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In this What Are Transformers In Artificial Intelligence tutorial, we'll delve into the world of Transformers in AI. We'll unravel the intricate workings of these powerful models, from their foundational architecture to their applications in natural language processing, computer vision, and beyond. By the end, you'll grasp not only how Transformers operate but also why they've become a cornerstone in modern AI research and development. So, buckle up as we embark on this enlightening exploration through the realm of Transformers in AI.

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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.

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Correction: At 30:42 I write "X = Y". They're not equal, what I meant to say is "X and Y are identically distributed".

The variance is a measure of how spread out a distribution is. In order to estimate the variance, one takes a sample of n points from the distribution, and calculate the average square deviation from the mean.
However, this doesn't give a good estimate of the variance of the distribution. The best estimate, however, is obtained when dividing by n-1 instead of n.
WHY!?!?!?!?!?!?!?
In this video, we dig deeper into why the variance calculation should be divided by n-1 instead of by n. For this, we use an alternate definition of the variance, which doesn't use the mean in its calculation.

*[0:00] Introduction and Bessel's Correction*
- Introducing Bessel's Correction and why we divide by \( n-1 \) instead of \( n \) to estimate variance.

*[0:12] Introduction to Variance Calculation*
- Explaining the premise of calculating variance and introducing the concept of estimating variance using a sample instead of the entire population.

*[1:01] Definition of Variance*
- Defining variance as a measure of how much values deviate from the mean and outlining the basic steps of variance calculation.

*[1:52] Introduction to Bessel's Correction*
- Discussing why we divide by \( n-1 \) when calculating variance and introducing Bessel's Correction.

*[2:35] Challenges of Bessel's Correction*
- Sharing personal challenges in understanding the rationale behind Bessel's Correction and discussing my research process on the topic.

*[3:20] Alternative Definition of Variance*
- Presenting an alternative definition of variance to aid in understanding Bessel's Correction and expressing curiosity about its presence in the literature.

*[4:45] Quick Recap of Mean and Variance*
- Briefly revisiting the concepts of mean and variance, demonstrating how they are calculated with examples, and explaining how variance reflects different distributions.

*[7:05] Sample Mean and Variance Estimation*
- Explaining the challenges of estimating the mean and variance of a distribution using a sample and discussing why sample variance is not a good estimate.

*[8:49] Bessel's Correction and Why \( n-1 \) is Used*
- Explaining how Bessel's Correction provides a better estimate of variance and why we divide by \( n-1 \) instead of \( n \). Emphasizing the importance of making a correct variance estimate.

*[10:51] Why Better Estimation Matters?*
- Discussing why the original estimate is poor and why making a better estimate is crucial. Explaining the significance of sample mean as a good estimate.

*[13:02] Issues with Variance Estimation*
- Illustrating the problems with variance estimation and demonstrating with examples why using the correct mean is essential for accurate estimates. Explaining the accuracy of estimates made using \( n-1 \).

*[15:04] Introduction to Correcting the Estimate*
- Discussing the underestimated variance and the need for correction in estimation.

*[15:57] Adjusting the Variance Formula*
- Explaining the adjustment in the variance formula by changing the denominator from \( n \) to \( n - 1 \).

*[16:22] Calculation Illustration*
- Demonstrating the calculation process of variance with the adjusted formula using examples.

*[16:57] Better Estimate with Bessel's Correction*
- Discussing how the corrected estimate provides a more accurate variance estimation.

*[18:24] New Method for Variance Calculation*
- Introducing a new method for calculating variance without explicitly calculating the mean.

*[20:06] Understanding the Relation between Variance and Variance*
- Explaining the relationship between variance and variance, and how they are related mathematically.

*[21:52] Demonstrating a Bad Calculation*
- Illustrating a flawed method for calculating variance and explaining the need for correction.

*[23:37] The Role of Bessel's Correction*
- Explaining why removing unnecessary zeros in variance calculation leads to better estimates, equivalent to Bessel's Correction.

*[25:08] Summary of Estimation Methods*
- Summarizing the difference between the flawed and corrected estimation methods for variance.

*[26:02] Importance of Bessel's Correction*
- Emphasizing the significance of Bessel's Correction for accurate variance estimation, especially with smaller sample sizes.

*[30:19] Mathematical Proof of Variance Relationship*
- Providing two proofs of the relationship between variance and variance, highlighting their equivalence.

*[35:24] Acknowledgments and Conclusion*

Thanks @mkan543 for the summary!

** Machine Learning Training with Python: https://www.edureka.co/data-sc....ience-python-certifi **
This "Top 10 Applications of Machine Learning in 2021" video will give you an idea of how vast the machine learning is and how commonly you are using it in your day to day life.
Topics covered in this session:
00:00 Introduction
00:33 Google Maps
02:01 Facebook
03:38 UberEats
05:15 Amazon
06:16 iPhone
07:52 Tesla
09:49 Netflix
10:32 Moley
11:43 Google Translate
12:43 Kuka

Check out our playlist for more videos: http://bit.ly/2taym8X

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How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
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 be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate!


- - - - - - - - - - - - - - - - -
About the Course

Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide 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 python programming language. Furthermore, 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 the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.

After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present

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

Why learn Machine Learning with Python?

Data Science is a set of techniques that enable the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.

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MIT 18.200 Principles of Discrete Applied Mathematics, Spring 2024
Instructor: Ankur Moitra
View the complete course: https://ocw.mit.edu/courses/18....-200-principles-of-d
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

We first describe the mechanics of the course. We then discuss the pigeonhole principle, explaining what it is and giving several surprising applications of it. The we briefly discuss foundations of probability and sample spaces.

License: Creative Commons BY-NC-SA
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We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.

In this tutorial, we will be building a Node.js API with MongoDB and Mongoose to track exercises. Our API will pass a series of tests, including creating a new user with a POST request to /api/users, getting a list of all users with a GET request to /api/users, adding exercises with a POST request to /api/users/:_id/exercises, and retrieving a full exercise log of any user with a GET request to /api/users/:_id/logs. Thanks for watching and have a great day!

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In this video on "Chat GPT for Programming", we will get started with programming using ChatGPT for absolute beginners. this video on ChatGPT for beginners will show you the chatbot's capabilities, which can range from generating code blocks out of thin air to completing and reviewing full-fledge programs. You can also learn how ChatGPT functions using a natural language, removing the need for learning any complex syntax.
If you are new to programming and want to know exactly how to get started with programming using ChatGPT, today's video is the perfect choice for you.

00:00 Chat GPT for Programming
01:36 How Does ChatGPT Works?
02:24 Why Do Programmers Use ChatGPT?
02:36 Code Generation
03:47 Code Completion
05:59 Code Review
07:13 Natural Language Interface

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#ChatGPTForProgramming #ProgrammingUsingChatGPT #ProgramWithChatGPT #CodingUsingChatGPT #ChatGPTForBeginners #ChatGPT #Chat_GPT #ChatGPTTutorial #OpenAI #AI #Simplilearn

ChatGPT generates responses to the text input using a method called transformer architecture. A large volume of text is fed into chatGpt from various sources, including books, websites, and other social media platforms. The model then uses this information to forecast the following word in a phrase based on the words that came before it.

Programming Using ChatGPT video helps Programmers to code efficiently, accurately, and in less time with minimum errors. The chatGPT system allows users to enter text or queries, and then the system uses its training data and algorithms to produce the right answer.

ChatGPT assists programmers by offering programming-related answers and solutions and helping them improve their skills. Besides that, ChatGPT is utilized for code generation, code completion, code review, and a natural language interface.

- Code generation: ChatGPT is trained to generate the code or even the entire program described in the natural language specified by what they want a program to do, and then the ChatGPT could generate the relevant code.

- Code completion: ChatGPT is trained to generate snippets of code or even full-fledged programs.

- Code review: ChatGPT can analyze code, identify the bugs or errors in the program, and further help resolve them.

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➡️ Artificial Intelligence Engineer Masters Program
Accelerate your career with this Master's in Artificial Intelligence in conjunction with IBM. Industry-relevant courses, including Data Science with Python, Machine Learning, Deep Learning, and NLP, feature unique hackathons and Ask Me Anything sessions hosted by IBM. Get job-ready in the AI domain with Capstone projects, practical labs, live sessions, and hands-on projects.

✅ Skills Covered
- Learn Artificial Intelligence's concept, purpose, domain breadth, phases, implementations, and impacts.
- Create real-world projects, games, prediction models, logic constraint satisfaction concerns, experience and understanding systems, probabilistic models, and agent decision-making skills using your artificial intelligent machines and models.
- Learn basic programming features and technicalities, such as data types, tuples, lists, arrays, basic operators, and functions.
- Learn about data wrangling, data exploration, data visualization, hypothesis creation, and testing procedures in Data Science.
- Use the NumPy and SciPy packages for high-level mathematical and technical computing and the Pandas package for data analysis.
- More

👉Learn More at: https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=23March2023ChatGPTforProgramming&utm_medium=Description&utm_source=youtube

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🔥Intellipaat Tableau training: https://intellipaat.com/tableau-training/
This tableau training for beginners video is a complete tableau tutorial where you will learn what is tableau, how to install tableau desktop, what is tableau architecture, how dimensions & measures works in tableau, the various file types in tableau, how data joining & data blending works in tableau, how to use filters in tableau and how to work with charts and graphs in tableau in detail.
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📝Following topics are covered in this tableau tutorial:
0:00 - Tableau Training
01:13 - Tableau Products
10:05 - Connecting tableau with data source
16:45 - Data Sorting and formatting (Meta data management)
30:20 - Different ways of connecting tableau with data source
37:05 - Data Blending
53:25 - What are dimensions and measures in tableau
01:28:45 - A note on File types
01:48:45 - Data Join and data blending
03:00:30 - How to create a hierarchy in tableau
03:10:55 - Learn about mark chards in tableau
03:16:12 - How do u create the an area mark type chart
03:28:20 - Types of filters in tableau
03:45:15 - Tableau calculations and expressions
04:35:40 - Level of Detail (LOD), LOD expression
04:41:38 - Include LOD Expression
05:07:10 - Exclude LOD Expression
06:06:30 - Difference between calculated fields and table calculations.
06:10:25 - Tableau Charts and graphs
07:19:43 - Creating the date parameter to dynamically change the date granularity view.
07:33:15 - Creating a Quantity bin using parameter
07:37:22 - Finding no. of P's in the name of the customers
07:45:30 - Calculating the rank and sort the region and sub category based on sales
07:48:00 - Which country has the highest profit ratio?

🔗 Watch Tableau video tutorials here: https://goo.gl/Gs7v5w

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Are you looking for something more? Enroll in our Tableau certification training and become a certified Data Visualization professional (https://intellipaat.com/tableau-training/). It is a 30 hrs instructor led Tableau developer and tableau server training provided by Intellipaat which is completely aligned with industry standards and certification bodies.

If you’ve enjoyed this tableau training video, Like us and Subscribe to our channel for more similar Tableau videos and free tutorials.
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If you want to learn Tableau to become a Data Visualization expert then this Intellipaat Tableau tutorial is for you. The Intellipaat video is your first step to learn Data Analysis. Since this Tableau tutorial and examples video can be taken by anybody, so if you are a beginner in technology then you can also watch other Tableau tutorial and enroll for Intellipaat Tableau training to take your skills to the next level.

Why Tableau is important?

Tableau is taking over each and every other Data Visualization tools because of its simplicity and rich functionality in data analysis. Reporting, graphs, organizing data, data blending, integrating with R and Hadoop are the most important aspects of Tableau that are being used everywhere from. Taking the Intellipaat Tableau training can help professionals to build a solid career in a rising technology domain and get the best jobs in top organizations.

Why should you opt for a Data Visualization career?

If you want to fast-track your career then you should strongly consider Tableau training. The reason for this is that it is one of the fastest growing BI tool. There is a huge demand for professionals in Tableau. The salaries for Data analysis Professionals is fantastic.There is a huge growth opportunity in this domain as well. Hence this Intellipaat Tableau tutorial is your stepping stone to a successful career!
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🔥Edureka PGP in AI & ML: https://www.edureka.co/post-gr....aduate/machine-learn
This Edureka video on What is Text Classification in Machine Learning gives you a brief overview of text classification. In this quick guide, the following topics will be covered:

1) What is Text Classification?
2) Use Case of Text Classification

Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s

#PythonEdureka #Edureka #textclassification #pythontextclassification #pythonprojects #pythonprogramming #pythontutorial #PythonTraining
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Become an expert in the exciting new world of AI & Machine Learning, get trained in cutting edge technologies and work on real-life industry-grade projects.
----------------------------------------------

Why Machine Learning & AI?

Because of the increasing need for intelligent and accurate decision making, there is an exponential growth in the adoption of AI and ML technologies. Hence these are poised to remain the most important technologies in the years to come.

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

PG Program in Machine Learning & AI

1. Ranked 4th among NITs by NIRF
2. Ranked among Top 50 Institutes in India
3. Designated as Institute of National Importance

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

Program Features

1. Mentorship from NITW faculty
2. Placement Assistance
3. Alumni Status
4. Industry Networking

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

Industry Projects

1. Building a Conversational ChatBot
2. Predictive Model for Auto Insurance
3. E-commerce Website - Sales Prediction

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

Mentors & Instructors

Dr. RBV Subramaanyam
Professor NITW

Dr. DVLN Somayajulu
Professor NITW

Dr. P. Radha Krishna
Professor NITW

Dr. V. Ravindranath
Professor JNTU Kakinada

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

Is this program for me?

If you’re passionate about AI & ML and want to pursue a career in this field, this program is for you. Whether you’re a fresher or a professional, this program is designed to equip you with the skills you need to rise to the top in a career in AI & ML.

Is there any eligibility criteria for this program?

A potential candidate must have one of the following prerequisites: Degrees like BCA, MCA, and B.Tech or Programming experience Should have studied PCM in 10+2

Will I get any certificate at the end of the course?

Yes, you will receive a Post-Graduate industry-recognized certificate from E & ICT Academy, NIT Warangal upon successful completion of the course.

For more information, please write back to us at sales@edureka.in or call us at IND: +91-9606058418 / US: 18338555775 (toll-free).

In this episode I break down my favorite song by a band that everyone hates.

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

November 11, 2025
This lecture covers agents, prompts, and RAG.

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/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

NOTE: There was no class on November 4, 2025 (Lecture 7). The previous lecture is Lecture 6.

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