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Artificial Intelligence Foundations: Neural Networks Online Class | LinkedIn Learning, formerly Lynda.com

Artificial Intelligence Foundations: Neural Networks Online Class | LinkedIn Learning, formerly Lynda.com

Learn the fundamental techniques and principles behind artificial neural networks.

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A neural network learns by continuously adjusting its parameters - the weights and biases - to improve its prediction accuracy. These parameters are tweaked upward or downward based on how far the model’s predictions deviate from the actual results. The difference between the predicted output and the true value is measured using a loss function, which quantifies the error. To minimize this error, the model uses backpropagation, an algorithm that calculates the gradients (partial derivatives of t

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A neural network learns by continuously adjusting its parameters - the weights

Bigger than all my problems | Zukaa Broadcast Network

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Deep Learning: Recurrent Neural Networks in Python

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theconversation.com

56 reactions · 17 shares | When a neural network is too large to pretrain more than once, tuning its hyperparameters is practically impossible. Today, we announce μTransfer—a new technique that can tune the 6.7 billion parameter GPT-3 model using only 7% of the pretraining compute: https://msft.it/6003wwxr7 | Microsoft Research | Facebook

56 reactions · 17 shares | When a neural network is too large to pret…

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