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Agentic AIStanford University
AI LuminariesStanford University
Taught byStanford UniversityTier 1: world-leading universities and India's top institutesStanford UniversityTier 1: world-leading universities and India's top institutes
Institution score97/10097/100
UniversityYesYes
PlatformYouTubeYouTube
TypeVideo playlistVideo playlist
PriceFree videoFree video
CertificateNo certificateNo certificate
RatingNo ratingsNo ratings
Learners or views687,4384,852,594Best here
Time6 videos, 4 hoursBest here20 videos, 15 hours
PaceSelf-pacedSelf-paced
LevelNot statedNot stated
LanguageEnglishEnglish
SubtitlesNot statedNot stated
StartsAny timeAny time
TeachersNot statedNot stated
Syllabus6 videos20 videosBest here
Skills only this one coversNoneNone
SubjectsArtificial Intelligence, Generative AI, AI AgentsArtificial Intelligence
Before you startNot statedNot stated
Advantages
  • Free and complete: every lecture is in the playlist, in order.
  • Watch at 1.5x, skip what you know, rewatch what you don't.
  • The actual Stanford lectures (CS229, CS224N, CS231N…): the deepest free material on AI anywhere.
  • Taught by Stanford University, one of the strongest names in its field.
  • 687,438 views, so help and notes are easy to find.
  • Completely free.
  • Self-paced: start any time.
  • A clear syllabus (6 videos) you can see before you start.
  • Free and complete: every lecture is in the playlist, in order.
  • Watch at 1.5x, skip what you know, rewatch what you don't.
  • The actual Stanford lectures (CS229, CS224N, CS231N…): the deepest free material on AI anywhere.
  • Taught by Stanford University, one of the strongest names in its field.
  • 4,852,594 views, so help and notes are easy to find.
  • Completely free.
  • Self-paced: start any time.
  • A clear syllabus (20 videos) you can see before you start.
Disadvantages
  • No certificate, deadlines or graded work.
  • Quality and depth vary: check that the playlist is finished before you start.
  • Graduate level: needs linear algebra, probability and programming first.
  • No certificate.
  • No graded assignments or feedback.
  • No certificate, deadlines or graded work.
  • Quality and depth vary: check that the playlist is finished before you start.
  • Graduate level: needs linear algebra, probability and programming first.
  • No certificate.
  • No graded assignments or feedback.
Technifyed score91/10093/100Best here
Syllabus side by side
Agentic AI
  1. Agentic AI vs LLMs: Why an Agent Doesn't Stop at the Answer
  2. Why AI Agents Matter Now with Stanford's Azalia Mirhoseini and Aakanksha Chowdhery
  3. Stanford CS224N: NLP w/ DL | Spring 2024 | Lecture 14 - Reasoning and Agents by Shikhar Murty
  4. Stanford Webinar - Agentic AI: A Progression of Language Model Usage
  5. Stanford CS25: V3 I Beyond LLMs: Agents, Emergent Abilities, Intermediate-Guided Reasoning, BabyLM
  6. Stanford CS25: V3 I Generalist Agents in Open-Ended Worlds
AI Luminaries
  1. Stanford Lecture - Strong Components and Weak Components, Dr. Donald Knuth I 2024
  2. Stanford Webinar - How AI is Changing Coding and Education, Andrew Ng & Mehran Sahami
  3. Ask About AI: Professor Chris Manning Answers AI-Generated Questions
  4. Ask About AI: Professor Chris Manning Answers Your AI Career Questions
  5. Andrew Ng: Opportunities in AI - 2023
  6. Andrew Ng and Chris Manning Discuss Natural Language Processing
  7. AI and Robotics - Chelsea Finn & Andrew Ng
  8. What is our ethical responsibility with AI & Machine Learning? - Fei-Fei Li & Andrew Ng
  9. Andrew Ng and Fei-Fei Li Discuss Human-Centered Artificial Intelligence - Stanford Online
  10. What advice do you have for getting started in AI & Machine Learning? - Fei-Fei Li & Andrew Ng
  11. Is a Career in AI and Machine Learning Right for Me? - Fei-Fei Li & Andrew Ng
  12. Stanford CS25: V2 I Represent part-whole hierarchies in a neural network, Geoff Hinton
  13. Stanford CS229: Machine Learning Lecture 1 - Andrew Ng (Autumn 2018)
  14. Stanford Lecture: Don Knuth - "Pi and The Art of Computer Programming" (2019)
  15. Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors
  16. Stanford Lecture: TeX For Beginners - Session 1 (Donald Ervin Knuth on February 23, 1981)
  17. Live Office Hours with Yuri Sagalov and Sam Altman - Stanford CS183F: Startup School
  18. How and Why to Start A Startup - Sam Altman & Dustin Moskovitz - Stanford CS183F: Startup School
  19. Stanford Seminar - Can the brain do back-propagation? Geoffrey Hinton
  20. Jen-Hsun Huang: Stanford student and Entrepreneur, co-founder and CEO of NVIDIA

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