1:01:07Hadi Ghauch: Large-scale training for deep neural networks
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A 61-minute video from MLRG KTH: “Hadi Ghauch: Large-scale training for deep neural networks”.
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1:01:07
57:37Learning at the Wireless Edge by Vince Poor
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A 58-minute video from MLRG KTH: “Learning at the Wireless Edge by Vince Poor”.
40:04Lecture 1 part 2: Introduction
Intro
A 40-minute video from MLRG KTH: “Lecture 1 part 2: Introduction”.
1:00:09Lecture 2 part 1: Centralized Convex ML (part 1: deterministic algorithms)
A 60-minute video from MLRG KTH: “Lecture 2 part 1: Centralized Convex ML (part 1: deterministic algorithms)”.
33:21Lecture 2 part 2: Centralized Convex ML (part 1: deterministic algorithms)
A 33-minute video from MLRG KTH: “Lecture 2 part 2: Centralized Convex ML (part 1: deterministic algorithms)”.
41:56Lecture 3 part 1: Centralized Convex ML (part 2: stochastic algorithms)
A 42-minute video from MLRG KTH: “Lecture 3 part 1: Centralized Convex ML (part 2: stochastic algorithms)”.
47:32Lecture 3 part 2: Centralized Convex ML (part 2: stochastic algorithms)
A 48-minute video from MLRG KTH: “Lecture 3 part 2: Centralized Convex ML (part 2: stochastic algorithms)”.
46:35Lecture 4 part 1: Centralized Nonconvex ML (basic definitions and special structures)
A 47-minute video from MLRG KTH: “Lecture 4 part 1: Centralized Nonconvex ML (basic definitions and special structures)”.
45:54Lecture 4 part 2: Centralized Nonconvex ML (generic solvers)
A 46-minute video from MLRG KTH: “Lecture 4 part 2: Centralized Nonconvex ML (generic solvers)”.
44:07Lecture 5 part 1: Distributed ML (star communication topology)
A 44-minute video from MLRG KTH: “Lecture 5 part 1: Distributed ML (star communication topology)”.
43:00Lecture 5 part 2: Distributed ML (general communication topology)
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A 43-minute video from MLRG KTH: “Lecture 5 part 2: Distributed ML (general communication topology)”.
41:29Lecture 6 part 1: ADMM (basic definitions and properties)
A 41-minute video from MLRG KTH: “Lecture 6 part 1: ADMM (basic definitions and properties)”.
37:39Lecture 6 part 2: ADMM (hyperparameter optimization and applications)
A 38-minute video from MLRG KTH: “Lecture 6 part 2: ADMM (hyperparameter optimization and applications)”.
47:18Lecture 7 part 1: Communication efficiency (master-worker architecture)
A 47-minute video from MLRG KTH: “Lecture 7 part 1: Communication efficiency (master-worker architecture)”.
41:42Lecture 7 part 2: Communication efficiency (general graph)
A 42-minute video from MLRG KTH: “Lecture 7 part 2: Communication efficiency (general graph)”.
49:00Lecture 8 part 1: Deep Neural Networks
A 49-minute video from MLRG KTH: “Lecture 8 part 1: Deep Neural Networks”.
46:02Lecture 8 part 2: Deep Neural Networks
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A 46-minute video from MLRG KTH: “Lecture 8 part 2: Deep Neural Networks”.
1:38:44Machine Learning in the Air by Deniz Gunduz
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A 99-minute video from MLRG KTH: “Machine Learning in the Air by Deniz Gunduz”.
1:24:38Pascal Bianchi: A dynamical system viewpoint on stochastic approximation methods
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A 85-minute video from MLRG KTH: “Pascal Bianchi: A dynamical system viewpoint on stochastic approximation methods”.
48:55Seminar 10: Fundamentals of deep neural networks
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A 49-minute video from MLRG KTH: “Seminar 10: Fundamentals of deep neural networks”.
1:40:41Seminar 11: Stochastic gradient methods
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A 101-minute video from MLRG KTH: “Seminar 11: Stochastic gradient methods”.
46:53Seminar 12: Alternating direction method of multipliers for large scale optimization
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A 47-minute video from MLRG KTH: “Seminar 12: Alternating direction method of multipliers for large scale optimization”.
53:48Seminar 13: Regret analysis for sequential decision making and online , part 1learning
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A 54-minute video from MLRG KTH: “Seminar 13: Regret analysis for sequential decision making and online , part 1learning”.
1:39:52Seminar 14: Regret analysis for sequential decision making and online learning, part 2
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A 100-minute video from MLRG KTH: “Seminar 14: Regret analysis for sequential decision making and online learning, part 2”.
1:13:50Seminar 15: Regret analysis for sequential decision making (part 3)
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A 74-minute video from MLRG KTH: “Seminar 15: Regret analysis for sequential decision making (part 3)”.
55:03Seminar 1: Overview of ML techniques
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A 55-minute video from MLRG KTH: “Seminar 1: Overview of ML techniques”.
1:22:11Seminar 2: PAC learnability in finite and infinite hypothesis spaces
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A 82-minute video from MLRG KTH: “Seminar 2: PAC learnability in finite and infinite hypothesis spaces”.
1:06:09Seminar 3: On the approximation capabilities of neural networks
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A 66-minute video from MLRG KTH: “Seminar 3: On the approximation capabilities of neural networks”.
1:32:50Seminar 4: Learnability of fully-connected neural networks
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A 93-minute video from MLRG KTH: “Seminar 4: Learnability of fully-connected neural networks”.
58:10Seminar 5: Energy propagation in deep convolution neural networks
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A 58-minute video from MLRG KTH: “Seminar 5: Energy propagation in deep convolution neural networks”.
1:13:35Seminar 6: Deep scattering transforms
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A 74-minute video from MLRG KTH: “Seminar 6: Deep scattering transforms”.
56:27Seminar 7: Stochastic approximation
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A 56-minute video from MLRG KTH: “Seminar 7: Stochastic approximation”.
41:44Seminar 9: Coordinate descent optimization methods
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A 42-minute video from MLRG KTH: “Seminar 9: Coordinate descent optimization methods”.
1:09:39Sindri Magnússon: On the convergence limited communication gradient methods
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A 70-minute video from MLRG KTH: “Sindri Magnússon: On the convergence limited communication gradient methods”.
35:27The Merging between AI and Wireless Communication - Keynote at IEEE Globecom Workshop on WCDI, 2022
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A 35-minute video from MLRG KTH: “The Merging between AI and Wireless Communication - Keynote at IEEE Globecom Workshop on WCDI, 2022”.
3:40:31Wireless for Machine Learning - Tutorial
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A 221-minute video from MLRG KTH: “Wireless for Machine Learning - Tutorial”.