In this video I show how to implement linear regression using the normal equation. If you want to check out the derivation I made a blog post that you can check out: https://aladdinperzon.github.i....o/2020/03/25/Linear-
People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
ML Course (affiliate): https://bit.ly/3qq20Sx
DL Specialization (affiliate): https://bit.ly/30npNrw
ML Course (no affiliate): https://bit.ly/3t8JqA9
DL Specialization (no affiliate): https://bit.ly/3t8JqA9

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PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

I was inspired to make these videos by this specialization: https://bit.ly/3SqLuA6

Progressive Growing of GANs for Improved Quality, Stability, and Variation: https://arxiv.org/abs/1710.10196

#PaperReview #PaperExplained

GitHub Repository:
https://github.com/aladdinpers....son/Machine-Learning

✅ Equipment I use and recommend:
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❤️ Become a Channel Member:
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✅ One-Time Donations:
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GAN Playlist:
https://youtube.com/playlist?l....ist=PLhhyoLH6IjfwIp8

PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

Timestamps:
0:00 - Introduction
0:34 - Overview
5:06 - Progressive growing
7:36 - MiniBatch Std
9:15 - Fading in new layers
13:37 - Normalization (PixelNorm & Eq. LR)
20:30 - Some results
20:55 - Implementations details
30:59 - Ending

#8k #china #60fps
Best of China 8K HDR Ultra HD - Relaxing nature movie with soothing music

This film was created for educational, entertainment and informative purposes.
Some footage have been originally recorded in 8K resolution and some in 4K. I upscaled all 4K footage to 8K resolution, resulting something new.
This film was re-edited in HDR10 standard and color corrected (high color correction , color grading , adjusted the blacks, adjusted the highlights and the saturation).
To watch this video in real HDR, you should use a HDR television set.
There are a lot of interesting facts that you probably didn’t know.
Shanghai is a financial and cultural hub for the entire world. Shanghai has the longest metro system in the world with 644 km of tunnels and track.
"The Pearl of Asia" and "The Paris of the East" are two nicknames for Shanghai.
Hong Kong is famous for many towering skyscrapers. The tallest skyscraper in Hong Kong is The International Commerce Center. Hong Kong is recognized as the crossroads of East and West.
Hangzhou is a famous tourist destination in this country. One of China's seven historic capitals is Hangzhou. Hangzhou has been known as "Capital of Tea" since ancient times.

#scenicrelaxation #8kvideo #relaxationmusic #relaxationfilm #8kvideoultrahd
------------------------------

🌿Welcome to the new relaxing music stream on Scenic Relaxation 8K channel. You can keep the video at low volume and start doing any work like studying, working, reading… or simply relaxing or getting a good night's sleep.

🌿Music to relax, meditate, study, read, massage, spa or sleep. This type of music is ideal to combat anxiety, stress or insomnia as it facilitates relaxation and helps us to get rid of bad vibrations. You can also use this music as a background for guided meditation classes or sleep relaxation.

🌿If you enjoyed the live stream and want more relaxing music content, don't forget to like and subscribe!

------------------------------
🎹More soothing music on Spotify playlist: https://spoti.fi/38bwOia

Music By:
"---------------------------------------------
🌿 Music by Helios Records:
➤ Spotify Relaxing: https://heliosrecords.fanlink.to/SpotifyRelaxing
➤ Spotify Concentration: https://heliosrecords.fanlink.....to/SpotifyConcentrat
➤ Instagram: https://heliosrecords.fanlink.to/Instagram
➤ Facebook: https://heliosrecords.fanlink.to/Facebook
➤ Tiktok: https://heliosrecords.fanlink.to/Tiktok
➤ SoundCloud: https://heliosrecords.fanlink.to/Soundcloud
---------------------------------------------"

"🌿 Music by Helios Relaxing Space
➤ Spotify: https://spoti.fi/3kJuAy3

🌿 Music by Vincent Carry
➤ Spotify: https://spoti.fi/2ZSgDCA

🌿 Music by Finn
➤ Spotify: https://spoti.fi/3Cm9QCk "

------------------------------

"🌞 For contact and submit music: [email protected]

►All rights belong to their respective owners.
✔ This video was given a special license directly from the artists and the right holders."

So what was the breakthrough that allowed deep nets to combat the vanishing gradient problem? The answer has two parts, the first of which involves the RBM, an algorithm that can automatically detect the inherent patterns in data by reconstructing the input.

Deep Learning TV on
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Twitter: https://twitter.com/deeplearningtv

Geoff Hinton of the University of Toronto, a pioneer and giant in the field, was able to devise a method for training deep nets. His work led to the creation of the Restricted Boltzmann Machine, or RBM.

Structurally, an RBM is a shallow neural net with just two layers – the visible layer and the hidden layer. In this net, each node connects to every node in the adjacent layer. The “restriction” refers to the fact that no two nodes from the same layer share a connection.

The goal of an RBM is to recreate the inputs as accurately as possible. During a forward pass, the inputs are modified by weights and biases and are used to activate the hidden layer. In the next pass, the activations from the hidden layer are modified by weights and biases and sent back to the input layer for activation. At the input layer, the modified activations are viewed as an input reconstruction and compared to the original input. A measure called KL Divergence is used to analyze the accuracy of the net. The training process involves continuously tweaking the weights and biases during both passes until the input is as close as possible to the reconstruction.

If you’ve ever worked with an RBM in one of your own projects, please comment and tell me about your experiences.

Because RBMs try to reconstruct the input, the data does not have to be labelled. This is important for many real-world applications because most data sets – photos, videos, and sensor signals for example – are unlabelled. By reconstructing the input, the RBM must also decipher the building blocks and patterns that are inherent in the data. Hence the RBM belongs to a family of feature extractors known as auto-encoders.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Dale’s Blog → https://goo.gle/3xOeWoK
Classify text with BERT → https://goo.gle/3AUB431

Over the past five years, Transformers, a neural network architecture, have completely transformed state-of-the-art natural language processing. Want to translate text with machine learning? Curious how an ML model could write a poem or an op ed? Transformers can do it all. In this episode of Making with ML, Dale Markowitz explains what transformers are, how they work, and why they’re so impactful. Watch to learn how you can start using transformers in your app!

Chapters:
0:00 - Intro
0:51 - What are transformers?
3:18 - How do transformers work?
7:41 - How are transformers used?
8:35 - Getting started with transformers

Watch more episodes of Making with Machine Learning → https://goo.gle/2YysJRY

Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech

#MakingwithMachineLearning #MakingwithML

product: Cloud - General; fullname: Dale Markowitz; re_ty: Publish;

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐃𝐞𝐯𝐎𝐩𝐬 𝐜𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 : https://www.edureka.co/devops-....certification-traini (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This DevOps Tutorial on Git Commands ( Git Blog series: https://goo.gl/XS1Vux ) will explain all the basic Git commands. You will learn about the commands like git add, git init, git pull, git branch etc.
00:00:00 Introduction
00:00:19 Git Basic Commands
00:07:45 Branching
00:14:54 Working with Remote Repositories
00:19:42 Advanced Git Commands

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🌕 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh


📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
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Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

MIT Introduction to Deep Learning 6.S191: Lecture 10
The Future of Robot Learning
Lecturer: Daniela Rus
2023 Edition

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

Lecture Outline - coming soon!


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!!

Concrete = cement + sand + gravel. Cement is the most important man-made material on Earth. Offset your carbon footprint on Wren: https://wren.co/veritasium . For the first 100 people who sign up, I will personally pay for the first month of your subscription!

▀▀▀
A huge thank you to Nevada Ready Mix for being willing to bury me in concrete, especially Elu Chavez and Mike Sherwood. https://www.nevadareadymix.com

And to Brandon Birchak of Six Foot Productions for providing the big fish bowl, safety equipment, planning and filming: https://www.sixfootcreations.com

▀▀▀
References:
Instant stone (just add water), Roots of Progress, https://rootsofprogress.org/in....stant-stone-just-add
https://rootsofprogress.org/cement-redux

Cement Chemistry and Sustainable Cementitious Materials
https://www.youtube.com/@cemen....tchemistryandsustain

Ahmad, S., Lawan, A., & Al-Osta, M. (2020). Effect of sugar dosage on setting time, microstructure and strength of Type I and Type V Portland cements. Case Studies in Construction Materials, 13, e00364. – https://ve42.co/Ahmad2020

Seymour, L. M., Maragh, J., Sabatini, P., Di Tommaso, M., Weaver, J. C., & Masic, A. (2023). Hot mixing: Mechanistic insights into the durability of ancient Roman concrete. Science advances, 9(1), eadd1602. -- https://ve42.co/Seymour2023

▀▀▀
Special thanks to our Patreon supporters:
Emil Abu Milad, Tj Steyn, meg noah, Bernard McGee, KeyWestr, Amadeo Bee, TTST, Balkrishna Heroor, John H. Austin, Jr., Eric Sexton, john kiehl, Anton Ragin, Benedikt Heinen, Diffbot, Gnare, Dave Kircher, Burt Humburg, Blake Byers, Evgeny Skvortsov, Meekay, Bill Linder, Paul Peijzel, Josh Hibschman, Mac Malkawi, Juan Benet, Ubiquity Ventures, Richard Sundvall, Lee Redden, Stephen Wilcox, Marinus Kuivenhoven, Michael Krugman, Cy 'kkm' K'Nelson, Sam Lutfi.

▀▀▀
Written by Derek Muller
Edited by Trenton Oliver
Filmed by Raquel Nuno, Austin Bradley and Bryson
Animated by Ivy Tello & Mike Radjabov
Additional video/photos supplied by Getty Images & Pond5
Music from Epidemic Sound & Jonny Hyman: the Bill Wurtz inspired ‘Skyscrapers are made of sea shells’
Produced by Derek Muller, Petr Lebedev, & Emily Zhang

🔥Edureka Tensorflow Training: https://www.edureka.co/ai-deep....-learning-with-tenso
This Edureka LSTM Explained video will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs.
00:00 Introduction
00:36 Agenda
00:47 Introduction to NLP
01:46 Ways to Process Text Data
02:57 Recurrent Neural Networks
22:02 Long Short-term Memory
51:46 LSTM Use Cases
53:45 Real Time Applications of LSTM

🔹Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
🔹Check our complete Deep Learning With TensorFlow Blog Series: http://bit.ly/2sqmP4s

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#Edureka #DeepLearningEdureka #LSTMExplained #NeuralNetworks #DeepLearningTraining #DeepearningTutorial #EdurekaTraining

How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

- - - - - - - - - - - - - -

About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.


- - - - - - - - - - - - - -

Who should go for this course?

The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'

2. Analytics Managers who are leading a team of analysts

3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

- - - - - - - - - - - - - -

Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.


For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).

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Demystifying attention, the key mechanism inside transformers and LLMs.
Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support
Special thanks to these supporters: https://www.3blue1brown.com/le....ssons/attention#than
An equally valuable form of support is to simply share the videos.

Demystifying self-attention, multiple heads, and cross-attention.
Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support

The first pass for the translated subtitles here is machine-generated, and therefore notably imperfect. To contribute edits or fixes, visit https://translate.3blue1brown.com/

And yes, at 22:00 (and elsewhere), "breaks" is a typo.

------------------

Here are a few other relevant resources

Build a GPT from scratch, by Andrej Karpathy
https://youtu.be/kCc8FmEb1nY

If you want a conceptual understanding of language models from the ground up, @vcubingx just started a short series of videos on the topic:
https://youtu.be/1il-s4mgNdI?si=XaVxj6bsdy3VkgEX

If you're interested in the herculean task of interpreting what these large networks might actually be doing, the Transformer Circuits posts by Anthropic are great. In particular, it was only after reading one of these that I started thinking of the combination of the value and output matrices as being a combined low-rank map from the embedding space to itself, which, at least in my mind, made things much clearer than other sources.
https://transformer-circuits.p....ub/2021/framework/in

Site with exercises related to ML programming and GPTs
https://www.gptandchill.ai/codingproblems

History of language models by Brit Cruise,  @ArtOfTheProblem 
https://youtu.be/OFS90-FX6pg

An early paper on how directions in embedding spaces have meaning:
https://arxiv.org/pdf/1301.3781.pdf

------------------

Timestamps:
0:00 - Recap on embeddings
1:39 - Motivating examples
4:29 - The attention pattern
11:08 - Masking
12:42 - Context size
13:10 - Values
15:44 - Counting parameters
18:21 - Cross-attention
19:19 - Multiple heads
22:16 - The output matrix
23:19 - Going deeper
24:54 - Ending

------------------

These animations are largely made using a custom Python library, manim. See the FAQ comments here:
https://3b1b.co/faq#manim
https://github.com/3b1b/manim
https://github.com/ManimCommunity/manim/

All code for specific videos is visible here:
https://github.com/3b1b/videos/

The music is by Vincent Rubinetti.
https://www.vincentrubinetti.com
https://vincerubinetti.bandcam....p.com/album/the-musi
https://open.spotify.com/album..../1dVyjwS8FBqXhRunaG5

------------------

3blue1brown is a channel about animating math, in all senses of the word animate. If you're reading the bottom of a video description, I'm guessing you're more interested than the average viewer in lessons here. It would mean a lot to me if you chose to stay up to date on new ones, either by subscribing here on YouTube or otherwise following on whichever platform below you check most regularly.

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Best Courses for Analytics:
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+ IBM Data Science (Python): https://bit.ly/3Rn00ZA
+ Google Analytics (R): https://bit.ly/3cPikLQ
+ SQL Basics: https://bit.ly/3Bd9nFu


Best Courses for Programming:
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+ Tableau: https://bit.ly/3q966AN
+ Excel: https://bit.ly/3RBxind

+ Computer Vision: https://bit.ly/3esxVS5
+ Natural Language Processing: https://bit.ly/3edXAgW

+ IBM Dev Ops: https://bit.ly/3RlVKt2
+ IBM Full Stack Cloud: https://bit.ly/3x0pOm6
+ Object Oriented Programming (Java): https://bit.ly/3Bfjn0K

+ TensorFlow Advanced Techniques: https://bit.ly/3BePQV2
+ TensorFlow Data and Deployment: https://bit.ly/3BbC5Xb
+ Generative Adversarial Networks / GANs (PyTorch): https://bit.ly/3RHQiRj


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🔥Data Analyst Masters Program (Discount Code - YTBE15) - https://www.simplilearn.com/data-analyst-masters-certification-training-course?utm_campaign=DoANDQMAmIg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥IITK - Professional Certificate Course in Data Analytics and Generative AI (India Only) - https://www.simplilearn.com/iitk-professional-certificate-course-data-analytics?utm_campaign=DoANDQMAmIg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Purdue - Post Graduate Program in Data Analytics - https://www.simplilearn.com/pgp-data-analytics-certification-training-course?utm_campaign=DoANDQMAmIg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Caltech - Data Analytics Bootcamp (US Only) - https://www.simplilearn.com/data-analytics-bootcamp?utm_campaign=DoANDQMAmIg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥IITG - Professional Certificate Program in Data Analytics and Generative AI (India Only) - https://www.simplilearn.com/iitg-generative-ai-data-analytics-program?utm_campaign=DoANDQMAmIg&utm_medium=DescriptionFirstFold&utm_source=Youtube

This Excel Full course video by simplilearn will help you learn Microsoft excel from basics to advanced concepts. This Introduction to Excel Full Course provides a detailed guide to learning Excel and related data analytics tools. Starting with what Microsoft Excel is, the course offers an Excel tutorial for beginners, teaching you the basics. You'll learn advanced features like Microsoft Power Query and how to use Copilot in Excel for enhanced productivity. The course covers 10 essential Excel formulas, explains the concept of DBMS, and explores data analytics using AI. It also compares Data Science vs. Data Analytics and outlines a roadmap to becoming a data analyst. Additionally, it introduces key BI terms every data analyst should know and demonstrates how to use ChatGPT to build an Excel dashboard. To solidify your skills, it concludes with the top 10 data analyst projects for your portfolio, providing practical insights to excel in your career.

00:00:00 Introduction to Excel Full Course
00:03:55 What is Microsoft Excel
05:42:00 Excel Tutorial For Beginners
06:03:24 How to use Microsoft power query
06:12:45 Copilot In EXCEL
06:36:31 10 important excel formulas
06:37:39 What is DBMS
06:48:32 Data Analytics using AI
07:12:59 Data Science Vs Data Analyst
07:31:40 Data Analyst Roadmap
07:32:29 Top BI Terms every data analyst know
08:09:23 How to use ChatGPT to built an Excel Dashboard
08:21:58 Top 10 Data Analyst Projects for your portfolio

⏩ Check out the Excel tutorial videos: https://www.youtube.com/watch?v=nPkmWE4JCfE&list=PLEiEAq2VkUUKf8aLrspLg3zuyJ5S-5K5S

#excelfullcourse #excelcourse #excel #exceldataanalytics #exceltraining #excelformulasandfunctions #exceldatavisualization #simplilearn #2024

➡️ About Post Graduate Program In Data Analytics
This Data Analytics Program is ideal for all working professionals and prior programming knowledge is not required. It covers topics like data analysis, data visualization, regression techniques, and supervised learning in-depth via our applied learning model with live sessions by leading practitioners and industry projects.

Key Features
✅ Post Graduate Program certificate and Alumni Association membership
✅ Exclusive hackathons and Ask me Anything sessions by IBM
✅ 8X higher live interaction in live online classes by industry experts
✅ Capstone from 3 domains and 14+ Data Analytics Projects with Industry datasets from Google PlayStore, Lyft, World Bank etc.
✅ Master Classes delivered by Purdue faculty and IBM experts
✅ Simplilearn's JobAssist helps you get noticed by top hiring companies
✅ Resume preparation and LinkedIn profile building
✅ 1:1 mock interview
✅ Career accelerator webinars

Skills Covered
✅ Data Analytics
✅ Statistical Analysis using Excel
✅ Data Analysis Python and R
✅ Data Visualization Tableau and Power BI
✅ Linear and logistic regression modules
✅ Clustering using kmeans
✅ Supervised Learning

👉 Learn More at: https://www.simplilearn.com/pgp-data-analytics-certification-training-course?utm_campaign=DoANDQMAmIg&utm_medium=Description&utm_source=youtube

A video about reinforcement learning, Q-networks, and policy gradients, explained in a friendly tone with examples and figures.

Introduction to neural networks: https://www.youtube.com/watch?v=BR9h47Jtqyw

Introduction: (0:00)
Markov decision processes (MDP): (1:09)
Rewards: (5:39)
Discount factor: (8:51)
Bellman equation: (10:48)
Solving the Bellman equation: (12:43)
Deterministic vs stochastic processes: (16:29)
Neural networks: (19:15)
Value neural networks: (21:44)
Policy neural networks: (25:44)
Training the policy neural network: (30:46)
Conclusion: (34:53)

Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt

Anthropic’s Alex Albert (Claude Relations) sits down with Erik Schluntz (Multi-Agent Research and co-author of our blog post, Building Effective Agents) for a discussion on the evolution of agents over the past six months, including tips for building multi-agent systems, common multi-agent patterns, and best practices for using skills, MCP servers, and tools.

00:00 - Introductions
00:35 - Training Claude to tackle agentic tasks
1:30 - Making Claude more autonomous with code
3:20 - Using the Claude Agent SDK to build agents
5:00 - Tips for using Agent Skills
6:40 - The evolution of workflows and agents (workflows of agents)
8:30 - The value of simple agent architectures
9:30 - Building multi-agent systems: orchestrators, subagents, and tool calling
11:40 - Training Claude to use subagents
12:25 - Multi-agent use design patterns: parallelization, MapReduce, and test-time compute
13:20 - Coordinating problem solving with tools and subagents
14:15 - Common agent failure modes
15:00 - Best practices for getting started with building agents (context engineering, MCPs, and tools)
17:15 - The future of agents: coding, computer use, and beyond

Read the original blog post: https://www.anthropic.com/engi....neering/building-eff
Learn more about Agent Skills: https://www.anthropic.com/engi....neering/equipping-ag

MIT 15.401 Finance Theory I, Fall 2008
View the complete course: http://ocw.mit.edu/15-401F08
Instructor: Andrew Lo

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

This video introduces the fundamentals of protein design and summarizes the trajectory of the course with a focus on (1) the foundational biochemistry that protein structure prediction and design hinge on and (2) deep learning / machine learning principles overview.

Video from the Rosetta Commons PPI Workshop (February 2025)
Video Instructor: Amrita Nallathambi (UNC Chapel Hill)

Credits:
Instructor: Amrita Nallathambi
Teaching Assistants: Yehlin Cho, Cyrus Haas, and Matthew Hvasta,
RC Leadership and NSF Sponsor Grant PIs: Julia Koehler Leman & Jeffrey Gray
RC Education Director: Ashley Vater
Videographer: Canyon Florey
Rosetta Workshop Participants

00:00 - Introduction
00:20 - Deep Learning Revolution for Proteins
01:50 - The Transformer
03:38 - AlphaFold2 Overview
06:22 - AlphaFold2 Inputs
09:48 - AlphaFold2 Outputs
13:29 - Graph Neural Networks
16:24 - ProteinMPNN Loss
17:35 - Diffusion models
18:30 - RFDiffusion
19:52 - Inputs and Outputs
20:43 - Potentials