Is it ever too late to expand your knowledge of computer science and AI? Mehran Sahami, Professor and Chair of the Computer Science Department at Stanford University, addresses this and other questions as he shares his expertise.

Learn about Code in Place:
https://codeinplace.stanford.edu/

Learn about our Artificial Intelligence Courses and Programs:
https://online.stanford.edu/ar....tificial-intelligenc

Learn about our Computer Science & Security Programs:
https://online.stanford.edu/co....mputer-science-secur

View course details: https://online.stanford.edu/co....urses/xcs224r-deep-r

April 2, 2025
This lecture covers:
• Class introduction
• Markov Decisions Processes
• Why study deep reinforcement learning?
• Intro to modeling behavior and reinforcement learning

To learn more about enrolling in the graduate course, visit: https://online.stanford.edu/co....urses/cs224r-deep-re

To follow along with the course schedule and syllabus, visit:
https://cs224r.stanford.edu/

Chelsea Finn
Assistant Professor in Computer Science and Electrical Engineering at Stanford University and co-founder of Pi.

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

November 21, 2025
This lecture covers:
• LLM-as-a-judge overview
• Best practices and benefits
• Biases and pitfalls

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

Chapters:
00:00:00 Introduction
00:07:08 Inter-rater agreement metrics
00:18:24 Rule-based metrics
00:21:00 METEOR, BLEU ROUGE
00:28:00 LLM-as-a-judge
00:33:44 Structured outputs
00:36:48 Variants
00:38:47 Position, verbosity, self-enhancement bias
00:47:22 Best practices
00:54:06 Factuality
01:00:15 Agent evaluation
01:23:50 Benchmarks
01:25:12 Knowledge with MMLU
01:29:34 Reasoning AIME, PIQA
01:33:57 Coding with SWE-bench
01:36:15 Safety with HarmBench
01:40:51 Agents with Tau-Bench

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 flexible graduate programs visit: https://learn.stanford.edu/YouTube-Grad.html

If you're interested in deepening your expertise and progressing your professional skill set, why not look into the graduate course and program options offered by Stanford Online? During our online information session, you'll discover the range of graduate opportunities available to you, what you can look forward to, and essential information to help you make an informed decision prior to enrollment.

The session includes:
Graduate Course Overview: Here’s what you can expect
Key information about applying and enrolling
Audience Q&A

Explore Stanford Online's graduate education options: https://online.stanford.edu/graduate-education

#gradschool #graduateprogram #onlineeducation

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

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!!

The imagedeep.io series serves to bridge the knowledge gap between medical imaging and AI education.

It was co-founded and is co-instructed by:

Mazen Zawaideh, MD. Chief Radiology Resident and imagedeep.io co-instructor.

David Haynor, MD, Ph.D. Professor of Neuroradiology and imagedeep.io co-instructor.

Nathan Cross, MD. Assistant Professor of Neuroradiology and imagedeep.io co-instructor.

To learn more, visit www.imagedeep.io

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

https://github.com/aamini/introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning - aamini/introtodeeplearning

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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.

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

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

Introduction to Deep Learning and Generative Models (Spring 2020). Optional lecture 6.5, explaining how to use Google Colab for Jupyter notebooks with a free GPU.

Welcome to the first video in my Deep Learning With Pytorch playlist!

In this video I'll talk a little bit about what a Neural Network is, and give you a little bit of background history on Pytorch vs. Tensorflow.

Then we'll set up a development environment using Google Colab, and create a first notebook and push it to a Github repository.

#pytorch #codemy #JohnElder

Timecodes

0:00​​ - Introduction
1:12 - Pytorch vs. Tensorflow
2:32 - What is a Neural Network?
4:10 - How Much Math Do You Need?
7:14 - Pytorch Documentation
7:37 - Development Environment Google Colab
9:09 - Use GPU Runtime
10:24 - Check Latest Version of Pytorch
12:04 - Set Up Github Repository
13:36 - Authorize Colab In Github
14:35 - Push Notebook To Github
16:09 - Conclusion

Intro to Deep Learning - Part 1

In this video, I introduce the fundamentals of deep learning with a real-world scenario:
📌 Predicting how much it will rain tomorrow (precipitation).

Suitable for beginner to intermediate practitioners in machine learning and AI.

🔍 Topics Covered in Part 1:
1. Scenario: How can we predict precipitation?
2. Data-Driven Approach:
- Adding more variables (features)
- Nearest neighbor
- Mapping vectors to real numbers
3. Machine Learning:
- Random search

💡 Coming in Part 2:
3. Machine Learning:
- Derivative
- Gradient Descent
4. Conclusion and Summary