13 Questions
What is the purpose of today's lecture?
To prepare for the upcoming quiz
Which items should students focus on to study for the quiz?
Lecture 3 – ML Basics (CNN)
What is the main topic discussed in Part A of the recap?
ML Basics (CNN)
What does SISD stand for in the context of computer architecture?
Single instruction operates on single data element
Which architecture is characterized by multiple instructions operating on single data elements?
MISD
In the context of GPU vs. CPU, which characteristic is associated with GPU?
More generalization
What does the Perceptron model primarily aim to understand?
Bias and synapse
What is the purpose of local receptive fields in a CNN?
To make connections in small, localized regions of the input image
What is the benefit of sharing weights and biases in a CNN?
Greatly reduced number of parameters
Which layer typically follows convolution layers in a CNN?
Pooling layer
What is the function of max-pooling in a CNN?
Outputs the maximum value of the region's neurons
What does CNN stand for in the context of this text?
Convolutional Neural Network
What role does shared weights and biases play in CNN?
Reduces the computational complexity and number of parameters
Prepare for the second quiz by reviewing ML basics, hardware architecture, ML with Google Colab, and performance benchmarking covered in lectures 3 to 6. Instructors: Dayane A.Reis, Ph.D. Department of Computer Science and Engineering, University of South Florida, Spring 2024.
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