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Learning resources

by @stats385 · last added 2026-09-24 · 12 items [learning]

Tutorials, courses, and concept primers — SGD, backprop, initialization, and companion courses.

─── Untagged (12) ──────────────────────────────────────

* MIT 6.S191 Intro to Deep Learning
Short modern intro lecture series — good warm-up week material.
youtube.com · node›
* Andrew Ng Deep Learning Specialization
Widely used MOOC primer before diving into the research papers.
coursera.org · node›
* fast.ai practical deep learning
Top-down practical course — useful complement to STATS 385’s theory focus.
fast.ai · node›
* Course definitions page
On-site glossary of deep learning motifs — pair with this list for vocabulary.
stats385.github.io · node›
* Deep Visualization Toolbox (video)
Lecture 1 recommended video — interactive CNN feature visualization.
youtube.com · node›
* Delving Deep into Rectifiers (He initialization)
He et al. — principled weight init for ReLU nets; course learning.md points here.
arxiv.org · node›
* Gradient descent
Basic optimizer primer — steps along the negative gradient of the loss.
en.wikipedia.org · node›
* Backpropagation
Chain-rule engine for computing all parameter gradients efficiently.
en.wikipedia.org · node›
* Stochastic gradient descent
Course cheat-sheet concept: SGD approximates full gradients with random minibatches.
en.wikipedia.org · node›
* Deep Learning Book — MLPs chapter
Core textbook chapter on multilayer perceptrons; assign early.
deeplearningbook.org · node›
* Stanford CS231n — CNNs for Visual Recognition
Sibling Stanford course; outstanding notes if you need vision/architecture grounding.
cs231n.stanford.edu · node›
* Dive into Deep Learning (interactive book)
Primary hands-on tutorial listed by the course — code + math together.
d2l.ai · node›
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