Table of Contents
Question
Your company wants to create a smartphone application that identifies plants using the phone’s camerA. The company purchases millions of digital images of plants labeled with the species names. You use this initial batch of images to train your artificial neural network. What type of machine learning are you using for your network?
A. self-supervised learning
B. reinforcement learning
C. unsupervised machine learning
D. supervised learning
Answer
D. supervised learning
Explanation
The answer is D. supervised learning.
In supervised learning, the model is trained on a dataset of labeled data. This means that each data point in the dataset has a label that tells the model what the correct output should be. In this case, the labeled data would be the digital images of plants labeled with the species names. The label would be the species name of the plant.
The artificial neural network would then be trained on this labeled data. This means that the network would learn to associate the features of the images (such as the shape of the leaves, the color of the flowers, etc.) with the label (the species name of the plant).
Once the network is trained, it can be used to identify plants. To do this, the network would be given a new image of a plant and would output a prediction of the species name of the plant.
Here are some other examples of supervised learning:
- Classifying images of cats and dogs. The model would be trained on a dataset of images of cats and dogs. Each image would be labeled as either a cat or a dog. The model would then learn to associate the features of the images (such as the shape of the ears, the length of the tail, etc.) with the label (cat or dog).
- Predicting the price of a house. The model would be trained on a dataset of houses. Each house would be labeled with its price. The model would then learn to associate the features of the houses (such as the number of bedrooms, the square footage, etc.) with the label (price).
- Recommending products to customers. The model would be trained on a dataset of customer purchases. Each purchase would be labeled with the products that the customer bought. The model would then learn to associate the features of the customers (such as their age, gender, interests, etc.) with the products that they are likely to buy.
Unsupervised learning, self-supervised learning, and reinforcement learning are all different types of machine learning. Here’s a brief overview of each:
- Unsupervised learning: In unsupervised learning, the model is not trained on labeled data. This means that the model must learn to identify patterns in the data on its own. Unsupervised learning is often used for tasks such as clustering and dimensionality reduction.
- Self-supervised learning: In self-supervised learning, the model is trained on unlabeled data, but the data is augmented with additional information that can be used to label the data. For example, a self-supervised learning model might be trained on a dataset of unlabeled images, but the images might be augmented with labels that indicate the position of objects in the images.
- Reinforcement learning: In reinforcement learning, the model learns to behave in a way that maximizes a reward. The model is not given any explicit instructions on how to behave. Instead, the model must learn to behave by trial and error. Reinforcement learning is often used for tasks such as playing games and controlling robots.
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