186 lines
5.0 KiB
Plaintext
186 lines
5.0 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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* Softmax can be used to convert inputs into probabilities
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- True
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+ False
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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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* Pred is the values predicted by the network
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+ True
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- False
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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 |