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Artificial Intelligence

Unit 1

Optimization

Introduction to Optimization Gradient Descent Stochastic Gradient Descent Adam Optimization

Unit 2

Neural Networks

Introduction to Neural Networks Feedforward Neural Networks Convolutional Neural Networks Recurrent Neural Networks

Unit 3

Backpropagation

Introduction to Backpropagation Backpropagation in Neural Networks

Unit 2 • Chapter 3

Convolutional Neural Networks

Video Summary

Convolutional Neural Networks (CNNs) are a type of neural network that is specifically designed for processing data that has a grid-like structure, such as images. CNNs work by applying a series of convolution operations to the input data, which helps to extract features from the data. This makes CNNs well-suited for tasks such as image classification, object detection, and segmentation.

Knowledge Check

What is the purpose of a convolutional neural network?

to learn the relationships between data points

to classify data points

to generate new data points

to cluster data points

What are the two main types of convolutional neural networks?

feed-forward networks and recurrent networks

feed-forward networks and convolutional networks

recurrent networks and convolutional networks

feed-forward networks and reinforcement learning networks

What are the advantages of convolutional neural networks over other types of neural networks?

they are more efficient at processing data

they are more accurate at making predictions

they are easier to train

they are more generalizable

Artificial Intelligence

Convolutional Neural Networks

Video Summary

Knowledge Check

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Recurrent Neural Networks

Feedforward Neural Networks