Advanced Algorithm Flashcards
What is the name of the machine learning technique that allows a neural network to focus on specific parts of an input sequence?
a) Attention Model
b) KNN Model
c) Support vector machine Model
d) Random Forest Model
a) Attention Model
What are the two main steps of the Attention Mechanism?
a) Make a separate line between different classes
b) Calculating the attention weights and generating the context vector.
c) Focus in the First part of the input sequence and ignore the rest
d) None of the above
b) Calculating the attention weights and generating the context vector.
What is the advantage of using the Attention Mechanism over a traditional sequence-to-sequence model?
a) Calculating the Weights and Softmax.
b) Focus in the First part of the input sequence and ignore the rest.
c) The Attention Mechanism lets the model focus on specific parts of the input sequence.
d) Make it difficult in Training data
c) The Attention Mechanism lets the model focus on specific parts of the input sequence.
Advantages of Attention Mechanism
a) Reduced Information Loss
b) Improved Performance
c) Interpretability
d) All of the above
d) All of the above
……………………. captures the relevant information from the input sequence needed to generate the output at each time step, allowing the model to focus on different parts of the input sequence as needed.
a) Context Vector
b) Softmax function
c) associated energy
d) global alignment weights
a) Context Vector
What is a Restricted Boltzmann Machine (RBM)?
a) An unsupervised learning deep neural network type
b) A classification task supervised learning algorithm
c) A reinforcement learning model for decision-making
d) A specific kind of regression problem support vector machine
a) An unsupervised learning deep neural network type
Which of the following statements about RBMs is true?
a) RBMs are either fully connected neural networks with no connection
b) limitations or shallow neural networks with only one hidden layer.
c) RBMs are generative models capable of learning an input data’s probability distribution.
d) Supervised learning problems are the main applications of RBMs
c) RBMs are generative models capable of learning an input data’s probability distribution.
What is the key characteristic of the “restricted” nature of RBMs?
a) Connections between visible and hidden units are bidirectional
b) Connections within the visible and hidden layers are sparse
c) Connections between visible units are limited to nearest neighbors
d) Connections between visible and hidden units are not allowed within the same layer
d) Connections between visible and hidden units are not allowed within the same layer
RBMs are trained using which algorithm?
a) Backpropagation
b) Gradient descent
c) Contrastive divergence
d) K-means clustering
c) Contrastive divergence
Which task is RBM commonly used for?
a) Image classification
b) Speech recognition
c) Collaborative filtering
d) Natural language processing
c) Collaborative filtering
In an RBM, what is the purpose of the hidden layer?
a) To reconstruct the input data
b) To capture latent features in the data
c) To perform dimensionality reduction
d) To calculate the error between predicted and actual outputs
b) To capture latent features in the data
Which activation function is commonly used in the hidden layer of an RBM?
a) Sigmoid
b) Relu
c) Tanh
d) Linear
a) Sigmoid
Which of the following is NOT a potential application of RBMs?
a) Collaborative filtering for recommendation systems
b) Dimensionality reduction in feature space
c) Image classification using convolutional RBMs
d) Reinforcement learning for game playing
d) Reinforcement learning for game playing
What is diffusion model?
a) The process of particles moving from an area of low concentration to an area of high concentration.
b) The process of particles, information, or energy moving from an area of high concentration to an area of lower concentration.
c) The process of creating new data samples using a stochastic process.
d) The process of transforming noisy data into clean data samples.
b) The process of particles, information, or energy moving from an area of high concentration to an area of lower concentration.
What are diffusion models in machine learning?
a) Models that generate new data based on the data they are trained on.
b) Models used for image colorization and style transfer.
c) Models that simulate a diffusion process to transform noisy data into clean data
samples.
d) Models that estimate the likelihood of data samples using the score function.
a) Models that generate new data based on the data they are trained on.
Which type of diffusion model is used for probabilistic data generation?
a) Score-Based Generative Models (SGMs)
b) Stochastic Differential Equations (SDEs)
c) Denoising Diffusion Probabilistic Models (DDPMs)
d) Forward Diffusion Models
c) Denoising Diffusion Probabilistic Models (DDPMs)
What is the purpose of data preprocessing in diffusion models?
a) To generate high-quality images with realistic textures.
b) To handle missing data during the generation process.
c) To transform images from one style to another.
d) To prepare the data for subsequent transformations during the diffusion process.
d) To prepare the data for subsequent transformations during the diffusion process.
How do diffusion models generate new data samples?
a) By applying a sequence of invertible transformations to diffuse the data.
b) By estimating the score function of the data distribution.
c) By simulating a diffusion process that transforms noisy data into clean data samples.
d) By applying a sequence of reverse transformations to map the data back to a simple distribution.
a) By applying a sequence of invertible transformations to diffuse the data.
What does BERT stand for in Natural Language Processing (NLP)?
a) Bidirectional Encoder Representations from Transformers
b) Basic Encoding Representations for Text
c) Binary Embedding Representations for Training
a) Bidirectional Encoder Representations from Transformers
How does BERT achieve bidirectionality in understanding text?
a) It reads text from left to right only.
b) It uses Transformer models with attention mechanisms.
c) It relies on recurrent neural networks for context understanding.
b) It uses Transformer models with attention mechanisms.
What are some key advantages of BERT in NLP applications?
a) Handling long-range dependencies and context understanding.
b) Generating high-resolution images from textual descriptions.
c) Performing real-time sentiment analysis on social media data.
a) Handling long-range dependencies and context understanding.
Which pre-training tasks are commonly used to train BERT models?
a) Image classification and object detection.
b) Masked Language Model (MLM) and Next Sentence Prediction
c) Clustering and dimensionality reduction.
b) Masked Language Model (MLM) and Next Sentence Prediction
What are some popular variants or adaptations of BERT used in specific domains?
a) Bio BERT for biomedical text analysis.
b) Geo BERT for geographical information extraction.
c) Music BERT for music recommendation systems.
a) Bio BERT for biomedical text analysis.
What is the definition of GPT?
a) A natural language models.
b) A programming language.
c) A type of computer.
d) A search engine.
a) A natural language models.
What are some of the advantages of GPT?
a) Ease of use.
b) Speed of response.
c) Ability to generate natural conversations.
d) All of the above.
d) All of the above.
What are some of the uses of GPT?
a) Text completion.
b) Translation.
c) Writing creative content.
d) All of the above
d) All of the above
What are some of the disadvantages of GPT?
a) Difficulty of training the model.
b) Potential for bias in the results.
c) All of the above.
d) No disadvantages.
c) All of the above.
What are some of the factors to consider when using GPT?
a) Data quality.
b) Purpose of use.
c) Limitations of the model.
d) All of the above
d) All of the above
Who introduced the YOLO algorithm?
a) Andrew Ng
b) Joseph Redmon
c) Geoffrey Hinton
d) Fei-Fei Li
b) Joseph Redmon
Which of the following is a feature of the YOLO(You Only Look Once) algorithm?
a) Slow processing speed
b) Localized reasoning on small image patches
c) Specific representations learned for each object
d) High detection accuracy and extremely fast processing speed
d) High detection accuracy and extremely fast processing speed
What is the role of the final fully connected layer in the YOLO
architecture?
a) It performs image preprocessing.
b) It predicts only class probabilities.
c) It predicts only bounding box coordinates.
d) It predicts both class probabilities and bounding box coordinates.
d) It predicts both class probabilities and bounding box coordinates.
What does the variable “pc” represent in the YOLO format for bounding box regression?
a) Probability score of the object class.
b) Probability score of the grid containing an object.
c) x-coordinate of the centre of the bounding box.
d) y-coordinate of the centre of the bounding box.
b) Probability score of the grid containing an object.
What is the purpose of using Intersection Over Unions (IOU) in object detection tasks?
a) To define the threshold for selecting relevant grid boxes.
b) To compute the probability score of each grid cell.
c) To calculate the intersection area between predicted and ground-truth bounding boxes.
d) To discard grid boxes with low relevance based on their IOU values.
d) To discard grid boxes with low relevance based on their IOU values.
What does the space-time diffusion model primarily describe?
a) The spread of information or phenomena over both geographical and temporal dimensions
b) The interaction between particles in a vacuum
c) The movement of celestial bodies in space
d) The process of photosynthesis in plants
a) The spread of information or phenomena over both geographical and temporal dimensions
What does “space” mean in the context of the space-time diffusion
model?
a) The cosmos beyond Earth’s atmosphere
b) The area between molecules
c) The geographical dimension, such as distance or location
d) The virtual dimensions in computer programming
c) The geographical dimension, such as distance or location
In the space-time diffusion model, which of the following factors affects the rate of diffusion?
a) The color of the phenomenon being diffused
b) The temperature of the surrounding environment
c) The density of the diffusion medium
d) The speed of light in a vacuum
b) The temperature of the surrounding environment
What distinguishes the space-time diffusion model from other types of diffusion models?
a) It only considers spatial dimensions, ignoring time
b) It only considers temporal dimensions, ignoring space
c) It integrates both spatial and temporal dimensions into a unified framework
d) It focuses exclusively on diffusion in biological systems
c) It integrates both spatial and temporal dimensions into a unified framework
Which of the following phenomena can the space-time diffusion model be used to model?
a) The spread of a rumor across a social network over time
b) The motion of a pendulum
c) The growth of a plant from seed to maturity
d) The behavior of subatomic particles
a) The spread of a rumor across a social network over time
