- #1
shivajikobardan
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Because we use updated bias and not the original bias? Please clear my confusion
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Isn't this table for perceptron of AND gate wrong for B?
In summary, The perceptron is a single-layer neural network that can accurately represent the behavior of an AND gate by setting the weights and bias in a specific way. It can also be used for other logic gates and can learn from data through training. However, it is limited in its ability to learn more complex patterns compared to traditional neural networks with multiple layers.
Because we use updated bias and not the original bias? Please clear my confusion
- #2
pbuk
Science Advisor
Homework Helper
Gold Member
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The model answer appears to use the updated weights and bias in columns w_1, w_2 and B, Edit: which is what I would expect (i.e. batch size = 1).
If you think some workings are wrong then you should present your own workings and point out the difference, don't leave us to guess at what you think the answer should be.
If you think some workings are wrong then you should present your own workings and point out the difference, don't leave us to guess at what you think the answer should be.
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1. Why is the table for perceptron of AND gate wrong for B?
The table for perceptron of AND gate is wrong for B because it does not accurately represent the expected output for the AND gate. The perceptron is typically used for classification tasks and relies on a linear decision boundary, which may not be suitable for modeling the AND gate.
2. How can I fix the table for perceptron of AND gate for B?
The table for perceptron of AND gate can be fixed by adjusting the weights and biases of the perceptron. By tweaking these parameters, the perceptron can learn to accurately classify input data for the AND gate.
3. Can I use a perceptron for other logic gates besides the AND gate?
Yes, perceptrons can be used for other logic gates such as OR, NOT, and XOR gates. However, the weights and biases would need to be adjusted accordingly to accurately model the desired logic gate.
4. Is the perceptron the only type of neural network that can model logic gates?
No, there are other types of neural networks such as multi-layer perceptrons and deep neural networks that can also model logic gates. However, perceptrons are a simpler and more basic form of neural network, making them suitable for simple tasks like logic gates.
5. Why is it important to understand the limitations of using a perceptron for logic gates?
Understanding the limitations of using a perceptron for logic gates is important because it can help us understand the capabilities and limitations of different types of neural networks. It also showcases the importance of choosing the right type of neural network for the task at hand.
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