How to Create Manually a Neural Network with Functions and Blobs - Cuardach Físeáin

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Artificial Intelligence | AI on Instagram: "Activation functions are crucial for neural networks because they add a non-linearity, allowing the network to learn complex patterns. The sigmoid function, which outputs values between 0 and 1, is useful for making predictions in binary classification tasks. ReLU, which outputs the input if it’s positive and zero if it’s not, is popular because it helps the network learn faster and avoids some training problems. These functions let neural networks cre

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Insightforge | AI & Data Science on Instagram: "Imagine you’re working with a dataset containing two classes, but the data isn’t linearly separable. In such cases, simple linear models like logistic regression or linear SVMs struggle to create an accurate decision boundary. This is where neural networks excel. By introducing nonlinearity through activation functions such as ReLU, sigmoid, or tanh, they transform the input space layer by layer, allowing the model to learn much more complex patter

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DAIRoBotica on Instagram: "Mitigating NaNs is crucial and they can come from the data, after feature engineering, custom activation functions, divide by zero operations, custom loss functions. #datascience #machinelearning #ai #artificalintelligence #neuralnetworks #transformers #convolutional neural network #modeltraining #deeplearning #mltips #recurrentneuralnetwork"

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