EulerFoldmachine learning
Deep Dive into Adam Optimization: From Theory to Production
“Master Adam optimization with a hands‑on, four‑week curriculum covering derivations, variants, and production deployment.”
W1
Foundations of Gradient‑Based Optimization
By the end of this module you will be able to implement and compare basic gradient descent variants on a synthetic dataset.
5 videos•131m
3 readings
5 topics
1 homework
W2
Adam Algorithm Derivation and Intuition
By the end of this module you will be able to derive Adam updates and implement it from scratch.
4 videos•99m
3 readings
5 topics
1 homework
W3
Advanced Adam Variants and Hyperparameter Tuning
By the end of this module you will be able to evaluate and select Adam variants for a given deep learning task.
4 videos•80m
3 readings
5 topics
1 homework
W4
Practical Deployment and Debugging of Adam in Production
By the end of this module you will be able to integrate Adam optimization into a scalable deep learning pipeline.
4 videos•108m
3 readings
5 topics
1 homework
References
Week 1: Foundations of Gradient‑Based Optimization
01
Stochastic Gradient Descent Ryan Tibshirani Convex Optimization 10-725
stat.cmu.edu02
Stochastic gradient descent - Cornell University Computational Optimization Open Textbook - Optimization Wiki
optimization.cbe.cornell.edu03
Stochastic Gradient Descent: The Workhorse of Machine Learning
cs.cornell.eduWeek 2: Adam Algorithm Derivation and Intuition
Week 3: Advanced Adam Variants and Hyperparameter Tuning
01
Comparative Analysis of Adam Optimizer and Its Variants: Adamax, RMSProp, and Adagrad — Addressing Vanishing and Exploding Gradient Problems | by Astitwa | Medium
medium.com02
Improving the Adam optimizer via projection-based gradient correction in deep learning - ScienceDirect
sciencedirect.com03
Published as a conference paper at ICLR 2018
openreview.netWeek 4: Practical Deployment and Debugging of Adam in Production
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