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Machine Learning Fundamentals

Unit 1

Unit 1: Data Preprocessing

Introduction to Data Preprocessing Data Cleaning Feature Engineering

Unit 2

Unit 2: Image Classification

Introduction to Image Classification Convolutional Neural Networks for Image Classification Transfer Learning for Image Classification

Unit 3

Introduction to SVM Kernel Methods for SVM Hyperparameter Tuning for SVM

Unit 2 • Chapter 2

Convolutional Neural Networks for Image Classification

Video Summary

Convolutional Neural Networks (CNNs) are a type of deep learning model 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 image classification tasks, where the goal is to assign a label to an image based on its content.

Knowledge Check

What are convolutional neural networks?

A type of neural network that is commonly used for image classification

A type of neural network that is commonly used for natural language processing

A type of neural network that is commonly used for time series forecasting

A type of neural network that is commonly used for recommendation systems

What is the difference between a convolutional neural network and a regular neural network?

Convolutional neural networks use convolution operations, while regular neural networks do not

Convolutional neural networks are more complex than regular neural networks

Convolutional neural networks are more efficient than regular neural networks

Convolutional neural networks are more accurate than regular neural networks

What are some of the advantages of using convolutional neural networks for image classification?

They are able to learn local features of images

They are able to learn hierarchical features of images

They are able to learn invariant features of images

All of the above

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Introduction to Image Classification

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Transfer Learning for Image Classification