kmario23/deep-learning-drizzle

★ 12,807HTMLAudience · researcherComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((DL Drizzle))
    Topics
      Deep learning
      Reinforcement learning
      Computer vision
      NLP
    Sources
      Stanford
      MIT
      Univ of Toronto
      Summer schools
    Format
      Video lectures
      Lecture slides
      Course websites
    Audience
      Self-directed learners
      Researchers

mindmap root((DL Drizzle)) Topics Deep learning Reinforcement learning Computer vision NLP Sources Stanford MIT Univ of Toronto Summer schools Format Video lectures Lecture slides Course websites Audience Self-directed learners Researchers

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Things people build with this

USE CASE 1

Find free university lecture series on a specific AI topic, reinforcement learning, NLP, computer vision, and follow along at your own pace.

USE CASE 2

Build a self-study curriculum for deep learning by combining courses from Stanford, MIT, and other top research universities.

USE CASE 3

Discover video lectures from prominent AI researchers on specialized topics like Bayesian deep learning or graph neural networks.

USE CASE 4

Bookmark a reference list of summer schools and boot camps run by major AI research institutions.

Tech stack

HTML

Getting it running

Difficulty · easy Time to first run · 5min

No installation needed, browse the repository directly or open linked course pages in a browser.

In plain English

Deep Learning Drizzle is a curated list of free lecture series and course materials covering artificial intelligence and machine learning topics. The repository collects links to video lectures, course websites, and lecture slides from universities around the world, organized by subject area. The topics covered span a broad range of the field. There are sections dedicated to core deep learning and neural networks, general machine learning fundamentals, reinforcement learning (where software learns by trial and reward), natural language processing (getting computers to understand and generate text), computer vision (image recognition and analysis), speech recognition, probabilistic graphical models, Bayesian deep learning, and graph neural networks. The collection also includes a section of intensive boot camps and summer school programs run by research institutions. Each entry in the tables typically shows the course name, the university or instructor who taught it, a link to the course webpage where slides are available, a link to the lecture videos (often hosted on YouTube), and the year the course was offered. Many entries are from well-known academic programs at schools such as Stanford, MIT, and the University of Toronto, and from researchers who are prominent names in the field. This is a reference list, not software you install or run. Its value is as a starting point for self-directed learners who want to study these subjects using freely available university-level material. No coding or technical background is required to browse the list, though the courses themselves are designed for people who want to go deep into the math and engineering behind modern AI systems. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

I want to learn reinforcement learning from scratch using free university lectures. Based on the Deep Learning Drizzle list, suggest a 3-month study plan with courses in order, including what math prerequisites I need.

Prompt 2

I'm a developer who knows Python but has never studied machine learning formally. Using the Deep Learning Drizzle collection, what 2-3 courses should I start with and why?

Prompt 3

I want to specialize in natural language processing. From the Deep Learning Drizzle list, which courses and lecture series should I prioritize, and what order should I watch them in?

Prompt 4

Summarize what topics Deep Learning Drizzle covers and which institutions contribute courses, so I can decide if it has the advanced material I need for graph neural network research.

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