Lecture 1 - Deep Learning Foundations: review of basic DL models and optimization solvers

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Course overview, ERM, basic DL models (Multi-Layer Perceptron and Convolutional Neural Networks), gradient descent, stochastic gradient descent, Momentum

Soheil Feizi

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I think these might be the best deep learning lectures on the internet

KevinBacheM

18:45 CMSC 828W: Foundations of deep learning
21:08 Agenda
21:58 Supervised learning (ERM)
28:57 Choices of the loss function(0-1, hinge, cross-entropy)
43:38 ERM (linear/affine, nonlinear)
53:05 CNNs (Conv, pooling, AlexNet)
1:00:38 ERM (Gradient descent, Stochastic GD)
1:06:12 Non-Convex optimization problem
1:10:10 Q&A

shiv

Hi, could u please introduce a good course in the basics of machine learning and deep learning for beginners?

parisaemkani

deep thanks for sharing this on youtube!!!

vacous

42:30 one option could be KL divergence loss?

bryanbocao

I was just wondering what does Empirical in ERM means?

benurajsharma

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