Questions and Answers
What is the purpose of setting the parameters W and b in a linear classifier?
To match the ground truth labels
In computer vision, what does the Loss Function indicate about the classifier?
How good the classifier is at modeling relationships
What is a reason to be cautious about overfitting in deep generative models?
Overfitting can compromise the model's generalization ability
What role does the Loss Function play in deep sequence modeling?
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How does a linear classifier handle the computed scores to make predictions?
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What is the impact of having a smaller loss value in deep generative models?
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What is one of the main goals of a linear classifier when setting parameters W and b?
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In deep sequence modeling, how does the loss function contribute to model performance?
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What is one of the dangers of high loss values in deep generative models?
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How does a linear classifier ensure that computed scores are aligned with ground truth labels?
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Study Notes
Deep Learning Course Overview
- The course is based on Stanford's CS231n: Convolutional Neural Networks for Visual Recognition
- The course covers foundation concepts, shallow artificial neural networks, training parameters, deep computer vision, convolutional neural networks, deep sequence modeling, object detection, deep generative models, deep reinforcement, recurrent neural networks, VAE, pre-trained models, LSTM, GAN, transfer learning, and transformers
Foundations of Deep Learning
- Four steps to train a model:
- Step 1: Start with a random W and b
- Step 2: Calculate the score function (hypotheses function)
- Step 3: Calculate the loss function (error)
- Step 4: Optimization step (find the set of parameters W that minimize the loss function)
Logistic Regression
- Score function: takes input feature vectors, applies some function f, and returns predicted class labels
- Loss function: measures the difference between predicted and actual labels
- Multiclass SVM loss: L_i = ∑ max(0, s_j - s_yi + 1) for j ≠ yi
- Multiclass SVM loss example: calculate the loss for three training examples and three classes
Linear Classifier
- Score function: f(x, W) = Wx + b
- Goal: set parameters W and b to match the ground truth labels across the whole training set
- Correct class should have a score higher than the scores of incorrect classes
Loss Function
- Measures how good the current classifier is
- Smaller loss indicates a better classifier
- Larger loss indicates more work needed to increase classification accuracy
- Loss function also known as error function
