Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition
57:56Lecture 2 | Image Classification
59:31Lecture 3 | Loss Functions and Optimization
1:14:40Lecture 4 | Introduction to Neural Networks
1:13:59Lecture 5 | Convolutional Neural Networks
1:08:56Lecture 6 | Training Neural Networks I
1:20:19Lecture 7 | Training Neural Networks II
1:15:30Lecture 8 | Deep Learning Software
1:18:07Lecture 9: CNN Architectures (CS231n)
1:17:40Lecture 10: Recurrent Neural Networks (CS231n)
1:13:09Lecture 11: Detection and Segmentation (CS231n)
1:14:26Lecture 12: Visualizing and Understanding (CS231n)
1:15:48Lecture 13: Generative Models (CS231n)
1:17:41Lecture 14: Deep Reinforcement Learning (CS231n)
1:04:01Lecture 15: Efficient Methods and Hardware for Deep Learning (CS231n)
1:16:52Lecture 16: Adversarial Examples and Adversarial Training (CS231n)
1:21:46Lecture 7 | Training Neural Networks II
Stanford Vision & Learning Lab Faculty
This lecture delves into practical aspects of deep neural network training, covering advanced optimization methods such as AdaGrad, RMSProp, and Adam, along with regularization strategies including dropout and data augmentation. It concludes with an introduction to transfer learning and fine-tuning methodologies.
Lecture 7 continues our discussion of practical issues for training neural networks. We discuss different update rules commonly used to optimize neural networks during training, as well as different strategies for regularizing large neural networks including dropout. We also discuss transfer learning and finetuning.
Keywords: Optimization, momentum, Nesterov momentum, AdaGrad, RMSProp, Adam, second-order optimization, L-BFGS, ensembles, regularization, dropout, data augmentation, transfer learning, finetuning
Lecture 6 | Training Neural Networks I
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