Questions and Answers
ICS 471-Deep Learning slide 2 Where we are now.
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ICS 471-Deep Learning slide 3 Where we are now.
Slide 3
ICS 471-Deep Learning slide 4 Where we are now.
Slide 4
ICS 471-Deep Learning slide 5 Where we are now.
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Mini-batch SGD Loop: 1. Sample a batch of data 2. Forward prop it through the graph (network), get loss 3. Backprop to calculate the gradients 4. Update the parameters using the gradient ICS 471-Deep Learning slide 7 Next: Training Neural Networks
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ICS 471-Deep Learning slide 6 Where we are now.
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ICS 471-Deep Learning slide 8 Overview 1. One time setup preprocessing, activation functions, weight initialization, regularization, gradient checking 2. Training dynamics ______ the learning process.
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ICS 471-Deep Learning slide 6 Where we are now.
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ICS 471-Deep Learning slide 8 Overview 1. One time setup preprocessing, ______, weight initialization, regularization, gradient checking 2. Training dynamics babysitting the learning process.
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ICS 471-Deep Learning slide 8 Overview 1. One time setup preprocessing, activation functions, weight initialization, ______, gradient checking 2. Training dynamics babysitting the learning process.
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Study Notes
Deep Learning Process Overview
- The process of deep learning involves a mini-batch SGD loop, which consists of:
- Sampling a batch of data
- Forward propagating the data through the network to get the loss
- Backpropagating to calculate the gradients
- Updating the parameters using the gradients
Training Neural Networks
- One-time setup for training involves:
- Preprocessing
- Activation functions
- Weight initialization
- Regularization
- Gradient checking
- Training dynamics involves:
- Babysitting the learning process
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