yeah -1,0,1 are valuespbuk said:
why?pbuk said:
pbuk said:
oh yeah that's what i thought. idk what we do between epochs? do we randomize? but i don't think i need to go to that depth tho.pbuk said:
And the first line of the question is "this algorithm is suitable for bipolar input vectors with a bipolar target". Do you see the problem?shivajikobardan said:
Because values ## - \theta \le y_{in} \lt \theta ## satisfy the condtions for both ## y = 0 ## and ## y = -1 ##.shivajikobardan said:
What do you mean "go to that depth"? You need to keep training until you achieve convergence, in this case when ## y = t ## for all training data. And yes, between each training epoch you randomize the order of the training data.shivajikobardan said:
The Perceptron is a type of artificial neural network that uses a single layer of neurons to perform binary classification. It works by assigning weights to the input features and calculating a weighted sum, which is then passed through an activation function to produce a binary output. However, the XOR gate is a non-linear function that cannot be represented by a single layer of neurons, making it impossible for the Perceptron to implement.
No, the Perceptron cannot be modified to implement the XOR gate. This is because the Perceptron's single layer of neurons is limited in its ability to handle non-linearly separable data. To implement the XOR gate, a multi-layer neural network with hidden layers and non-linear activation functions is needed.
Yes, the Perceptron has several limitations. Apart from being unable to implement non-linear functions like the XOR gate, it also struggles with data that is not linearly separable. Additionally, it is prone to overfitting and cannot handle complex datasets with multiple classes.
The activation function in the Perceptron is responsible for transforming the weighted sum of inputs into a binary output. It introduces non-linearity to the model and allows the Perceptron to learn and classify non-linearly separable data. However, since the XOR gate is a non-linear function, it cannot be represented by a single activation function.
Yes, the Perceptron is still used in some modern machine learning applications, but it has been largely replaced by more advanced neural network architectures. However, the concept of the Perceptron is still relevant and serves as the building block for more complex neural networks, such as multi-layer perceptrons and deep learning models.