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Stanford CS231n: Convolutional Neural Networks for Visual Recognition →0/16
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Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition

57:56
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Lecture 2 | Image Classification

59:31
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Lecture 3 | Loss Functions and Optimization

1:14:40
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Lecture 4 | Introduction to Neural Networks

1:13:59
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Lecture 5 | Convolutional Neural Networks

1:08:56
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Lecture 6 | Training Neural Networks I

1:20:19
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Lecture 7 | Training Neural Networks II

1:15:30
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Lecture 8 | Deep Learning Software

1:18:07
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Lecture 9: CNN Architectures (CS231n)

1:17:40
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Lecture 10: Recurrent Neural Networks (CS231n)

1:13:09
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Lecture 11: Detection and Segmentation (CS231n)

1:14:26
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Lecture 12: Visualizing and Understanding (CS231n)

1:15:48
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Lecture 13: Generative Models (CS231n)

1:17:41
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Lecture 14: Deep Reinforcement Learning (CS231n)

1:04:01
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Lecture 15: Efficient Methods and Hardware for Deep Learning (CS231n)

1:16:52
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Lecture 16: Adversarial Examples and Adversarial Training (CS231n)

1:21:46

Lecture 3 | Loss Functions and Optimization

Fei-Fei Li, Justin Johnson & Serena YeungStanford University

Stanford Vision & Learning Lab Faculty

1:14:40·2017·graduate level

This lecture introduces the formalization of loss functions, specifically multiclass SVM loss and multinomial logistic regression loss, for quantifying model error in image classification. It details regularization techniques like weight decay to combat overfitting and introduces stochastic gradient descent as a primary optimization algorithm.

AI & MLDeep LearningAlgorithms & Data StructuresStatistical Learning & CausalityMachine Learning

Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model’s predictions, and discuss two commonly used loss functions for image classification: the multiclass SVM loss and the multinomial logistic regression loss. We introduce the idea of regularization as a mechanism to fight overfitting, with weight decay as a concrete example. We introduce the idea of optimization and the stochastic gradient descent algorithm. We also briefly discuss the use of feature representations in computer vision.

Keywords: Image classification, linear classifiers, SVM loss, regularization, multinomial logistic regression, optimization, stochastic gradient descent

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Lecture 2 | Image Classification

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Lecture 4 | Introduction to Neural Networks

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