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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 3

Transfer Learning for Image Classification

Video Summary

Transfer learning for image classification is a technique that uses a model trained on a large dataset of images to improve the performance of a model on a smaller dataset of images with a similar task. This can be done by initializing the weights of the model with the weights of the pre-trained model, or by fine-tuning the model on the smaller dataset. Transfer learning can help to improve the performance of a model on a small dataset by providing it with a starting point that is already familiar with the task of image classification.

Knowledge Check

In transfer learning, which model is typically used as the feature extractor?

A pre-trained model

A convolutional neural network

A fully-connected neural network

A recurrent neural network

What is the main goal of transfer learning?

To improve the performance of a model on a downstream task

To reduce the amount of training data required for a model

To increase the generalization ability of a model

To make a model more efficient

Which of the following is not a common use case for transfer learning?

Object detection

Natural language processing

Face recognition

Image classification

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Convolutional Neural Networks for Image Classification

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Introduction to SVM