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#Stochastic Gradient Descent

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Stanford University

Information Theory of Deep Learning - Naftali Tishby

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Explores the connection between deep learning and information theory, offering new insights into neural network design, generalization, and optimization through an information bottleneck framework.
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11 Lesons
1 hour 25 minutes
On-Demand
Free-Video
University of Central Florida

CAP5415 - Training Neural Networks Part 2 - Fall 2020 - Lecture 7

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Explore advanced neural network training techniques, including loss functions, backpropagation, gradient descent optimization, and strategies to address common challenges in deep learning.
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18 Lesons
1 hour 2 minutes
On-Demand
Free-Video
University of Central Florida

Training Neural Networks for Computer Vision - Part I - Lecture 10

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Explore neural network training techniques, including gradient descent, loss functions, and backpropagation, for effective computer vision model optimization and performance improvement.
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13 Lesons
36 minutes
On-Demand
Free-Video
Great Learning

Stochastic Gradient Descent

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Explore stochastic gradient descent, its advantages, and implementation. Learn about mini-batches, momentum, and convergence in this optimization algorithm for machine learning models.
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14 Lesons
1 hour 37 minutes
On-Demand
Free-Video
Great Learning

Multilayer Perceptron - Back Propagation in Neural Networks

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This tutorial on "Multi-Layer Perceptron" will help you to master all the core concepts of multi layer perceptrons and deep neural networks.
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7 Lesons
2 hours 8 minutes
On-Demand
Free-Video
Alexander Amini

MIT: Introduction to Deep Learning

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Foundations of deep learning: perceptrons, neural networks, loss functions, backpropagation, optimization techniques, and strategies to prevent overfitting in neural network training.
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32 Lesons
45 minutes
On-Demand
Free-Video
BIMSA

Gaussian Differential Privacy

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探索高斯差分隐私:一种新的隐私保护方法,提供更精确的组合分析和子采样隐私放大,并通过中心极限定理证明其在组合下的收敛性。
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1 Lesons
43 minutes
On-Demand
Free-Video
IEEE

Differentially Private Model Publishing for Deep Learning

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Explore techniques for publishing deep learning models with differential privacy, optimizing privacy loss and model accuracy through concentrated differential privacy and dynamic budget allocation.
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18 Lesons
23 minutes
On-Demand
Free-Video
Google TechTalks

Private Convex Optimization via Exponential Mechanism - Differential Privacy for Machine Learning

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Explore private convex optimization using the exponential mechanism. Learn differential privacy, noisy SGD, and isoperimetric inequality for ML applications.
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21 Lesons
53 minutes
On-Demand
Free-Video
Google TechTalks

Improving the Privacy-Utility Tradeoff in Differentially Private Machine Learning with Public Data

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Explore techniques to enhance privacy-utility balance in differentially private machine learning using public data, focusing on DP-RAFT and DOPE-SGD algorithms for improved model accuracy.
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1 Lesons
38 minutes
On-Demand
Free-Video
Google TechTalks

Differentially Private Diffusion Models

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Explore privacy-preserving machine learning with differentially private diffusion models, optimizing design for sensitive data and enhancing performance through public pre-training.
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1 Lesons
45 minutes
On-Demand
Free-Video
Hausdorff Center for Mathematics

Large Scale Machine Learning and Convex Optimization - Lecture 1

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Explore advanced stochastic optimization techniques for large-scale machine learning, including novel Newton-based algorithms and hybrid approaches that achieve improved convergence rates and efficiency.
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1 Lesons
1 hour 8 minutes
On-Demand
Free-Video
IPhT-TV

Statistical Physics of Stochastic Gradient Descent

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Explore statistical physics principles applied to stochastic gradient descent, uncovering insights into machine learning optimization techniques.
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1 Lesons
28 minutes
On-Demand
Free-Video
Centre for Networked Intelligence, IISc

Heterogeneity-Aware Algorithms for Federated Learning and Distributed Optimization

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Delve into advanced federated learning algorithms, focusing on heterogeneity challenges in distributed model training and innovative solutions for edge computing optimization.
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20 Lesons
1 hour 8 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning Fundamentals - Lecture 11

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Dive into advanced machine learning concepts and data science fundamentals through comprehensive theoretical discussions and practical applications at a graduate level.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
UofU Data Science

Stochastic Gradient Descent for Support Vector Machines - Lecture 22

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Dive into stochastic gradient descent algorithms and their application in optimizing Support Vector Machines through practical implementation techniques and mathematical foundations.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
Data Science Courses

Deep Learning Optimization: Stochastic Gradient Descent and Momentum - Lecture 3

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Dive into advanced optimization techniques for deep learning, covering stochastic gradient descent, mini-batches, momentum, and Stein's unbiased risk estimator.
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1 Lesons
1 hour 16 minutes
On-Demand
Free-Video
Centre for Networked Intelligence, IISc

Cost-Efficient Distributed Learning

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Discover strategies for optimizing distributed gradient descent algorithms by managing worker responses and implementing adaptive techniques to balance computational efficiency with model quality in machine learning systems.
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1 Lesons
1 hour 3 minutes
On-Demand
Free-Video
UofU Data Science

Stochastic Gradient Descent for Support Vector Machines - Lecture 22A

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Dive into stochastic sub-gradient descent optimization for Support Vector Machines and discover its connection to perceptron algorithms through practical implementation techniques.
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1 Lesons
1 hour 5 minutes
On-Demand
Free-Video
UofU Data Science

Stochastic Gradient Descent for Support Vector Machines - Lecture 21B

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Dive into optimization techniques for Support Vector Machines, focusing on stochastic gradient descent methods and their practical implementation in machine learning algorithms.
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1 Lesons
20 minutes
On-Demand
Free-Video
Simons Institute

Stochastic Variance Reduction Methods for Policy Evaluation

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Explore stochastic variance reduction methods for policy evaluation in reinforcement learning, covering MSPBE, SVRG, and SAGA algorithms with experimental results.
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21 Lesons
47 minutes
On-Demand
Free-Video
StatQuest with Josh Starmer

Gradient Descent, Step-by-Step

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Comprehensive step-by-step explanation of Gradient Descent, covering its application in Machine Learning, optimization techniques, and variations like Stochastic Gradient Descent.
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10 Lesons
24 minutes
On-Demand
Free-Video
Hausdorff Center for Mathematics

Large Scale Machine Learning and Convex Optimization - Lecture 3

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Explore advanced stochastic optimization techniques for large-scale machine learning, including novel Newton-based methods and combinations of batch and online algorithms for improved convergence rates.
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18 Lesons
1 hour 5 minutes
On-Demand
Free-Video
IEEE

SecureML - A System for Scalable Privacy-Preserving Machine Learning

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Explores scalable privacy-preserving machine learning techniques for linear regression, logistic regression, and neural networks using secure two-party computation, with efficient protocols and MPC-friendly alternatives to nonlinear functions.
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12 Lesons
19 minutes
On-Demand
Free-Video
USENIX Enigma Conference

Trustworthy Machine Learning: Challenges and Frameworks

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Exploring the security challenges in machine learning, this talk discusses strategies for building trustworthy AI systems by analyzing data influence, addressing vulnerabilities, and enhancing interpretability.
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15 Lesons
18 minutes
On-Demand
Free-Video
Alfredo Canziani

Optimisation

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Explore optimization techniques for neural networks, including gradient descent, SGD, momentum, adaptive methods, and normalization layers, with real-world applications in MRI technology.
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7 Lesons
1 hour 29 minutes
On-Demand
Free-Video
Alfredo Canziani

Stochastic Gradient Descent and Backpropagation

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Explore stochastic gradient descent, backpropagation, and neural network implementation in PyTorch. Learn practical tricks, basic modules, and gradient computation for effective deep learning.
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7 Lesons
1 hour 43 minutes
On-Demand
Free-Video
Association for Computing Machinery (ACM)

PyTorch - A Modern Library for Machine Learning with Adam Paszke

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Explore PyTorch's capabilities in machine learning, from research to production. Learn about its innovative solutions for efficient inference and its versatility across various ML scenarios.
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22 Lesons
1 hour 2 minutes
On-Demand
Free-Video
Simons Institute

Insights on Gradient-Based Algorithms in High-Dimensional Learning

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Explore gradient-based algorithms in high-dimensional learning, covering statistical physics solutions for performance analysis in nonconvex settings like spiked mixed matrix-tensor model, perceptron, and phase retrieval.
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29 Lesons
1 hour
On-Demand
Free-Video
Simons Institute

The Information Bottleneck Theory of Deep Neural Networks

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Explore the Information Bottleneck Theory's application to deep neural networks, examining statistical learning, information theory, and stochastic gradient descent in brain data analysis.
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14 Lesons
1 hour 38 minutes
On-Demand
Free-Video
Simons Institute

Implicit Regularization I

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Explore implicit regularization in deep learning, covering boosting, complexity control, optimization landscapes, and stochastic gradient descent for effective model training.
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13 Lesons
1 hour 17 minutes
On-Demand
Free-Video
Simons Institute

On the Foundations of Deep Learning - SGD, Overparametrization, and Generalization

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Explore foundations of deep learning, focusing on SGD, overparametrization, and generalization. Delve into optimization dynamics, margin theory, and the impact of network architecture on performance.
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27 Lesons
46 minutes
On-Demand
Free-Video
Simons Institute

Studying Generalization in Deep Learning via PAC-Bayes

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Explore PAC-Bayes theory's application to deep learning generalization, focusing on risk bounds, optimal priors, and SGD prediction for neural networks.
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15 Lesons
45 minutes
On-Demand
Free-Video
ChariotSolutions

Machine Learning: From ABCs to DEFs

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Explore machine learning fundamentals, from basic concepts to practical applications, focusing on people, data, and code. Learn to build simple AI models and understand key ML principles.
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14 Lesons
1 hour
On-Demand
Free-Video
Fields Institute

SGD in the Large - Average-Case Analysis, Asymptotics, and Stepsize Criticality

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Explore average-case analysis, asymptotics, and stepsize criticality in stochastic gradient descent, with insights on optimization problems and stochastic momentum techniques.
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17 Lesons
1 hour 10 minutes
On-Demand
Free-Video
International Centre for Theoretical Sciences

Stochastic Gradient Descent and Machine Learning - Lecture 1

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Explore optimization methods, gradient descent, Newton's method, and convexity in machine learning. Gain insights into iterative algorithms and their applications in statistical physics and data analysis.
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20 Lesons
1 hour 53 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Implicit and Explicit Regularization in Deep Neural Networks

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Explores implicit and explicit regularization in deep neural networks, connecting learning algorithms to H-infinity control and explaining convergence behavior in over-parametrized models, offering insights into generalization abilities.
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20 Lesons
37 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Gradient Descent, Stochastic Gradient Descent, and Acceleration - Part 2

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Explore advanced gradient-based optimization methods for large-scale machine learning, including stochastic gradient descent and accelerated versions, with convergence analysis and ODE connections.
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1 Lesons
1 hour 2 minutes
On-Demand
Free-Video
Paul Hand

Gradient Descent and Stochastic Gradient Descent

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Explore gradient descent and stochastic gradient descent in deep neural networks, covering convergence rates, learning rate effects, and challenges in optimization algorithms.
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10 Lesons
57 minutes
On-Demand
Free-Video
MITCBMM

Fit Without Fear - An Over-Fitting Perspective on Modern Deep and Shallow Learning

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Explore over-fitting in modern machine learning, challenging conventional wisdom and presenting kernel methods as a competitive alternative to deep learning. Insights on interpolation, generalization, and stochastic gradient descent.
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31 Lesons
55 minutes
On-Demand
Free-Video
MITCBMM

Introduction to Optimization

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Learn optimization techniques for machine learning, including likelihood estimation, gradient descent, and regularization, with practical examples and applications.
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21 Lesons
1 hour 12 minutes
On-Demand
Free-Video
TheIACR

Federated Learning with Formal User-Level Differential Privacy Guarantees

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Explore federated learning with differential privacy guarantees, covering key concepts, challenges, and algorithms like DP-SGD and DP-FTRL, with insights on privacy-utility trade-offs and future directions.
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20 Lesons
59 minutes
On-Demand
Free-Video
Paul G. Allen School

Better Understanding of Non-convex Methods in Machine Learning

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Exploring non-convex optimization in machine learning, focusing on matrix completion and neural network architectures. Insights into algorithmic approaches for complex, high-dimensional models.
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1 Lesons
58 minutes
On-Demand
Free-Video

Neural Networks For Your Dog

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Learn to build neural networks from scratch in Python, covering perceptrons, gradient descent, and deep learning concepts through simple, engaging explanations and hands-on coding.
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15 Lesons
2 hours 14 minutes
On-Demand
Free-Video
NDC Conferences

What You Always Wanted to Know About Deep Learning, but Were Afraid to Ask

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Demystifying deep learning concepts like back-propagation, gradient descent, and neural networks. Practical guide to training a model for object recognition using TensorFlow and Keras.
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26 Lesons
58 minutes
On-Demand
Free-Video
ACCU Conference

Turtles! Hill Climbing! Hammers! Paper Bags!

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Explore optimization techniques through turtle graphics, from hill climbing to simulated annealing, laying the groundwork for understanding neural networks and machine learning concepts.
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25 Lesons
1 hour 6 minutes
On-Demand
Free-Video
EuroPython Conference

A Gentle Introduction to Data Science

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Explore data science fundamentals, from AI's influence to practical machine learning in Python. Covers key concepts, problem-solving applications, and essential Python tools for aspiring data scientists.
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26 Lesons
32 minutes
On-Demand
Free-Video
Center for Language & Speech Processing(CLSP), JHU

Where Does Negative Transfer Come From? On the Implicit Bias of SGD in Multi-Task Learning

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Explore negative transfer in multi-task learning, examining task conflict, optimization temperature, and SGD's implicit bias. Gain insights into generalization challenges in neural networks.
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1 Lesons
29 minutes
On-Demand
Free-Video
Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

The Long-Run Distribution of Stochastic Gradient Descent: A Large Deviations Approach

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Explore the long-run behavior of stochastic gradient descent in non-convex problems, analyzing state space visitation patterns and energy level distributions.
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1 Lesons
25 minutes
On-Demand
Free-Video
Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Stochastic Algorithms for Constrained Continuous Optimization

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Explore stochastic-gradient-based algorithms for constrained continuous optimization, focusing on interior-point and sequential-quadratic-programming methods with convergence guarantees and practical applications.
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1 Lesons
29 minutes
On-Demand
Free-Video
Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Hessian-Aware Stochastic Differential Equation Modeling of SGD

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Explore a novel SDE model for SGD that incorporates Hessian information, improving accuracy in capturing escaping behaviors and achieving exact recovery for quadratic objectives.
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1 Lesons
29 minutes
On-Demand
Free-Video
Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

The First Optimal Distributed SGD in the Presence of Data, Compute and Communication Heterogeneity

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Explore optimal time complexities for parallel optimization methods with heterogeneous data, compute, and communication. Gain insights into efficient distributed SGD algorithms.
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1 Lesons
29 minutes
On-Demand
Free-Video
NCCR SwissMAP

Phase Diagram of Stochastic Gradient Descent in High-Dimensional Two-Layer Neural Networks

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Explore the phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks, focusing on its behavior and implications.
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1 Lesons
18 minutes
On-Demand
Free-Video
ACM SIGPLAN

Haskell for Choice-Based Learning in Machine Learning Programming

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Explore language design for machine learning, focusing on choice-based learning. Combine algebraic effects, handlers, and selection monad to create powerful frameworks for decision-making models and optimization techniques.
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1 Lesons
49 minutes
On-Demand
Free-Video
NashKnolX

Methods of Optimization in Machine Learning

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Explore optimization techniques for machine learning models, including gradient descent and stochastic methods. Learn to adjust hyperparameters and minimize cost functions effectively.
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13 Lesons
31 minutes
On-Demand
Free-Video
Centre International de Rencontres Mathématiques

Non-convex SGD and Lojasiewicz-type Conditions for Deep Learning

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Explore non-convex SGD and Lojasiewicz-type conditions for deep learning in this mathematical conference talk by Kevin Scaman at CIRM.
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1 Lesons
47 minutes
On-Demand
Free-Video
GOTO Conferences

Functional Programming and Scala for Machine Learning - YOW! 2015

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Explore how functional programming and Scala can revolutionize machine learning, offering strongly-typed solutions for efficient algorithm implementation and scalable data processing.
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1 Lesons
34 minutes
On-Demand
Free-Video
MITCBMM

SGD and Weight Decay Secretly Compress Your Neural Network

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Explore how SGD and weight decay techniques compress neural networks, enhancing efficiency and performance in deep learning models.
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1 Lesons
55 minutes
On-Demand
Free-Video
VinAI

Stochastic Gradient Descent Methods with Biased Estimators

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Explore recent advancements in stochastic gradient descent methods, focusing on biased estimators for large-scale optimization and minimax problems in machine learning applications.
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1 Lesons
1 hour 7 minutes
On-Demand
Free-Video
UofU Data Science

Least Mean Square Regression for Machine Learning - Lecture 11

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Dive into least mean square regression, exploring loss function minimization and gradient descent methods for effective linear regression implementation and optimization.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
UofU Data Science

Linear Regression and Least Mean Square Method - Lecture 11

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Master linear regression fundamentals through least mean square method, loss function minimization, and gradient descent techniques for practical machine learning applications.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
QuICS

Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walks

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Explore quantum speedups for nonconvex optimization using quantum tunneling walks. Learn how this approach outperforms classical algorithms in specific landscapes with high, thin barriers between minima.
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1 Lesons
1 hour 1 minute
On-Demand
Free-Video
Institut des Hautes Etudes Scientifiques (IHES)

Random Matrices and Dynamics of Optimization in Very High Dimensions - Lecture 3

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Explorez les dynamiques d'optimisation en très haute dimension et leur application en apprentissage automatique, en mettant l'accent sur les matrices aléatoires et les statistiques résumées.
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1 Lesons
1 hour 52 minutes
On-Demand
Free-Video
Institut des Hautes Etudes Scientifiques (IHES)

Random Matrices and Dynamics of Optimization in Very High Dimensions - Lecture 4

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Explore random matrices and optimization dynamics in high dimensions, focusing on effective dynamics, spectral transitions, and applications in machine learning algorithms.
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1 Lesons
1 hour 34 minutes
On-Demand
Free-Video
MITCBMM

Towards Understanding the Implicit Regularization Effect of SGD

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Explore the implicit regularization effect of Stochastic Gradient Descent, uncovering its impact on optimization and generalization in machine learning models.
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1 Lesons
1 hour 14 minutes
On-Demand
Free-Video
Institut des Hautes Etudes Scientifiques (IHES)

Random Matrices and Dynamics of Optimization in Very High Dimensions - Lecture 2

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Explorez les dynamiques d'optimisation en très haute dimension et leur application en apprentissage automatique, en mettant l'accent sur les matrices aléatoires et les statistiques résumées.
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1 Lesons
2 hours 5 minutes
On-Demand
Free-Video
Institut des Hautes Etudes Scientifiques (IHES)

Random Matrices and Dynamics of Optimization in Very High Dimensions - Lecture 1

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Explorez les algorithmes d'optimisation en haute dimension pour l'apprentissage automatique, en mettant l'accent sur la descente de gradient stochastique et les transitions spectrales des matrices aléatoires.
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1 Lesons
2 hours 3 minutes
On-Demand
Free-Video
USENIX

Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD

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Explore how DP-SGD provides stronger privacy guarantees for similar datapoints in deep learning, revealing why common benchmarks leak less information than previously estimated.
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1 Lesons
11 minutes
On-Demand
Free-Video