To learn more: Gradient descent: https://
youtube.com/watch?v=qg4Pch
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…
Back prop and image credit: https://
youtube.com/watch?v=An5z8l
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… Original post credit: @bindureddy 10/10
@abacusai
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Gradient Descent and Backpropagation Learning Resources
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Low Test Error Indicates Successful Model Learning and Generalization
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If your error on the test set is low, you have learned the weights and biases of a model that predict the output given specific inputs and you can now use it to predict unseen data. 9/10
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Neural Network Training: Iteration and Data Requirements
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6. Iteration: We repeat steps 2 to 6 for all the data points in your training set multiple times (epochs). Hence, your neural network is likely to be a better fit, if you have more training data points. 7/10
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Evaluation: Testing Neural Networks on Unseen Data Points
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7. Evaluation: You should always keep aside some data points from your original training set for testing. Here we evaluate how the NN predicts data points it's never been trained on. 8/10
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Gradient Descent: Adjusting Weights and Biases to Reduce Loss
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5: Gradient Descent: We now adjust the weights and biases based on the gradients calculated in the last step. Typically this is done by multiplying the gradient by a small factor called learning rate. The basic idea is to reduce the error or loss. 6/10
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Backpropagation: Computing Gradients Using the Chain Rule
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You move backwards from the last layer using the chain rule of calculus and compute "gradients". Basically, you are calculating the gradient of the loss function with respect to each weight or bias 5/10
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Calculating Loss: Measuring Prediction Error in Neural Networks
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3. Calculate Loss: At first the prediction is a random guess. The error between the prediction and reality is called loss. If the network predicts there is no cat in an image of a cat, loss is high and vice-versa. 3/10
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Backpropagation: Calculating Parameter Contribution to Network Loss
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4. Backpropagation: This is where the magic happens. Fundamentally, you are calculating how much each parameter (weight or bias of each node) in the network contributed to the loss or error from step 2. 4/10
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Neural Network Initialization and Feedforward Pass Basics
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1. Initialize the neural network: Create a neural network and initialize it with random weights and biases. 2. Feedforward pass: NN predicts an outcome based on input. For example: does the input image have a cat? 2/10
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Backpropagation and Gradient Descent: Deep Learning Mathematics Explained
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Backpropagation and Gradient Descent – The dynamic duo of deep learning Ever wondered about the mathematics behind NN training? Here is a simplified way to understand the process: 1/10