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Deep reinforcement learning and control fall 2018, cmu 10703 instructors: katerina fragkiadaki, Tom mitchell.
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Homework 4: deep reenforcement learning.
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Linear models are alkaline building blocks for many deep architectures, and stochastic optimisation is used to learn every worthy that we'll talk about in our naturally.
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Lecture 18: variational autoencoders & invertible models.
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What are the two tricks of deep learning?
4.2Barron’s two tricks 5Sampling from infinite width networks 6Benefits of depth 6.1Bounding oscillations in ReLU networks 6.2Step 2: constructing a deep function with many regular pieces 6.3Step 3: depth separation proof via region counting 6.4Other depth separations 7Approximating x^2 8Function space norms, and the Neural Tangent Kernel (NTK)
Homework 3: Natural Language Processing. Homework 4: Deep Reinforcement Learning. Lecture videos are provided via the course Piazza. See Syllabus for more information. Lecture 1: Introduction. Lecture 2: ML Basics 1. Lecture 3: ML Basics 2. Lecture 4: Optimization.
What are the lecture notes for deep learning?
Deep learning theory lecture notes Matus Telgarsky mjt@illinois.edu 2021-02-14 v0.0-1dabbd4b (pre-alpha) Preface Basic setup: feedforward networks and test error decomposition Highlights Missing topics and references Acknowledgements 1Approximation: preface 1.1Omitted topics 2Elementary constructive approximations
Last Update: Oct 2021
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