Page 362 - AI Ver 3.0 class 10_Flipbook
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5. How does Teachable Machine utilise TensorFlow.js?
Ans. Teachable Machine uses TensorFlow.js, an open-source JavaScript library, to run Machine Learning models directly in a
web browser, eliminating the need for specialised hardware or software installations.
6. What is the purpose of testing the model in Teachable Machine?
Ans. Testing the model in real time allows users to check its performance by using examples, such as pointing the webcam
at an object or making a gesture, to see if the model recognises them correctly.
7. What is the goal of creating a classification model on Coral Bleaching?
Ans. The purpose of developing a classification model for early identification of coral bleaching to safeguard marine
ecosystems.
8. What are the two types of image features mentioned in the text?
Ans. The two types of image features are low-level features (such as colour, texture, and intensity) and high-level features
(such as shapes and objects).
9. Define Convolution.
Ans. Convolution is defined as a simple Mathematical operation that multiplies two numeric arrays of the same dimensions
but different sizes to produce a third numeric array of the same dimensions.
10. What makes No-Code AI tools a game-changer for businesses?
Ans. No-Code AI tools allow businesses to integrate AI solutions without the need for specialised technical teams, reducing
the time and cost involved in AI model development.
11. Explain the function of the Pooling Layer in a CNN.
Ans. The Pooling Layer reduces the dimensions of the input image while still retaining the important features. This will help
in making the input image more resistant to small transformations, distortions and translations. All this is done to
reduce the number of parameters and computation in the network thus making it more manageable and improving
the efficiency of the whole system.
12. Why is non-linearity important in a CNN, and how does ReLU introduce it?
Ans. Non-linearity is important in a CNN because it allows the network to model complex relationships and patterns within
the data, which would be impossible with a purely linear system. The Rectified Linear Unit (ReLU) introduces non-
linearity by transforming all negative values in the feature map to zero, while leaving positive values unchanged. This
non-linearity helps the network to better model complex patterns by allowing it to activate only the important features
and ignore less relevant information (like negative values).
13. How are edges and corners different in image analysis?
Ans. Edges represent transitions between regions in an image, while corners represent points where multiple edges meet.
Corners are more distinctive and accurate for precise localisation, while edges are less distinctive.
14. Why are No-Code AI platforms gaining popularity?
Ans. No-Code AI platforms are gaining popularity because they make AI accessible to a broader audience, allowing
businesses and individuals without coding expertise to develop and deploy AI models.
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