Lesson 1B Introduction to AI: Pre Quiz
* A famous 19th century proto-computer engineer was
- Charles Barkley
+ Charles Babbage
- Charles Darwin
* Weak AI is a system designed to solve many tasks
- True
+ False
* Chat bots are an example of truly intelligent systems
- false, they are usually designed by a series of rules.
- true, they are usually considered to be 'intelligent
+ false, but they are increasingly able to pass Turing tests as they become more sophisticated.

Lesson 1E Introduction to AI: Post-Quiz
* A top-down approach to AI is a model of reasoning called
- strategic reasoning
+ symbolic reasoning
- synergistic reasoning
* A bottom-up approach to AI is based on neural networks
+ True
- False
* The AI Winter occurred in this era
- 1950s
- 1960s
+ 1970s

Lesson 2B Knowledge Representation and Expert Systems: Pre-Quiz
* The top-down approach to creating intelligent systems was based on:
- knowledge seeking and reading
+ knowledge representation and reasoning
- knowledge reasoning and seeking
* Knowledge is the same as information
- True
+ False
* Knowledge is obtained by an:
+ active learning process
- passive learning process
- both of these

Lesson 2E Knowledge Representation and Expert Systems: Post-Quiz
* The simplest method of knowledge representation is:
+ algorithmic
- symbolic
- synergistic
* Scenarios can represent complex situations that can unfold in time
+ true
- false
* Forward inference starts with initial data and then:
+ executes a reasoning loop
- looks for a goal
- starts over

Lesson 3B Introduction to Neural Networks - Perceptron: Pre-Quiz
* Early neural networks required
+ manual weight adjusting
- terabytes of data
- special reasoning
* A simple neuron is also called a 'threshold logic unit'
+ true
- false
* A perceptron is a ___ type of model
- multi-class classification
- clustering
+ binary classification

Lesson 3E Introduction to Neural Networks - Perceptron: Post-Quiz
* To train a perceptron, find a weights vector that results in the smallest ___.
- size
+ error
- nodes
* To minimize the function of weights, you can use gradient descent
+ true
- false
* During gradient descent, each step updates the ___
- learning rate
+ weights
- gradient

Lesson 4B Neural Networks: Pre Quiz
* The quality of prediction is measured by Loss function 
+ True
- False
* One layer network is capable of classifying ____
- linearly joined classes
+ linearly separable classes
- single layers of classes
* The method of training multi-layered perceptron is called ____
+ back propagation
- multiple propagation
- front propagation

Lesson 4E Neural Networks: Post Quiz
* We use ____ for regression loss functions
- absolute error
- mean squared error
+ all of the above
* All but one is a type of classification loss function
- 0-1 loss
+ binary loss
- logistic loss
* Softmax can be used to convert inputs into probabilities
- True
+ False
* Cross-entropy loss is a function that can calculate similarity between two arbitrary probability distributions
+ True
- False

Lesson 5B Frameworks: Pre Quiz
* Deep Neural Network training requires a lot of computations
+ True
- False
* Overfitting occurs because of ____
- Not enough testing data
+ Too powerful model
- Too much noise in output data
* Bias errors are caused by our ____ not being able to capture the relationship between training data correctly.
- model
+ algorithm
- computer

Lesson 5E Frameworks: Post Quiz
* After compiling our model object, we train by calling ____ function
+ fit
- train
- teach
* Binary cross-entropy is also called log loss
+ True
- False
* Tensorflow is to ____ while PyTorch is to ____
- Facebook, Google
+ Google, Facebook
- Microsoft, Google
* Pred is the values predicted by the network
+ True
- False

Lesson 7B Convolutional Neural Networks: Pre Quiz
* To extract patterns from images we use?
+ convolutional filters
- extractor
- filters
* One of these is not a CNN Architecture
- ResNet
- MobileNet
+ Tensorflow
* CNN are mostly used for computer vision tasks.
+ true
- false

Lesson 7E Convolutional Neural Networks: Post Quiz
* Which pooling layer is used "scale down" the size of the image
- average pooling
- max pooling
+ a and b
* Convolutional networks generalizes much better
+ True
- False
* To train our neural network, we need to convert images to tensors
+ true
- false

Lesson 8B Pre-trained Networks and Transfer Learning: Pre Quiz
* Transfer learning approach uses untrained models for classification
- true
+ false
* One of these is not a normalization technique?
+ height normalization
- weight normalization
- layer normalization
* We choose Stochastic Gradient Descent(SGD) in deep learning because classical gradient descent can be ____
- fast
+ slow

Lesson 8E Pre-trained Networks and Transfer Learning: Post Quiz
* Dropout layers act as a ____ technique
- gradient boosting
- training
+ regularization
* freezing weights of convolutional feature extractor can be done by ____
- setting `requires_grad` property to `False`
- setting `trainable` property to `False`
+ a and b
* Batch normalization is to bring values that flow through the ____ to right interval
- algorithms
- batches
+ neural network

Lesson 9B Autoencoders: Pre Quiz
* Self-supervised learning uses ____ data for training
- pre-trained
+ raw
- labeled
* Encoder Network coverts input images into latent spaces
+ true
- false
* VAE is short for?
- Variable AutoEncoding
+ Variation auto-encoder
- Variational automated encoders

Lesson 9E Autoencoders: Post Quiz
* Properties of autoencoders include
- it is data Specific
- works on unlabeled data
+ all of the above
* Auto encoders can be used to effectively remove noise from images
+ true
- false
* Variational auto-encoders loss funnction does not consist of which of these?
- reconstruction loss
- KL loss
+ TF loss

Lesson 10B Generative Adversarial Networks: Pre Quiz
* Gnerators take vectors and produces ____
- videos
+ image
- gif
* GANs is short for?
- General adversarial networks
- Generative advisor networks
+ Generative adversarial networks
* GAN uses ____ neural networks
- 1
+ 2
- 3

Lesson 10E Generative Adversarial Networks: Post Quiz
* We can use Batch normalization and BatchNorm1D to stabilize the training
+ true
- false
* Deep Convolutional GAN uses convolutional layers for ____ and ____
+ generator, discriminator
- CNN, generator
- training, testing
* Problems of GAN training includes
- Sensitivity to hyperparameters
- Keeping balance between generator and discriminator
+ all of the above

Lesson 12B Segmentation: Pre Quiz
* There are ____ segmentation algorithms?
- 1
+ 2
- 3
* Segmentation is a _____ task
+ computer vision
- natural language processing
- neural networks
* Segmentation networks consist of ____ and ____ parts
- classifier, divider
+ encoder, Decoder
- generator, discriminator

Lesson 12E Segmentation: Post Quiz
* ____ extracts features from input image
- decoder
- generator
+ encoder
* ____ transforms input features into mask image
+ decoder
- generator
- encoder
* SegNet relies on ____ to train multi-layered network
+ batch normalization
- height normalization
- weight normalization

Lesson 13B Text Representation: Pre Quiz
* Each word in a Bag of Words is linked to a vector index
+ true
- false
* Text can be represented using _____ approaches
- 1
+ 2
- 3
* Character level representation represents each _____ as a number
+ letter
- word
- symbol

Lesson 13E Text Representation: Post Quiz
* Word level representation represents _____ as a number
- letter
+ word
- symbol
* N-Grams refers to _____
- combination of n number of words and symbols
- combination of n number of letters
+ combination of n number of Words
* The main drawback of N-gram is that the vocabulary size grows fast
+ true
- false

Lesson 14B Embeddings: Pre Quiz
* Embedding is to represent words with _____ dimensional dense vectors
+ lower
- higher
- average
* Word2Vec pre-trained embeddings can also be used in place of embedding layer in neural networks
+ True
- False
* Using embedding layer we cannot switch from bag-of-words to embedding bag
- True
+ false

Lesson 14E Embeddings: Post Quiz
* Word2Vec has _____ main architectures
- 1
+ 2
- 3
* Word sense disambiguation is a limitation of traditional pretrained embedding representations
+ True
- False
* An embedding layer takes _____ as input
+ word
- symbol
- number 

Lesson 16B RNN: Pre Quiz
* RNN is short for?
- regression neural network
+ recurrent neural network
- re-iterative neural network
* Simple RNN cell has two weight _____
+ matrices
- cell
- neuron
* vanishing gradients is a problem of _____
+ RNN
- CNN
- KNN

Lesson 16E RNN: Post Quiz
* _____ takes some information from the input and hidden vector, and inserts it into state
- forget gate
- output gate
+ input gate
* Bidirectional RNNs runs recurrent computation in _____
+ both directions
- nort-west direction
- left-right direction
* All RNN Cells have the same shareable weights
+ True
- False

Lesson 17B Generative networks: Pre Quiz
* RNNs can be for generative tasks
+ yes
- no
* _____ is a traditional neural network with one input and one output
+ one-to-one
- sequence-to-sequence
- one-to-many
* RNN generate texts by generating next output character for each input character
+ true
- false

Lesson 17E Generative networks: Post Quiz
* Output encoder converts hidden state into _____ output
+ one-hot-encoded
- sequence
- number
* Selecting the character with higher probabilities always gives a meaningful text.
- true
+ false
- maybe
* Many-to-many can also be referred to as _____
- one-to-one
+ sequence-to-sequence
- one-to-many

Lesson 23B Multi-Agent Modeling: Pre Quiz
* By modeling the behavior of simple agents, we can understand more complex behaviors of a system.
+ true
- false
* The principle of metasystem transition is derived from:
- Evolutionary Cybernetics
- Emergentism
+ both of these
* Multi-Agent systems emerged in the ____:
- 1970s
- 1980s
+ 1990s

Lesson 23E Multi-Agent Modeling: Post Quiz
* An agent is:
- an entity that lives alone
+ an entity that lives in an environment
- an entity that is intelligent
* Reactive agents usually have:
+ simple request-response behavior
- complex behavior
- no behavior
* Multi-agent systems are used in:
- video production and systems modeling
- games and automations
+ both the above