencePiece. RNN Language Models Q: What is the data flow in an... | hands-on-ai | lib/data/slide-index.json | JSON | 8c58e755e9fc7060d1f8a4555d2e246014671a643a6d68915b42a98a86cae309 | 13 | 896 |
.g., translation). Q: Masked vs unmasked attention? • Unmasked: encoder, all tokens can attend to all others. • Masked: decoder, tokens attend only to previous ones (for autoregressive generation). Scaling & Energy (could be asked) Q: How do model parameters scale?",
"keywords": [
"state",
"embedd... | hands-on-ai | lib/data/slide-index.json | JSON | e0efbfdd7d1babb39d34e2a85c7cdb83d512e034b5a7b632f8fa6806099e7074 | 14 | 896 |
many years. • is an extremely extensive process.",
"keywords": [
"drug",
"molecule",
"state"
],
"retrievalText": "AI for Drug Discovery. Unit 5 - DRUG DISCOVERY Q: Which of the following statements are correct regarding generative models in the area of drug discovery? • Generat... | hands-on-ai | lib/data/slide-index.json | JSON | 666f454f7f8ec96a5ee827dca4fbf0b85df5c48f0397b6c53efdbbb51df37193 | 15 | 896 |
the order of 10^60. TARGET PREDICTION (HTS vs VS) Q: High-Throughput Screening (HTS) • HTS is experimental testing of many compounds in assays; it is expensive and time-consuming. • HTS provides the raw experimental data that populates public assay databases. Q: Virtual Screening (VS) • VS uses computational models (e.... | hands-on-ai | lib/data/slide-index.json | JSON | bd5c2fafce2f0a11639af53f0cdf960cdb088144be0b0abfd40f00f1bb1f865f | 16 | 896 |
encode 3D conformation. • A molecular graph can be transformed into a SMILES string and vice versa. Q: MOL / SDF files • MOL / SDF are file formats that encode atoms, coordinates and bond information."
},
{
"id": "summary-pdf-p032",
"sourceId": "summary-pdf",
"lectureId": "summary-unit-02",
... | hands-on-ai | lib/data/slide-index.json | JSON | d1f6573034f4c6c4b0d6757b6264f154ba0594845d521eb0b0510e78b7794499 | 17 | 896 |
made of amino-acid sequences (typically from ~50 up to ~2000 residues) performing diverse biological functions (enzymes, signaling, structure, transport, etc.). Q: Protein folding problem • The protein folding problem: predict a protein’s 3D structure from its amino-acid sequence. • The number of possible folds/configu... | hands-on-ai | lib/data/slide-index.json | JSON | 2084bdbe45c6882fa93eafce5dbd61234f8ad109830ab31083dfa07a6061536f | 18 | 896 |
Key ready facts to memorize (short bullets) • Drug development costs ≈ $0.5–2B; discovery stage ≈ 30% of cost; typical success rate ≈ 10%. • Eroom’s law: drug-discovery costs have been increasing (roughly doubling every ~10 years). • Chemical space for drug-like molecules is astronomically large (≈ 10^60 for ~40- atom ... | hands-on-ai | lib/data/slide-index.json | JSON | 7bb61f01c9939e0e20c7901311d761ce033ef9588994a3836fdabd4b14f3fc91 | 19 | 896 |
Find a policy π(s) mapping states → actions that maximizes cumulative reward. Q: What is a common challenge in RL? • Reward signals are often sparse (only some state transitions give feedback). The Goal of RL Q: What is a policy? • The behavior function of an agent, specifying actions in each state. Q: What is the goal... | hands-on-ai | lib/data/slide-index.json | JSON | ed0be69891c99e5ffd3e4ee1cd89d978f3e717982bdcb2c0a63cb39e0e5b5eb7 | 20 | 896 |
4. Update Q(s,a) using the update rule. 5. Repeat until convergence. Q: How to balance exploration and exploitation? • Use ε-greedy: with probability ε pick random action (exploration), else pick best action (exploitation). Limits of Q-Learning Q: Why doesn’t Q-learning scale well? • Large state/action spaces → Q-table... | hands-on-ai | lib/data/slide-index.json | JSON | 55176af3f4cce57a6cd8f5d24f95a512b16ea0c0d39008cce94ac55bcdf8d733 | 21 | 896 |
RL Q: What is the difference between narrow and general RL systems? • Narrow AI: works well in games/environments with clear rules, rewards, and full observability. • General AI: real-world problems are messy (uncertainty, sparse/ambiguous rewards, long time horizons). Q: Why is real-world RL harder? • Sparse or ambigu... | hands-on-ai | lib/data/slide-index.json | JSON | 651799fa48c542c65d491aefc308335e5e5dd616e8fe996fe76f41a096a2cbe9 | 22 | 896 |
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