639 lines
17 KiB
Plaintext
639 lines
17 KiB
Plaintext
Lesson 1B Introduction to AI: Pre Quiz
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* A famous 19th century proto-computer engineer was
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- Charles Barkley
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+ Charles Babbage
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- Charles Darwin
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* Weak AI is a system designed to solve many tasks
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- True
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+ False
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* Chat bots are an example of truly intelligent systems
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- false, they are usually designed by a series of rules.
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- true, they are usually considered to be 'intelligent
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+ false, but they are increasingly able to pass Turing tests as they become more sophisticated.
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Lesson 1E Introduction to AI: Post-Quiz
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* A top-down approach to AI is a model of reasoning called
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- strategic reasoning
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+ symbolic reasoning
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- synergistic reasoning
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* A bottom-up approach to AI is based on neural networks
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+ True
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- False
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* The AI Winter occurred in this era
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- 1950s
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- 1960s
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+ 1970s
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Lesson 2B Knowledge Representation and Expert Systems: Pre-Quiz
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* The top-down approach to creating intelligent systems was based on:
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- knowledge seeking and reading
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+ knowledge representation and reasoning
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- knowledge reasoning and seeking
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* Knowledge is the same as information
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- True
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+ False
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* Knowledge is obtained by an:
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+ active learning process
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- passive learning process
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- both of these
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Lesson 2E Knowledge Representation and Expert Systems: Post-Quiz
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* The simplest method of knowledge representation is:
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+ algorithmic
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- symbolic
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- synergistic
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* Scenarios can represent complex situations that can unfold in time
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+ true
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- false
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* Forward inference starts with initial data and then:
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+ executes a reasoning loop
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- looks for a goal
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- starts over
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Lesson 3B Introduction to Neural Networks - Perceptron: Pre-Quiz
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* Early neural networks required
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+ manual weight adjusting
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- terabytes of data
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- special reasoning
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* A simple neuron is also called a 'threshold logic unit'
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+ true
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- false
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* A perceptron is a ___ type of model
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- multi-class classification
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- clustering
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+ binary classification
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Lesson 3E Introduction to Neural Networks - Perceptron: Post-Quiz
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* To train a perceptron, find a weights vector that results in the smallest ___.
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- size
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+ error
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- nodes
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* To minimize the function of weights, you can use gradient descent
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+ true
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- false
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* During gradient descent, each step updates the ___
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- learning rate
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+ weights
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- gradient
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Lesson 4B Neural Networks: Pre Quiz
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* The quality of prediction is measured by Loss function
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+ True
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- False
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* One layer network is capable of classifying ____
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- linearly joined classes
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+ linearly separable classes
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- single layers of classes
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* The method of training multi-layered perceptron is called ____
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+ back propagation
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- multiple propagation
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- front propagation
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Lesson 4E Neural Networks: Post Quiz
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* We use ____ for regression loss functions
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- absolute error
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- mean squared error
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+ all of the above
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* All but one is a type of classification loss function
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- 0-1 loss
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+ binary loss
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- logistic loss
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* Cross-entropy loss is a function that can calculate similarity between two arbitrary probability distributions
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+ True
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- False
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Lesson 5B Frameworks: Pre Quiz
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* Deep Neural Network training requires a lot of computations
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+ True
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- False
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* Overfitting occurs because of ____
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- Not enough testing data
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+ Too powerful model
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- Too much noise in output data
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* Bias errors are caused by our ____ not being able to capture the relationship between training data correctly.
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- model
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+ algorithm
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- computer
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Lesson 5E Frameworks: Post Quiz
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* After compiling our model object, we train by calling ____ function
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+ fit
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- train
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- teach
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* Binary cross-entropy is also called log loss
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+ True
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- False
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* TensorFlow is to ____ while PyTorch is to ____
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- Facebook, Google
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+ Google, Facebook
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- Microsoft, Google
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Lesson 6B Introduction to Computer Vision: Pre Quiz
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* Computer vision aims to allow computers gain understanding of _____
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+ images
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- text
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- computers
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* Python libraries available for image processing includes
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- OpenCV
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- Pillow
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+ a and b
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* Images cannot be represented as NumPy arrays in Python
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- true
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+ False
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Lesson 6E Introduction to Computer Vision: Post Quiz
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* Optical Flow helps us to understand how each pixel on video frames move.
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+ true
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- false
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* _____ computes the vector field that shows where each pixel is moving
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- Sparse Optical Flow
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+ Dense Optical Flow
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- none
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* Resizing and Blurring are steps that can be taken during?
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+ pre-processing
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- training
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- image transformation
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Lesson 7B Convolutional Neural Networks: Pre Quiz
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* To extract patterns from images we use?
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+ convolutional filters
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- extractor
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- filters
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* One of these is not a CNN Architecture
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- ResNet
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- MobileNet
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+ TensorFlow
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* CNN are mostly used for computer vision tasks.
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+ true
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- false
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Lesson 7E Convolutional Neural Networks: Post Quiz
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* Which pooling layer is used "scale down" the size of the image
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- average pooling
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- max pooling
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+ a and b
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* Convolutional networks generalizes much better
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+ True
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- False
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* To train our neural network, we need to convert images to tensors
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+ true
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- false
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Lesson 8B Pre-trained Networks and Transfer Learning: Pre Quiz
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* Transfer learning approach uses untrained models for classification
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- true
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+ false
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* One of these is not a normalization technique?
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+ height normalization
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- weight normalization
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- layer normalization
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* We choose Stochastic Gradient Descent(SGD) in deep learning because classical gradient descent can be ____
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- fast
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+ slow
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Lesson 8E Pre-trained Networks and Transfer Learning: Post Quiz
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* Dropout layers act as a ____ technique
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- gradient boosting
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- training
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+ regularization
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* freezing weights of convolutional feature extractor can be done by ____
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- setting `requires_grad` property to `False`
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- setting `trainable` property to `False`
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+ a and b
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* Batch normalization is to bring values that flow through the ____ to right interval
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- algorithms
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- batches
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+ neural network
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Lesson 9B Autoencoders: Pre Quiz
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* Self-supervised learning uses ____ data for training
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- pre-trained
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+ raw
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- labeled
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* Encoder Network coverts input images into latent spaces
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+ true
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- false
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* VAE is short for?
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- Variable AutoEncoding
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+ Variation auto-encoder
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- Variational automated encoders
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Lesson 9E Autoencoders: Post Quiz
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* Properties of autoencoders include
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- it is data Specific
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- works on unlabeled data
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+ all of the above
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* Auto encoders can be used to effectively remove noise from images
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+ true
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- false
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* Variational auto-encoders loss function does not consist of which of these?
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- reconstruction loss
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- KL loss
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+ TF loss
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Lesson 10B Generative Adversarial Networks: Pre Quiz
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* Generators take vectors and produce ____
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- videos
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+ image
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- gif
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* GANs is short for?
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- General adversarial networks
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- Generative advisor networks
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+ Generative adversarial networks
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* GAN uses ____ neural networks
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- 1
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+ 2
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- 3
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Lesson 10E Generative Adversarial Networks: Post Quiz
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* We can use Batch normalization and BatchNorm1D to stabilize the training
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+ true
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- false
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* Deep Convolutional GAN uses convolutional layers for ____ and ____
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+ generator, discriminator
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- CNN, generator
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- training, testing
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* Problems of GAN training includes
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- Sensitivity to hyperparameters
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- Keeping balance between generator and discriminator
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+ all of the above
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Lesson 11B Object Detection: Pre Quiz
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* Neural networks can only be used to classify images
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- true
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+ false
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* With object detection, we don't just get the class of an object, but also its ____
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- shape
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+ location
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- type
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* How many objects can an object detection model detect?
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- one
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- two
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+ any number
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Lesson 11E Object Detection: Post Quiz
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* An Object detection model gives us
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- object class
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- bounding box
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+ both class and bounding box
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* Which object detection models are faster?
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+ one-pass models
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- region proposal networks
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- Fast R-CNN
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* Which metric can be used to determine how well bounding boxes are aligned?
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- accuracy
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- precision
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+ IoU
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Lesson 12B Segmentation: Pre Quiz
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* How many segmentation algorithm are there?
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- 1
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+ 2
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- 3
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* Segmentation is a _____ task
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+ computer vision
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- natural language processing
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- neural networks
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* Segmentation networks consist of ____ and ____ parts
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- classifier, divider
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+ encoder, decoder
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- generator, discriminator
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Lesson 12E Segmentation: Post Quiz
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* ____ extracts features from an input image
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- decoder
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- generator
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+ encoder
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* ____ transforms input features into mask image
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+ decoder
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- generator
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- encoder
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* SegNet relies on ____ to train multi-layered network
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+ batch normalization
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- height normalization
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- weight normalization
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Lesson 13B Text Representation: Pre Quiz
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* Each word in a Bag of Words is linked to a vector index
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+ true
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- false
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* Text can be represented using _____ approaches
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- 1
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+ 2
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- 3
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* Character level representation represents each _____ as a number
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+ letter
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- word
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- symbol
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Lesson 13E Text Representation: Post Quiz
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* Word level representation represents _____ as a number
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- letter
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+ word
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- symbol
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* N-Grams refers to _____
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- combination of n number of words and symbols
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- combination of n number of letters
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+ combination of n number of Words
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* The main drawback of N-gram is that the vocabulary size grows fast
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+ true
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- false
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Lesson 14B Embeddings: Pre Quiz
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* Embedding is used to represent words with _____ dimensional dense vectors
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+ lower
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- higher
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- average
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* Word2Vec pre-trained embeddings can also be used in place of embedding layer in neural networks
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+ True
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- False
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* Using embedding layer we cannot switch from bag-of-words to embedding bag
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- True
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+ false
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Lesson 14E Embeddings: Post Quiz
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* Word2Vec has _____ main architectures
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- 1
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+ 2
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- 3
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* Word sense disambiguation is a limitation of traditional pretrained embedding representations
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+ True
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- False
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* An embedding layer takes _____ as input
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+ word
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- symbol
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- number
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Lesson 15B Language Modeling: Pre Quiz
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* Which of the following can be considered a language model?
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+ Word2Vec embeddings
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- Embedding layer in RNN
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- RNN used for text classification
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* A language model should be able to ____ the next word in the sentence
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- use
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+ predict
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- guess
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* Language models are trained on
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- language vocabulary
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- specially labeled data
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+ any natural text
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Lesson 15E Language Modeling: Post Quiz
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* Which of the architectures predicts a word from neighboring words?
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+ CBoW
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- Skip-gram
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- N-Gram
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* When we train a CBoW model, we obtain
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- A model that can generate text
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+ Word2Vec embedding vectors
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- Text classification model
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* The CBoW model is based on
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+ Dense neural network
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- Convolutional neural network
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- Recurrent neural network
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Lesson 16B RNN: Pre Quiz
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* RNN is short for?
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- regression neural network
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+ recurrent neural network
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- re-iterative neural network
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* A simple RNN cell has two weight _____
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+ matrices
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- cell
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- neuron
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* vanishing gradients is a problem of _____
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+ RNN
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- CNN
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- KNN
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Lesson 16E RNN: Post Quiz
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* _____ takes some information from the input and hidden vector, and inserts it into state
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- forget gate
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- output gate
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+ input gate
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* Bidirectional RNNs runs recurrent computation in _____
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+ both directions
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- north-west direction
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- left-right direction
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* All RNN Cells have the same shareable weights
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+ True
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- False
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Lesson 17B Generative networks: Pre Quiz
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* RNNs can be for generative tasks
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+ yes
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- no
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* _____ is a traditional neural network with one input and one output
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+ one-to-one
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- sequence-to-sequence
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- one-to-many
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* RNN generates texts by generating next output token for each input token
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+ true
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- false
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Lesson 17E Generative networks: Post Quiz
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* Output encoder converts hidden state into _____ output
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+ one-hot-encoded
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- sequence
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- number
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* Selecting the character with higher probabilities always gives a meaningful text.
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- true
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+ false
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- maybe
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* Many-to-many can also be referred to as _____
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- one-to-one
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+ sequence-to-sequence
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- one-to-many
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Lesson 18B Transformers: Pre Quiz
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* Attention mechanism provides a means of _____ the impact of an inout vector on an output prediction of RNN
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+ weighting
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- training
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- testing
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* BERT is an acronym for
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- Bidirectional Encoded Representations From Transformers
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+ Bidirectional Encoder Representations From Transformers
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- Bidirectional Encoder Representatives of Transformers
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* In positional encoding the relative position of the token is represented by number of steps
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+ true
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- false
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Lesson 18E Transformers: Post Quiz
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* Positional embedding _____ the original token and its position within the sequence
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- separates
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- compares
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+ embeds
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* Multi-Head Attention is used in transformers to give network the power to capture _____ of dependencies
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+ different types
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- same type
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- none
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* In transformers attention is used in _____ instances
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- 1
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+ 2
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- 3
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Lesson 19B Named Entity Recognition: Pre Quiz
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* What does NER stands for?
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- Nearest Estimated Region
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- Nearest Entity Region
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+ Named Entity Recognition
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* An entity always consists of one token
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- true
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+ false
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* To train NER model, we need
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+ Labeled dataset
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- Any natural text
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- Translated texts in two languages
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Lesson 19E Named Entity Recognition: Post Quiz
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* NER model is essentially a ____ model
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- text classification
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+ token classification
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- text regression
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* Which neural network types can be used for NER?
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- RNNs
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- Transformers
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+ Both RNNs and Transformers
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* NER model is a good example of ____ network architecture
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- one-to-one
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- one-to-many
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+ many-to-many
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Lesson 20B Language Models: Pre Quiz
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* What does GPT stand for?
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- Generic Pre-Trained network
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+ Generative Pre-trained Transformers
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- Generic Positional Text
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* What can GPT be used for?
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- Text generation
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- Text classification
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+ Both text generation and other tasks
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* GPT is based on transformer architecture
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+ true
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- false
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Lesson 20E Language Models: Post Quiz
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* What is zero-shot learning?
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+ Getting an answer from pre-trained network
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- Training the network from scratch
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- Training the network only for one epoch
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* Prompt engineering can be used with
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- Zero-shot learning
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- Few-shot learning
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+ Both
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* Which metric can be used to estimate the quality of a language model?
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- accuracy
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- recall
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+ perplexity
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Lesson 21B Genetic Algorithms: Pre Quiz
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* Genetic Algorithms are based on which of the following?
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- mutations
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- Selection
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+ both a and b
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* Crossover allows us to combine two solutions together to obtain a new valid solution
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+ true
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- false
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* Valid solutions to genetic algorithm can be represented as _____
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+ genes
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- neurons
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- cells
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Lesson 21E Genetic Algorithms: Post Quiz
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* Genetic Algorithms can solve which of these tasks
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- Schedule optimization
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- Optimal packing
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+ both of a and b
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* In implementing a genetic algorithm the first step is to randomly select two genes
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- true
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+ false
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* When using Crossover operation the algorithm randomly selects _____ genes
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- 3
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- 1
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+ 2
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Lesson 22B Reinforcement Learning: Pre Quiz
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* To train RL model, we need
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+ Simulation environment
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- Labeled dataset
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- Unlabeled dataset
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* What is a good example of Reinforcement learning:
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- Zero-shot image classification
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- Zero-shot text classification
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+ Learning to play chess
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* When creating RL-based chess engine, we need to
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- use all existing chess matches as a dataset
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+ let computer play against itself many times
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- program exhaustive search algorithm
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Lesson 22E Reinforcement Learning: Post Quiz
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* How does RL training algorithm knows how well it did?
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- It achieves high accuracy
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- Using perplexity metric
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+ Using reward function
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* Which problem(s) is RL is applicable to?
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- With discrete environment
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- With continuous environment
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+ Both
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* In Actor-Critic model, critic predicts
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+ Reward function
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- Best next action
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- Probability of next actions
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Lesson 23B Multi-Agent Modeling: Pre Quiz
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* By modeling the behavior of simple agents, we can understand more complex behaviors of a system.
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+ true
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- false
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* The principle of metasystem transition is derived from:
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- Evolutionary Cybernetics
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- Emergentism
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+ both of these
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* Multi-Agent systems emerged in the ____:
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- 1970s
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- 1980s
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+ 1990s
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Lesson 23E Multi-Agent Modeling: Post Quiz
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* An agent is:
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- an entity that lives alone
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+ an entity that lives in an environment
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- an entity that is intelligent
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* Reactive agents usually have:
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+ simple request-response behavior
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- complex behavior
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- no behavior
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* Multi-agent systems are used in:
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- video production and systems modeling
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- games and automations
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+ both the above
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Lesson 24B Ethical and Responsible AI: Pre Quiz
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* Why we need to worry about Ethical AI?
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- AI is a very powerful tool and can cause harm
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- We need to make sure AI models do not discriminate people
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+ Both
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* Which is the example of interpretable AI?
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+ Expert system
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- Neural network
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- Image classifier
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* It is not ethical to use AI in medicine
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- true
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+ false
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Lesson 24E Ethical and Responsible AI: Post Quiz
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* Why an AI model can discriminate?
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- Because it may become unfriendly
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+ Because datasets were not properly balanced
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- Because developers programmed it in such a way
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* Which of the following is not a principle of Responsible AI?
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- Transparency
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- Fairness
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+ Cleverness
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* Accountability of an AI system means that
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+ there should be a human being involved in taking decisions, who can take responsibility
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- AI system should be held responsible for its actions
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- AI system developers should be held responsible
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* Model fairness is related to
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- Interpretability
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+ Biases
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- Accountability |