DL-Intro

How does Deep Learning differ from Machine Learning?

• Deep Learning uses decision trees

• Deep Learning cannot handle large datasets

• Deep Learning does not use neural networks

• Deep Learning can automatically extract features from raw data
  

What does a feedforward neural network do?

• Moves data only in one direction, from input to output

• Processes data in a loop

• Stores data in a memory cell

• Reconstructs data from noisy input

What does CNN stand for?

• Computational Neural Network

• Convolutional Neural Network

• Cyclic Neural Network

• Complex Neural Network

What are CNN primarily used for?

• Speech recognition

• Image and video recognition

• Time-series forecasting

• Language translation

What does RNN stand for?

• Recursive Neural Network

• Reverse Neural Network

• Recurrent Neural Network

• Random Neural Network

What type of data is RNN best suited for?

• Static images

• Data with no temporal dependencies

• Randomized datasets

• Sequential data like time-series and text

What is the main purpose of the encoder in an Autoencoder?

• To generate new data

• To compress the input data into a smaller representation

• To reconstruct data from compressed representation

• To classify input data

What does GAN stand for?

• Graphical Autoencoder Network

• Generalized Artificial Network

• Generative Adversarial Network

• Gradient Algorithm Network

What are the two main components of a GAN?

• Encoder and Decoder

• Convolution and Pooling

• Input and Output layers

• Generator and Discriminator

What is the purpose of pooling layers in CNNs?

• To increase the size of feature maps

• To reduce the spatial dimensions of feature maps

• To introduce non-linearity in the network

• To train the network faster