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MIT Introduction to Deep Learning 6.S191: Lecture 5
Deep Reinforcement Learning
Lecturer: Alexander Amini
2023 Edition

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

Lecture Outline:
0:00 - Introduction
3:49 - Classes of learning problems
6:48 - Definitions
12:24 - The Q function
17:06 - Deeper into the Q function
21:32 - Deep Q Networks
29:15 - Atari results and limitations
32:42 - Policy learning algorithms
36:42 - Discrete vs continuous actions
39:48 - Training policy gradients
47:17 - RL in real life
49:55 - VISTA simulator
52:04 - AlphaGo and AlphaZero and MuZero
56:34 - Summary


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Whether you like it or not, generative AI like ChatGPT and Stable Diffusion are about to change not only how you work, but how the content you consume is produced. Forbes spoke with a number of leading voices in the AI space to determine both the benefits and the dangers of this next wave of technological innovation, and find out why both tech giants as well as cutting edge startups are racing to grab their share of the market.

0:00 Introduction
1:17 What is generative AI?
2:02 Why Forbes decided to cover this story
2:18 The rise of Open AI
3:11 AI's recent hype
4:30 Stability AI and Stable Diffusion
6:39 Bill Gates thoughts on generative AI
7:53 Where AI can help in workflows
9:56 The issues with AI that need to be resolved
12:04 How do we set safeguards for AI to protect society?
15:14 How will we further incorporate AI in the future?
19:14 The idea of "platform democracy"
20:56 How we used AI for this video

Read the full story on Forbes: https://www.forbes.com/sites/a....lexkonrad/2023/02/02

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Finally understand pointers in C in this course for absolute beginners. Pointers are variables that store the memory address of another variable. They "point" to the location of data in memory. With a bunch of examples, this course demystifies pointers and their various uses, covering topics such as passing by reference vs. value, void pointers, arrays, and more.

✏️ Course created by @onaecO

⭐️ Contents ⭐️
(0:00:00) Introduction
(0:00:39) What is a computer eli5 CPU, RAM, bytes
(0:08:04) Data Types
(0:13:31) Intro to processes
(0:16:44) process memory layout
(0:19:17) Variables in memory
(0:23:01) Naive change_value program
(0:28:05) Change_value with pointers
(0:33:03) The classic swap
(0:34:05) Why declaration and dereference have the same syntax for pointers?
(0:38:39) Advantages of passing by reference va passing by value
(0:45:26) Why do pointers to different data types have the same size?
(0:47:49) Given that pointers have all the same size, why do we need a pointer type?
(0:58:16) void pointers are confusing
(1:00:14) why malloc is handy and more on void*
(1:09:09) Are arrays just pointers?
(1:25:00) Array Decay into a pointer
(1:32:59) why array decay is useful?
(1:37:49) arr[5] == 5[arr]
(1:39:04) pointers to pointers: **argv
(1:47:11) *argv[] or **argv?
(1:52:41) pointer to functions
(1:59:02) use case with pointers to functions

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This "Salesforce Apex Tutorial for Beginners" video by Edureka will help you understand about salesforce's apex programming language. This video is ideal for beginners and will talk about some of the basic topics in apex programming languages, like classes in apex, datatypes and variables in apex, conditional statements in apex and so on. It will also explain how to write your first apex program:
00:00:00 Introduction
00:01:23 WHAT IS APEX IN SALESFORCE?
00:04:37 WHEN TO CHOOSE APEX?
00:05:56 APEX DEVELOPER ENVIRONMENT
00:12:34 CLASSES IN SALESFORCE
00:16:31 APEX DATATYPE & VARIABLES
00:23:49 KEYWORDS IN APEX
00:26:03 APEX CONDTIONAL STATEMENTS
00:30:13 APEX LOOP STATEMENT
00:34:42 WHAT ARE TRIGGERS IN SALESFORCE?
00:35:22 SYNTAX FOR WRITING A TRIGGER

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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.

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

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

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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
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✅ Master Classes delivered by Purdue faculty and IBM experts
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✅ 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

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MIT 6.7960 Deep Learning, Fall 2024
Instructor: Sara Beery
View the complete course: https://ocw.mit.edu/courses/6-....7960-deep-learning-f
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

This video provides a course overview and introduces deep neural networks, covering their fundamental concepts and basic building blocks. It sets the stage for understanding how these models work and what components they are built from.

License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRlQ

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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

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

✅ Equipment I use and recommend:
https://www.amazon.com/shop/aladdinpersson

❤️ Become a Channel Member:
https://www.youtube.com/channe....l/UCkzW5JSFwvKRjXABI

✅ One-Time Donations:
Paypal: https://bit.ly/3buoRYH
Ethereum: 0xc84008f43d2E0bC01d925CC35915CdE92c2e99dc

▶️ You Can Connect with me on:
Twitter - https://twitter.com/aladdinpersson
LinkedIn - https://www.linkedin.com/in/al....addin-persson-a95384
GitHub - https://github.com/aladdinpersson

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:
https://www.amazon.com/shop/aladdinpersson

❤️ Become a Channel Member:
https://www.youtube.com/channe....l/UCkzW5JSFwvKRjXABI

✅ One-Time Donations:
Paypal: https://bit.ly/3buoRYH
Ethereum: 0xc84008f43d2E0bC01d925CC35915CdE92c2e99dc

▶️ You Can Connect with me on:
Twitter - https://twitter.com/aladdinpersson
LinkedIn - https://www.linkedin.com/in/al....addin-persson-a95384
Github - https://github.com/aladdinpersson

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

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