MIT 7.016 Introductory Biology, Fall 2018
Instructor: Barbara Imperiali
View the complete course: https://ocw.mit.edu/7-016F18
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

Professor Imperiali covers the basics of covalent and non-covalent chemical bonding. She then focuses on lipids, their structures and properties, and the formation of lipid bilayers.

License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

November 18, 2025
This lecture covers career advice and a guest speaker.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

Please follow along with the course schedule and syllabus: https://cs230.stanford.edu/syllabus/

View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

Guest Speaker
Laurence Moroney
Best-selling AI author and award-winning researcher

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

Sam Altman, President of Y Combinator, and Dustin Moskovitz, Cofounder of Facebook and Asana, kick off the How to Start a Startup Course. Dustin discusses Why to Start a Startup and Sam introduces the 4 key components of Starting a Startup: Idea, Product, Team and Execution.
http://www.slideshare.net/Stev....enPham7/lecture-1-ho

Explore Stanford Online courses: https://online.stanford.edu/explore

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

October 17, 2025
This lecture covers:
• Pretraining
• Quantization
• Hardware optimization
• Supervised finetuning (SFT)
• Parameter-efficient finetuning (LoRA)

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:07:19 Pretraining
00:13:26 FLOPs, FLOPS
00:16:34 Scaling laws, Chinchilla law
00:24:49 Training optimizations overview
00:31:09 Data parallelism with ZeRO
00:35:51 Model parallelism
00:38:26 Flash Attention
00:52:37 Quantization
00:56:00 Mixed precision training
01:02:31 Supervised finetuning
01:09:21 Instruction tuning
01:37:53 Parameter-efficient finetuning with LoRA
01:45:16 QLoRA

Afshine Amidi is an Adjunct Lecturer at Stanford University.

Shervine Amidi is an Adjunct Lecturer at Stanford University.

View the course playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

November 14, 2025
This lecture covers:
• Retrieval-augmented generation
• Advanced RAG techniques
• Function calling
• Agents
• ReAct framework

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:06:38 RAG overview
00:27:39 Similarity search with SBERT and bi-encoders
00:34:25 Heuristic search with BM25
00:37:54 HyDE and contextual retrieval
00:41:00 Prompt caching
00:45:24 Re-ranking with cross-encoders
00:47:49 Retrieval evaluation with NDCG, MRR
00:59:28 Tool calling
01:26:22 Tool selection
01:29:17 Model Context Protocol (MCP)
01:31:56 Agents with ReAct
01:42:16 Safety and closing thoughts

Afshine Amidi is an Adjunct Lecturer at Stanford University.

Shervine Amidi is an Adjunct Lecturer at Stanford University.

View the course playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai

August 7, 2025

Guest Lecture:
Gabor Angeli
AI Research Engineer, Resolve AI

Bharat Khandelwal
AI Research Engineer, Resolve AI

Spiros Xanthos
Founder & CEO, Resolve AI

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

December 5, 2025
This lecture covers:
• Recap
• Trending topics
• Closing thoughts

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:01:12 Transformer
00:06:35 Transformer-based models & tricks
00:11:17 Large Language Models
00:15:05 LLM training
00:24:09 LLM tuning
00:29:41 LLM reasoning
00:38:37 Agentic LLMs (RAG, tool calling)
00:44:09 LLM evaluation
00:48:57 Vision Transformer
01:04:02 Diffusion-based LLMs
01:23:38 Closing thoughts
01:50:16 Thank you!

Afshine Amidi is an Adjunct Lecturer at Stanford University.

Shervine Amidi is an Adjunct Lecturer at Stanford University.

View the course playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

MIT Introduction to Deep Learning 6.S191: Lecture 1
*New 2025 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini

For all lectures, slides, and lab materials: http://introtodeeplearning.com/

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MIT Introduction to Deep Learning 6.S191: Lecture 2
Recurrent Neural Networks
Lecturer: Ava Amini
** New 2025 Edition **

For all lectures, slides, and lab materials: http://introtodeeplearning.com

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In this lecture, we highlight the course logistics and give a brief overview of the course.

Sebastian's books: https://sebastianraschka.com/books/
The lecture slides are available at: https://github.com/rasbt/stat4....53-deep-learning-ss2

Covers some of the basics of recurrent neural networks. In particular, this lecture covers

RNNs and Sequence Modeling Tasks: 00:00
Backpropagation Through Time: 20:23
Long-short term memory (LSTM): 31:42
Many-to-one Word RNNs: 45:16
Generating Text with Character RNNs: 50:45
Attention Mechanisms and Transformers: 1:00:09

In this video, we'll go through data preprocessing steps for 3 different datasets. We'll also go in depth on a dimensionality reduction technique called Principal Component Analysis.

Coding challenge for this video:
https://github.com/llSourcell/....How_to_Make_Data_Ama

Charles-David's Winning Code:
https://github.com/alkaya/earthquake-cotw

Siby Jack Grove's Runner-up code:
https://github.com/sibyjackgro....ve/Earthquake_predic

Please subscribe. And like. And comment. That's what keeps me going.

More Learning Resources:
http://www.cs.ccsu.edu/~markov..../ccsu_courses/datami
http://www.slideshare.net/jaso....nrodrigues/data-prep
http://iasri.res.in/ebook/win_....school_aa/notes/Data
http://staffwww.itn.liu.se/~ai....dvi/courses/06/dm/le
http://ufldl.stanford.edu/wiki..../index.php/Data_Prep
http://machinelearningmastery.....com/how-to-prepare-d
https://plot.ly/ipython-notebo....oks/principal-compon

Public datasets:
https://github.com/caesar0301/....awesome-public-datas
https://aws.amazon.com/public-datasets/
http://archive.ics.uci.edu/ml/index.html
https://dreamtolearn.com/ryan/1001_datasets

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Deep learning is essential to developing cutting-edge AI, including image recognition, sound and voice recognition, and complex generative learning models - to name a few. Using Python within Anaconda, the process of building these models is easier with updated packages, security concerns mitigated, clean and clear notebooks, and multiple platforms installed at once.

Learn how to talk Machine Learning with the best of them. I demystify the magic of deep learning and explain the buzz words

MIT Introduction to Deep Learning 6.S191: Lecture 3
Convolutional Neural Networks for Computer Vision
Lecturer: Alexander Amini
** New 2025 Edition **

For all lectures, slides, and lab materials: http://introtodeeplearning.com​

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

Prepare for the course