Stochastic Gradient Descent Courses and Certifications

Alexander Amini;  MIT

MIT: Introduction to Deep Learning

Foundations of deep learning: perceptrons, neural networks, loss functions, backpropagation, optimization techniques, and strategies to prevent overfitting in neural network training.

• YouTube
• 45 minutes
• On-Demand
• Free Video

Society for Industrial and Applied Mathematics

Bayesian Inference with Plug-and-Play Priors - Imaging and Inverse Problems Seminar

Explore Bayesian imaging with Plug & Play methods, focusing on convergence guarantees and well-posed models. Learn about novel algorithms for Monte Carlo sampling and MAP inference in inverse problems.

• YouTube
• 53 minutes
• On-Demand
• Free Video

IEEE

SecureML - A System for Scalable Privacy-Preserving Machine Learning

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.

• YouTube
• 19 minutes
• On-Demand
• Free Video

IEEE

Differentially Private Model Publishing for Deep Learning

Explore techniques for publishing deep learning models with differential privacy, optimizing privacy loss and model accuracy through concentrated differential privacy and dynamic budget allocation.

• YouTube
• 23 minutes
• On-Demand
• Free Video

Alfredo Canziani

Optimisation

Explore optimization techniques for neural networks, including gradient descent, SGD, momentum, adaptive methods, and normalization layers, with real-world applications in MRI technology.

• YouTube
• 1 hour 29 minutes
• On-Demand
• Free Video

Alfredo Canziani

Stochastic Gradient Descent and Backpropagation

Explore stochastic gradient descent, backpropagation, and neural network implementation in PyTorch. Learn practical tricks, basic modules, and gradient computation for effective deep learning.

• YouTube
• 1 hour 43 minutes
• On-Demand
• Free Video

MITCBMM

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

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.

• YouTube
• 55 minutes
• On-Demand
• Free Video

MITCBMM

Introduction to Optimization

Learn optimization techniques for machine learning, including likelihood estimation, gradient descent, and regularization, with practical examples and applications.

• YouTube
• 1 hour 12 minutes
• On-Demand
• Free Video

NDC Conferences

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

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.

• YouTube
• 58 minutes
• On-Demand
• Conference Talk

Association for Computing Machinery (ACM)

PyTorch - A Modern Library for Machine Learning with Adam Paszke

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.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

Paul Hand

Gradient Descent and Stochastic Gradient Descent

Explore gradient descent and stochastic gradient descent in deep neural networks, covering convergence rates, learning rate effects, and challenges in optimization algorithms.

• YouTube
• 57 minutes
• On-Demand
• Free Video

StatQuest with Josh Starmer

Gradient Descent, Step-by-Step

Comprehensive step-by-step explanation of Gradient Descent, covering its application in Machine Learning, optimization techniques, and variations like Stochastic Gradient Descent.

• YouTube
• 24 minutes
• On-Demand
• Free Video

ChariotSolutions

Machine Learning: From ABCs to DEFs

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.

• YouTube
• 1 hour
• On-Demand
• Free Video

Simons Institute

The Information Bottleneck Theory of Deep Neural Networks

Explore the Information Bottleneck Theory's application to deep neural networks, examining statistical learning, information theory, and stochastic gradient descent in brain data analysis.

• YouTube
• 1 hour 38 minutes
• On-Demand
• Free Video

Simons Institute

Stochastic Variance Reduction Methods for Policy Evaluation

Explore stochastic variance reduction methods for policy evaluation in reinforcement learning, covering MSPBE, SVRG, and SAGA algorithms with experimental results.

• YouTube
• 47 minutes
• On-Demand
• Free Video