Помощь проекту: https://vertdider.tv/to-support-us/

Чему мы обязаны успехами, которых достигаем? В своем новом видео Дерек Маллер рассматривает вопрос о том, какова роль случая в наших победах и как мы субъективно оцениваем свои усилия. Из ролика узнаем, как приписывание успеха собственному упорству мешает оценить других людей, как сиднейские поезда помогли Дереку познакомиться с женой и, наконец, что же делать, чтобы достичь своих целей.

Перевод: Елена смотрова
Редактура: Алексей Малов
Озвучка: Дмитрий Чепусов
Монтаж звука: Андрей Фокин
Монтаж видео: Джон Исмаилов
Обложка: Андрей Гавриков

Спасибо за поддержку на https://www.patreon.com/VertDider.
Озвучки Vert Dider выходят с вашей помощью:

Pavel Dunaev, Nick Denizhenko, Yegor Barakovskiy, Oleksii, Ігор Дорохов, Mikhail Stolpovskiy, Роман Чурин, Aiorraro, Евгений Сельменев, Alex Afalex, Sergei W, Виталий Алешко, Luba Karpenko, Владимир Кашутин, Pavel Parpura, Anton Bolotov, Yevhen, Katoto Chan, Kosoy, Данил Рягузов, Михаил Панькин, Anton Makiievskyi, Максим 'Sheridan' Горлов, Sultan Ishankulov, Александр Яковлев, Mike Sviblov, Andrey Rusanov, Inna Klymenko, Dima Chumakov, Dmitriy Lashtaba, Olesya Bolobova, Ilya Alexeevsky, Alexander Balynskiy, Максим Иванов, Ihar Marozau, Мария Соозарь, Ольга Канкулова, Alexander, Viktor Lova, Maria Tronina, Sergii Cherepanov, Alexander Gorodok, Ivan Iakimov, Alexander Zimin, Евгений Миф, Виталий Пастушенко, Serj Kravchuk, Claudia Barzaeva, Pandaben, Monza UA, Lirin Alex, Konstantin Pesyakov, Denis Titusov, Alterien, Viktoria, Alexandr Globov, Oleg Streltsov, Olga Podolskaya, Alexey Kukushkin, F23D24, Olga Shistareva, Kamerton_440, Alex Katkov, Alexey, Evgeny Vrublevsky, Irina Shakhverdova, Vladimir Grachev, Dmitriy Omelyansky, Maria Burtseva, Aleksey Sazonov, Vladimir Goshev, Vladyslav Sokolenko, Дмитрий Мисюра, Yuliia Zinko, Anton Novozhenin, Spartak Kagramanyan, Александр Фалалеев

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Мы в социальных сетях:
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Разрешение на публикацию: https://goo.gl/6n4dT7
© https://www.youtube.com/user/1veritasium
Источник: https://youtu.be/3LopI4YeC4I

There's more over on Veritasium! "What is NOT Random?": https://www.youtube.com/watch?v=sMb00lz-IfE
SOURCES AND MORE BELOW!

My twitter: https://twitter.com/tweetsauce
My instagram: http://instagram.com/electricpants

Generate random numbers using atmospheric noise: http://www.random.org/

randomness:

http://www.random.org/randomness/
http://www.random.org/analysis/
http://faculty.rhodes.edu/wetz....el/random/mainbody.h
http://faculty.rhodes.edu/wetz....el/random/level23int
https://www.cs.auckland.ac.nz/....~chaitin/sciamer.htm

flipping a coin until 10 heads happen in a row: https://www.youtube.com/watch?v=rwvIGNXY21Y

rolling dice until you get a Yahtzee: https://www.youtube.com/watch?v=fiTwar7mFws

find word in YouTube video URLs with this in Google: allinurl:[your word here] site:youtube.com/watch

"Random" as slang:

http://www.nytimes.com/2010/10/31/magazine/31FOB-onlanguage-t.html?_r=3&ref=on_language
http://www.theawl.com/2011/03/....our-obsession-with-t

The many sides of dice: http://commons.wikimedia.org/w....iki/Dice_by_number_o

non-transitive dice: http://mathsgear.co.uk/product....s/non-transitive-gri

Checking the fairness of dice:

http://www.awesomedice.com/blo....g/353/d20-dice-rando
http://www.1000d4.com/wp-conte....nt/uploads/2013/02/d
https://www.youtube.com/watch?v=NKhpYJzCcSw
http://www.dakkadakka.com/wiki/en/That's_How_I_Roll_-_A_Scientific_Analysis_of_Dice
http://www.insidescience.org/b....log/2012/09/12/dice-

A fancy super-fair die:

http://kotaku.com/the-most-tec....hnologically-advance

coin-flipping odds:

http://econ.ucsb.edu/~doug/240a/Coin%20Flip.htm
[PDF] http://statweb.stanford.edu/~s....usan/papers/headswit
http://www.dartmouth.edu/~chan....ce/chance_news/recen

a nickel landing on it's side:

http://labs.adsabs.harvard.edu..../adsabs/abs/1993PhRv
[PDF]: http://arxiv.org/pdf/1008.4559.pdf

a book that will keep you guessing: http://www.amazon.com/Million-....Random-Digits-Normal

17 'feels' random: http://scienceblogs.com/cognit....ivedaily/2007/02/05/

How to be random (er.. wandom): http://www.wikihow.com/Be-Random

Bell's inequality:

http://drchinese.com/David/Bel....l_Theorem_Easy_Math.
http://www.felderbooks.com/papers/bell.html
http://www.quantiki.org/wiki/Bell's_theorem

Bell's inequality videos:

https://www.youtube.com/watch?v=z-s3q9wlLag
https://www.youtube.com/watch?v=7zfnvGXpy-g
https://www.youtube.com/watch?v=qd-tKr0LJTM

You can't even handle how wandom and quirky I am: https://www.youtube.com/watch?v=STSNHeAtETM

160 Greatest Arnold Schwarzenegger Quotes https://www.youtube.com/watch?v=pDxn0Xfqkgw

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Day - 1 | Introduction to Generative AI

The moment we've all been eagerly waiting for is finally here! Today marks the commencement of our much-anticipated course on GENERATIVE AI. 🎉

We'll be diving into fascinating topics like LLM, LlamaIndex, Vector DB, and much more. It's your chance to embark on a comprehensive exploration of cutting-edge concepts.

For course details please visit : https://ineuron.ai/course/gene....rative-ai-community-

Connect with us on : +91 8071176111

Social Media Links:-
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** Data Science Master's Program: https://www.edureka.co/masters....-program/data-scient **
This Edureka "Data Science Training" video (Data Science Blog Series: https://goo.gl/1CKTyN) will help you understand all the concepts of Data Science. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:

1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning

Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS

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1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
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. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!

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

About the Course

Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.

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

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Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.

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

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The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

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2. Analytics Managers who are leading a team of analysts
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4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies

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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Customer Reviews:

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🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 𝐌𝐚𝐬𝐭𝐞𝐫 𝐏𝐫𝐨𝐠𝐫𝐚𝐦: https://www.edureka.co/masters....-program/machine-lea (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka tutorial explains the 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, Various techniques used for feature selection like filter methods, wrapper methods. Embedded methods along with practical examples in Python. Explore the feature selection statistics and advantages and disadvantages of feature selection.
We will be covering the following topics in this video:
00:00:00 - Introduction
00:00:52 - Need for Feature Selection
00:02:15 - What is Feature Selection?
00:02:51 - Importance of Feature Selection
00:03:32 - Feature Selection Statistics
00:04:52 - Various Techniques
00:05:28 - Filter Method with Hands-on
00:10:20 - Wrapper Method with Hands-on
00:14:22 - Embedded Method with Hands-on
00:17:48- Advantages of Feature Selection
00:18:27 - Disadvantages of Feature Selection
📝Feel free to comment your doubts in the comment section below, and we will be happy to answer📝

-------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧---------

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--------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝗼𝘀𝘁 𝗚𝗿𝗮𝗱𝘂𝗮𝘁𝗲 𝐂𝐨𝐮𝐫𝐬𝐞𝐬--------
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⏩𝐓𝐨𝐩 𝟏𝟎 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟮: https://bit.ly/3yhEs7S
⏩𝐓𝐨𝐩 𝟏𝟎 𝐇𝐢𝐠𝐡𝐞𝐬𝐭 𝐏𝐚𝐲𝐢𝐧𝐠 𝐉𝐨𝐛𝐬 𝐅𝐨𝐫 𝟐𝟎𝟐𝟐: https://bit.ly/3HcpPG6
⏩𝐓𝐨𝐩 𝟏𝟎 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬 𝐟𝐨𝐫 𝟐𝟎𝟐𝟐: https://bit.ly/3FW6F69
⏩𝐓𝐨𝐩 𝟏𝟎 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝟐𝟎𝟐𝟐: https://bit.ly/3gH57mr

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About the Machine Learning with Python Training
Edureka’s Machine Learning Certification Training using Python is designed to make you grab the concepts of Machine Learning. This Machine Learning Certification will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in the python programming language. Furthermore, in this Machine Learning with Python Training, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real-life scenarios using Machine Learning Algorithms. Towards the end of this Machine Learning online course, we will be discussing various practical use cases of Machine Learning with Python programming language to enhance your learning experience. Edureka offers the best Machine Learning Training online for those who want to be the best in Python. Enroll now with Edureka's online Machine Learning Certification to get trained with industrial experts.

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This video on What is Machine Learning will make you understand the basics of Machine Learning and how Machine Learning works. By the end of this tutorial, you will be able to understand the different types of Machine Learning algorithms and some of the breakthroughs in the Machine Learning field. You will also learn what machine learning has to offer in terms of career opportunities in this machine learning tutorial.

This Machine Learning tutorial video will cover the following topics:

00:00 - 00:55 Real-life applications of Machine Learning
00:55 - 01:56 Challenges before Machine Learning
01:56 - 02:41 How did Machine Learning evolve?
02:41 - 03:48 Why Machine Learning / Machine Learning benefits
03:48 - 04:48 What is Machine Learning?
04:48 - 05:22 Types of Machine Learning
05:22 - 08:44 Machine Learning algorithms
08:44 - 12:57 Breakthroughs in Machine Learning
12:57 - 14:38 Machine Learning future
14:30 - 15:26 Machine Learning career
15:26 - 17:10 Machine Learning job trends

#WhatisMachineLearing #MachineLearningAlgorithms #Datasciencecourse #datascience #SimplilearnMachineLearning #MachineLearningCourse #Simplilearn

What is Machine Learning?
Machine Learning is a method that enables computers to imitate and adapt human-like behaviour, just like you and me. It isn’t programmed to perform a task. It analyses the data it has received previously, learns from it, and makes predictions or decisions for the problem scenario. In a way, the computer is training itself to perform a task the right way, after it learns everything it can from the data.

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➡️ About Post Graduate Program In AI And Machine Learning

This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

✅ Key Features

- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more
- Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools

✅ Skills Covered

- ChatGPT
- Generative AI
- Explainable AI
- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
- And Many More…

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About the Webinar
Unlock the transformative power of artificial intelligence with our Applied Generative AI Specialization in partnership with Purdue University.

Join us on April 18th, 09:00 PM, for an exclusive insight into our Applied Generative AI Specialization. Discover the cutting-edge curriculum, hands-on projects, and unique features of this program designed to prepare you for success in developing and deploying AI-driven solutions.

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Topics Covered:

In this enlightening session, we will explore key components of our Applied AI Course, including:

✅ Foundational AI Concepts
✅ Developing AI Applications
✅ AI in Practice
✅ AI Governance
✅ Interactive Q&A Session

About Applied Generative AI Specialization

Master Generative AI with this cutting-edge program delivered collaboratively by Purdue University Online and Simplilearn. Explore concepts like prompt engineering, large language models, attention mechanisms, and LLM fine-tuning. Learn in-demand tools and shape the future of intelligent systems.

Key Features
✅ Program completion certificate from Purdue University Online and Simplilearn
✅ Access to Purdue’s alumni association membership on program completion
✅ 50+ hours of core curriculum delivered in live online classes by industry experts
✅ Build Generative AI-enabled applications through hands-on projects
✅ Live online masterclasses delivered by Purdue faculty and staff
✅ Gain exposure to ChatGPT, OpenAI, Hugging Face, DALL-E 2 and other prominent tools
✅ Explore concepts like prompt engineering, attention mechanism, transformers, LLM application development, and LLM fine-tuning
✅ Simplilearn's JobAssist helps you get noticed by top hiring companies
✅ Earn official trophy and badges for ‘Microsoft Azure AI Fundamentals: Generative AI’ on the Microsoft Learn portal

Applied Gen AI Course Advantage
Gain a competitive edge with hands-on learning in the transformative arena of Generative AI. Master prompt engineering, LLMs, attention mechanisms, fine-tuning techniques, and LLM application development.

Applied Gen AI Course Details

Explore our Applied Generative AI program to deploy advanced techniques in real-world scenarios. Master GANs, VAEs, and prompt engineering, alongside tools like ChatGPT, DALL-E 2, OpenAI and Hugging Face. Develop LLM applications, fine-tuning skills, and explore governance principles.

Learning Path
✅ AGS: Program Induction
✅ AGS: Python Basics (Optional)
✅ AGS: Essentials of Generative AI, Prompt Engineering & ChatGPT
✅ AGS: Advanced Generative AI - Models and Architecture
✅ AGS: Advanced Generative AI - Building LLM Applications
✅ AGS: Advanced Generative AI - Image Generation Capabilities
✅ AGS: Generative AI Governance
Electives:
✅ AGS: Microsoft Azure AI Fundamentals - Generative AI
✅ AGS: Academic Masterclass

Skills Covered
✅ Python Programming
✅ Explainable AI
✅ Prompt Engineering
✅ Variational Autoencoders VAEs
✅ Generative Adversarial Networks GANs
✅ Transformers
✅ LLM Architecture
✅ Attention Mechanism
✅ Langchain for Workflow Design
✅ GenAI Application Development
✅ LLM Fine Tuning
✅ LLM Benchmarking
✅ Stable Diffusion
✅ Generative AI Governance

Tools Covered

✅ Python
✅ ChatGPT
✅ OpenAI
✅ Hugging Face
✅ Bard
✅ Dall-E-2
✅ LangChain
✅ Gradio
✅ Chroma
✅ StreamLit

Industry Projects
✅ Project 1
Craft an AI Powered HR Assistant

✅ Project 2
Build a Creative Logo Designer

✅ Project 3
Conversational Chatbot for Document Interaction

👉Learn more at: https://www.simplilearn.com/applied-ai-course?utm_campaign=Live-11Sep2024GenAI&utm_medium=Description&utm_source=youtube

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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 𝐰𝐢𝐭𝐡 𝐓𝐞𝐧𝐬𝐨𝐫𝐟𝐥𝐨𝐰 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧: https://www.edureka.co/ai-deep....-learning-with-tenso (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka "𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝" video will help you in understanding why we need Transformers and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using Transformers.
00:00:00 Agenda
00:01:28 Introduction to Deep Learning
00:08:24 What is Deep Learning
00:13:02 NLP using RNN
00:15:59 Scaling up NLP task
00:16:49 Transformers Walkthrough
00:19:51 Top Language Models

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This is the 1st video in a series on using large language models (LLMs) in practice. I introduce LLMs and three levels of working with them.

▶️ Series Playlist: https://www.youtube.com/playli....st?list=PLz-ep5RbHos
📰 Read more: https://towardsdatascience.com..../a-practical-introdu

Music by Me
https://soundcloud.com/shawhin....-talebi/letsalljustt
https://www.youtube.com/@shawhin-music

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Intro - 0:00
What is an LLM? - 1:13
Zero-shot Learning - 3:36
How do LLMs work? - 5:44
3 Levels of Using LLMs - 7:52
Level 1: Prompt Engineering - 8:22
Level 2: Model Fine-tuning - 11:00
Level 3: Build your own - 13:13

Are you confused about What are Large Language models (LLMs)?
Which LLM to use according to your problem statement?
Which LLM is affordable and Which LLM is Open Source?
What are Top LLMs in Market today 2023?

Then this video is your solution!, Join me on an exciting journey into the world of Large Language Models (LLMs) and their impact on Artificial Intelligence. In this video, we delve deep into the capabilities of cutting-edge LLMs, their applications in Generative AI, and how they are transforming the fields of Content Generation, Software Development, Data Science, and many more domains. Discover the power of language generation, natural language processing, and creative AI. Stay up-to-date with the latest advancements in AI and explore the endless possibilities with Large Language Models. Don't miss this opportunity to expand your knowledge and explore the limitless potential of AI-driven language technologies.

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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐂𝐨𝐮𝐫𝐬𝐞 - 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 𝐭𝐨 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝: https://www.edureka.co/openai-....chatgpt-training-cou
In this Edureka video on 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥 𝐟𝐨𝐫 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬, you will learn how to use ChatGPT for coding and programming using different languages. ChatGPT can become a very. useful tool for every developer.
00:00:00 ChatGPT for Developers
00:00:40 What is ChatGPT
00:01:37 How ChatGPT Works
00:02:16 ChatGPT vs Codex
00:03:16 How to use ChatGPT
00:03:51 ChatGPT for Programming
00:04:06 ChatGPT for Shell Scripting
00:08:45 ChatGPT for Python Programming
00:10:47 ChatGPT for HTML and CSS Coding
00:12:10 ChatGPT for JavaScript

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Dashboard in Excel - https://www.youtube.com/watch?v=QyNo__lufoU

This video by simplilearn in ChatGPT Made This Excel Dashboard help you in building an Interactive Sales Analysis Dashboard with Slicers. Welcome to our latest Excel tutorial! In this video, we'll guide you through the process of creating a dynamic and visually appealing sales analysis dashboard using Excel. Whether you're a marketing analyst, business professional, or anyone looking to elevate their data presentation skills, this tutorial is tailored just for you.

📊 What You'll Learn:

How to set up a pivot table for sales data.
Creating compelling charts to represent month-on-month sales, county-wise sales, region-wise sales, and more.
Adding slicers for easy and interactive data filtering.
Enhancing the dashboard's aesthetics and functionality.
🔍 Key Features:

Understand the importance of pivot tables for data summarization.
Learn the step-by-step process of creating various charts to analyze different aspects of sales data.
Implement slicers to allow effortless filtering and exploration of your data.
Tips for formatting and designing a professional-looking dashboard.
🛠️ Tools Used:

Microsoft Excel
📈 Who Should Watch:

Marketing Analysts
Business Professionals
Data Enthusiasts
Anyone wanting to master Excel for data analysis and visualization
📚 Resource Links:

Link to relevant marketing strategies
🕒 Timestamps:

00:00 Introduction
01:12 Setting up Pivot Tables
03:45 Creating Charts for Sales Analysis
06:20 Adding Slicers for Interactivity
08:45 Dashboard Formatting Tips

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➡️ About Post Graduate Program In Business Analysis
This Post Graduate Program in Business Analysis is for professionals looking to pursue a Business Analysis career, understand business analysis techniques, get hands-on experience, and for experienced analysts looking to learn the latest tools and frameworks used by Agile teams.

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- Certificate from Simplilearn in collaboration with Purdue University
- Become eligible to be part of the Purdue University Alumni Association
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- Earn 35 PDs/CDUs post completion of the CBAP® module
- Harvard Business Publishing case studies of Pearson, CarMax, EvCard, etc.
- Capstone from 3 domains and 14+ projects
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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
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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.

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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

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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

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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 deep learning course is designed to take you from beginner to proficient in deep learning. You will learn the fundamental concepts, architectures, and applications of deep learning in a clear and practical way. So get ready to build, train, and deploy models that can tackle real-world problems across various industries.

Course created by @AyushSinghSh

GitHub: https://github.com/ayush714/co....re-deep-learning-cou

⭐️ Contents ⭐️
0:00:00 Intro
0:03:07 Getting started
0:05:07 Vectors
0:21:51 Operation on vectors
0:38:52 Matrices
0:52:02 Operation on Matrices
0:52:27 Matrix Scalar Multiplication
0:55:47 Addition of Matrices
0:59:27 Properties of Matrix addition
1:03:07 Matrix Multiplication
1:08:02 Properties of Matrix Multiplication
1:18:32 Linear Combination Concept
1:36:20 Span
1:50:57 Linear Transformation
2:05:30 Transpose
2:14:02 Properties of Transpose
2:19:52 Dot Product
2:25:22 Geometric Meaning of Dot Product
2:34:32 Types of Matrices
3:04:22 Determinant
3:11:17 Geometric Meaning of Determinant
3:15:42 Calculating Determinant
3:23:37 Properties of Determinant
3:27:22 Rule of Sarus
3:48:42 Minor
3:56:49 Cofactor of a Matrix
4:00:42 Steps to calculate Cofactor of a Matrix
4:03:17 Adjoint of a Matrix
4:18:47 Trace of a Matrix
4:17:22 Properties of Trace
4:38:17 System of Equations
5:03:07 Example
5:17:42 Determinant
5:57:47 Single Variable Calculus
6:02:48 What is Calculus?
6:11:07 Ideas in Calculus
6:11:33 Differentiation
6:18:38 Integration
6:22:07 Precalculus Functions
6:43:52 Single Variable Calculus (Trigonometry Review)
6:45:02 Trigonometry functions
7:12:02 Unit Circle
7:24:32 Limit Concept
7:51:47 Definition of a limit
7:53:27 Continuity
8:00:17 Evaluating Limits
8:17:12 Sandwich Theorem
8:21:12 Differentiation
8:45:42 Differentiation as rate of Change
8:52:37 Differentiation in terms of Limit
9:04:51 Example
9:09:54 Important Differentiation Rules
9:53:12 Rule Chain Rule
10:17:27 What is Deep Learning
10:18:27 What is Machine Learning
10:36:37 Definition of Deep Learning
10:43:07 Applications
10:47:19 Introduction to Neural Networks
10:51:17 Artificial Neural Networks
11:08:31 The Perceptron
11:19:57 Linear Neural Network
11:21:32 Intuition Behind Activation function and Backpropagation Algorithm



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The recent interest in AI as meant a lot of people have been encountering new vocabulary. Martin Keen is to help you sort it out. This video runs through key terms like machine learning, deep learning, foundation models, and large language models and how they're related to each other.