Adapting a Pre-trained Network for a New Task
A developer is building a system to identify 10 different types of rare birds from a new dataset containing 50,000 images. They decide to adapt a powerful, pre-existing neural network that was originally trained to classify 1,000 common object categories (e.g., cars, dogs, furniture). Describe the two essential changes the developer must make to the network's structure and training approach to make it suitable for this new task, and briefly explain the reason for each change.
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Data Science
Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Application in Bloom's Taxonomy
Cognitive Psychology
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An engineer needs to build a model to classify 15 types of local wildflowers using a custom dataset of only 900 images. They select a very deep and complex neural network that was previously trained on a dataset of over a million general-purpose images (e.g., animals, vehicles, household objects). The engineer's strategy is to retrain all layers of this complex network from scratch, using only their small wildflower dataset. What is the most likely outcome of this strategy?
You are tasked with building an image classifier for a new, specialized task (e.g., identifying specific types of industrial equipment), but you only have a small, custom dataset. You decide to adapt a model that has already been trained on a very large, general image dataset. Arrange the following steps in the correct logical order to implement this strategy.
Adapting a Pre-trained Network for a New Task