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This Edureka video on 'SQL Triggers' will help you understand the concept of triggers in SQL. Below are the topics covered in this SQL Triggers For Beginners:
00:00:00 Introduction
00:00:21 Agenda
00:00:48 What is a Trigger
00:02:21 Syntax of Trigger
00:04:29 Nested Triggers
00:05:19 Operation in Trigger

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⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

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This video by Simplilearn on JavaScript Full Course is designed, developed, and curated in collaboration with industry experts to make sure to cover the major technical fundamentals, skills, tips, and tricks required to become an expert JavaScript Developer according to the current IT Standards. This JavaScript Tutorial For Beginners video will help you become proficient in JavaScript. At the end of the video, you will understand the concepts required to build complex JS applications.

JavaScript Full Course 2023 00:00:00
Introduction To JavaScript 00:03:03
Why Learn JavaScript 00:12:17
What is DOM 00:18:16
How To Install VS Code 00:23:46
Hello World Program In JavaScript 00:30:36
JavaScript Statements And Comments 00:42:33
Variables In JavaScript 00:48:23
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What is JavaScript:
JavaScript is a scripting language that is used to create and manage dynamic web pages, basically anything that moves on your screen without requiring you to refresh your browser. It can be anything from animated graphics to an automatically generated Facebook timeline. When most people get interested in web development, they start with good old HTML and CSS. From there, they move on to JavaScript, which makes sense because these three elements together form the backbone of web development.

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This program will advance your career as a MEAN stack developer. You’ll learn top skills such as MongoDB, Express.js, Angular, and Node.js (“MEAN”), plus GIT, HTML, CSS, and JavaScript to build and deploy interactive applications and services. This Full Stack MEAN Developer program provides complete knowledge of software development and testing technologies such as JavaScript, Node.js, Angular, Docker, and Protractor. You'll build an end-to-end application, test and deploy code, and store data using MongoDB.

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Career Masterclass: How to Get Job-Ready for a DevOps Career With Caltech CTME is a live and interactive webinar hosted by simplilearn in collaboration with real-time industry experts explaining the fundamentals of DevOps and skills required to make a career in DevOps. They will talk about the roles and responsibilities that come with a DevOps career and much more. The host will also cover the course details offered by simplilearn for a better learning experience.

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DevOps is how the future of software is created. The tools of DevOps - containerization, CI/CD pipelines, automated testing - are how sites and apps like Google, Facebook, and Amazon keep their functionality growing seamlessly.

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You can participate in the Q&A session with *expert instructor Ashutosh Bhakare* to ask your questions about DevOps. You’ll find out what makes this one of the most sought-after careers today.

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✅ A bachelor's degree with an average of 50% or higher marks
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This PRINCE2® Certification Training Video will give an overview of Simplilearn's PRINCE2® Certification course and will discuss about the PRINCE2® Certification exam outline and syllabus. This video will also explain all the PRINCE2® Principles and PRINCE2® themes in detail.

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4. Project Executives / Project Engineers
5. Software Developers
6. Any professional aspiring to be a Project Manager


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MIT Introduction to Deep Learning 6.S191: Lecture 3
Convolutional Neural Networks for Computer Vision
Lecturer: Alexander Amini
2023 Edition

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

Lecture Outline
0:00​ - Introduction
2:37​ - Amazing applications of vision
5:35 - What computers "see"
12:38- Learning visual features
17:51​ - Feature extraction and convolution
22:23 - The convolution operation
27:30​ - Convolution neural networks
34:29​ - Non-linearity and pooling
40:07 - End-to-end code example
41:23​ - Applications
43:18 - Object detection
51:36 - End-to-end self driving cars
54:08​ - Summary

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In this video, we're going to go over how to fine-tune a GPT-3 model.

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We'll start by creating a prompt, then we'll generate some synthetic data to use for training. Next, we'll augment that data to make it even more useful for training.

Finally, we'll prepare a JSON file for uploading to OpenAI GPT-3. Once our model is fine-tuned, we'll test it out to see how it performs.


00:00 Intro
00:26 Fine Tune GPT-3 Model Plan
03:07 Creating a prompt for fine-tuning GPT-3
08:22 Syntetic Data for fine-tuning GPT-3
13:13 Augmenting the Data for fine-tuning
15:46 Preparing a json file for fine-tuning
19:06 Uploading json file to OpenAI GPT-3
20:29 Testing our fine-tuned GPT-3 model
26:20 Recap of GPT-3 fine tuning

Become a GPT text generation expert with our comprehensive 3-part video series. Learn how to build, train, and test your own GPT model, and gain insights into GPT-2 architecture, tokenization, and training processes that will elevate your language model projects.

Discover the power of GPT transformers and learn how to build, train, and test your own text generator in this comprehensive 3-part video series. Gain practical experience and insights into GPT-2 architecture, tokenization, and training techniques.

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In this video " Top 20 Hadoop Interview Questions" we will cover the 20 most asked question in the hadoop and big data interviews.
Big Data in layman’s terms is a vast volume of high velocity data , think of hundreds of gigabytes of data flowing through in minutes. Obviously this can’t be stored and processed using traditional data processing systems. Now this is where Hadoop comes in. Hadoop is an open-source framework designed to handle big data. It provides a distributed file system (Hadoop Distributed File System - HDFS) and a distributed processing framework (MapReduce) that allows for parallel and scalable processing of large datasets across clusters of nodes. Hadoop's architecture enables fault tolerance and high availability, making it suitable for processing big data. Organizations and enterprises globally are on the lookout for skilled professionals who are able to handle such vast volumes of data, help structure and streamline the processing of this data. The salary of a Hadoop developer can vary based on factors like experience, location, company size, and industry but on average Hadoop developers earn anywhere between $80,000 and $150,000 per year which can go as high as 250,000$ for some experienced roles. It is therefore one of the best times for you to get yourself certified and skilled in the big data domain. With companies like Google, Adobe, Intuit,Oracle, Accenture, VISA, and many hiring hadoop developers you must get yourself enrolled in one of our amazing certification training programs to land yourself your dream job. Let me tell you about this global certification program.

00:00 Introduction to Big Data and Hadoop
Hadoop Developer Salary
Companies Hiring Hadoop Developer
Top 20 Hadoop Interview Questions

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When will we have AGI, or human-level AI? That's the question on everyone's minds, and in this video I gathered together predictions from various experts to try to come up with an answer.

The temptation as humans is to look at progress in the past when predicting progress in the future. But this assumes a linear technological progress, which is not the case. Technology advances exponentially. Creating predictions that take this exponential growth into account is very difficult, especially because each individual technology advances through an S-curve and eventually levels out, giving way to newer technologies.

Hence, a lot of predictions are going to be too high because they assume linear progress. The trick is to exclude the predictions that are too high while not jumping to the most optimistic predictions immediately. At the end of the video, I make my own prediction about the arrival of AGI.


Scaling up learning across many different robot types
https://deepmind.google/discov....er/blog/scaling-up-l

How Soon is Now? Predicting the Expected Arrival Date of AGI- Artificial General Intelligence
https://papers.ssrn.com/sol3/p....apers.cfm?abstract_i

"AGI within 18 months" explained with a boatload of papers and projects
https://www.youtube.com/watch?v=YXQ6OKSvzfc

Alan’s conservative countdown to AGI
https://lifearchitect.ai/agi/

7 AI-Experts Predicting Short Timelines… Is Life-Changing AI Months Away, Not Years?
https://www.linkedin.com/pulse..../7-ai-experts-predic

When will the first general AI system be devised, tested, and publicly announced?
https://www.metaculus.com/ques....tions/5121/date-of-a

Shane Legg (DeepMind Founder) - 2028 AGI, Superhuman Alignment, New Architectures
https://www.youtube.com/watch?v=Kc1atfJkiJU

Google AI Chief Says There’s a 50% Chance We’ll Hit AGI in Just 5 Years
https://futurism.com/google-deepmind-agi-5-years

OpenAI CEO: When will AGI arrive? | Sam Altman and Lex Fridman
https://www.youtube.com/watch?v=ZgN7ZYUxcXM

#agi #ai #singularity

0:00 Intro
0:23 Contents
0:28 Part 1: Definitions
0:58 AGI is about generality
1:29 Adversarial definitions of AGI
2:12 S-curves of technological progress
2:58 Hype cycle
3:22 Can LLMs take us all the way to AGI?
4:22 Part 2: Historical attitudes
4:44 LLMs changed people's predictions
5:03 Linear view of history
5:39 Example: one year before the singularity
6:03 Discounting linear predictions
6:40 Timeline: Metaculus average
7:05 Timeline: Oxford economic modeling
7:54 Wisdom of crowd says 2032, 2041...
8:19 Part 3: Timeline predictions
8:28 Geoffrey Hinton: godfather of AI (under 20 years)
9:03 Ray Kurzweil: futurist, studies advancement of technology (2029)
9:33 David Shapiro: YouTuber and researcher (2024)
10:12 Sam Altman: CEO of OpenAI (under 10 years)
10:53 Shane Legg: DeepMind founder (2025/2028)
11:23 Dario Amodei: CEO of Anthropic (2025-2026)
11:46 Dr Alan D. Thompson: countdown to AGI (2024-2025 maybe 2026)
12:44 Measuring stepping stones to AGI
13:15 Dr Waku prediction!
14:05 Conclusion
14:38 Outro

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В университете штата Мичиган под руководством Ричарда Ленски проходит самый продолжительный за всю историю эксперимент по изучению эволюции. Уже 35 лет 12 популяций кишечной палочки (E. coli) живут в комфортных и неизменных лабораторных условиях, но, вопреки ожиданиям даже самого профессора, продолжают эволюционировать и обретать новые полезные для выживания свойства. О том, как устроен эксперимент и чем примечательны его результаты, расскажет Дерек Маллер в новом видео Veritasium.

Перевод: Алексей Лоскутов
Редактура: Алексей Малов
Научная редактура: Гор Гиноян
Озвучка: Дмитрий Чепусов, Юрий Катарманов
Монтаж звука: Андрей Фокин
Монтаж видео: Джон Исмаилов
Обложка: Андрей Гавриков

#Veritasium #VertDider

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© https://www.youtube.com/c/veritasium/
Источник: https://youtu.be/w4sLAQvEH-M

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Visitamos la Armada de los Estados Unidos para probar los mejores anteojos de visión nocturna del mundo. Dirígete a https://brilliant.org/veritasium para comenzar tu prueba gratuita de 30 días, y las primeras 200 personas obtendrán un 20% de descuento en una suscripción premium anual.

Si estás buscando un kit de modelado molecular, visita https://ve42.co/SnatomsV para probar Snatoms, un kit que inventé donde los átomos se unen magnéticamente.

Un agradecimiento enorme al Dr. Ben Conley y a todo el equipo del Centro de Guerra Naval Surface, División Crane. Puedes conocer más sobre su trabajo aquí: https://www.navsea.navy.mil/Home/Warf...


▀▀▀
Referencias del video:
Tyson, J. (2001). Cómo funciona la visión nocturna. How Stuff Works. - https://ve42.co/HSWNightVision
Montoro, H. P. Intensificación de imágenes: la tecnología de la visión nocturna. Photonics. - https://ve42.co/PhotonicsTech
Brain Stuff - HowStuffWorks a través de YouTube - https://ve42.co/HSWVideo
Photonis a través de YouTube - https://ve42.co/Photonis
Operator Drewski a través de YouTube - https://ve42.co/NVGaming
Dispositivo de visión nocturna a través de Wikipedia - https://ve42.co/NVWiki
Intensificador de imagen a través de Wikipedia - https://ve42.co/Intensifier
Lodriguss, J. Cómo funcionan las cámaras digitales. AstroPix. - https://ve42.co/AstroPix
Kinka-Byo. (2019). ¿Cuál es la causa física del aumento de ruido a ISO alto? StackExchange. - https://ve42.co/PhotoISO
Macias, A. (2015). Las NVG secretas que usó el SEAL Team Six en la incursión contra Bin Laden. Business Insider. - https://ve42.co/BINightVision
Van Dommelen, L. Emisión termoiónica. Mecánica cuántica para ingenieros. - https://ve42.co/Thermoionic
Nave, R. (2016). Radiación de cuerpo negro. HyperPhysics. - https://ve42.co/BlackBody
Terminología de visión nocturna - Generaciones. ModArmory. - https://ve42.co/ModArmory
NASA (2018). Detectores de rayos X - Detección de corriente eléctrica. - https://ve42.co/NASAXRay

Imágenes y videos:
Informe de Osama Bin Laden, a través de CNN en YouTube - https://ve42.co/CNNOBL
Escena de incursión nocturna de Sicario, a través de Movieclips en YouTube - https://ve42.co/Sicario
Metraje de Splinter Cell, a través de Ubisoft en YouTube - https://ve42.co/SplinterCell
Escena de visión nocturna de Step Brothers, a través de masusockvevo en YouTube - https://ve42.co/StepBrothers
Informe de Ucrania, a través de CNN en YouTube - https://ve42.co/CNNUkraine
Metraje de la cámara de imagen térmica FLIR K2, a través de Teledyne FLIR en YouTube - https://ve42.co/FLIRK2
Imágenes del Observatorio de rayos X Chandra, a través de la NASA - https://ve42.co/NASAChandra

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Adam Foreman, Anton Ragin, Balkrishna Heroor, Bernard McGee, Bill Linder, Burt Humburg, Chris Harper, Dave Kircher, Diffbot, Evgeny Skvortsov, Gnare, John H. Austin, Jr., John Kiehl, Josh Hibschman, Juan Benet, KeyWestr, Lee Redden, Marinus Kuivenhoven, Mario Bottion, Max Maladino, Meekay, Meg Noah, Michael Krugman, Paul Peijzel, Richard Sundvall, Sam Lutfi, Stephen Wilcox, Tj Steyn, TTST, Ubiquity Ventures

▀▀▀
Dirigido por Emily Zhang
Escrito por Emily Zhang y Derek Muller
Editado por Trenton Oliver
Animado por Mike Radjabov, Fabio Albertelli e Ivy Tello
Filmado por Trenton Oliver, Emily Zhang, Derek Muller y Raquel Nuno
Producido por Emily Zhang y Derek Muller

Miniatura por Ren Hurley
Video/fotos adicionales proporcionados por Getty Images y Pond5
Música de Epidemic Sound

Video en Inglés del Canal @Veritasium: https://youtu.be/UAeJHAFjwPM

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

🕹 Este canal de Youtube es administrado por: https://www.unilingo.co/
❓Comentarios o sugerencias de traducción: [email protected]

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#veritasiumenespañol

Бозон Хиггса просто прекрасен, однако большая часть вашей массы от него не зависит. Механизм Хиггса объясняет наличие массы у всех вещей, не так ли? На самом деле нет, он применим только для элементарных частиц: электроны получают всю свою массу благодаря взаимодействию Хиггса, однако протоны и нейтроны, состоящие из кварков, — нет.

На самом деле масса кварков настолько мала, что она составляет всего лишь один процент массы протона и нейтрона. Вся остальная масса обусловлена энергией глюоного поля. Глюоны не имеют массы, однако в их поле столько энергии, что, из-за того, что Е=мс2, в этом поле находится значительное количество массы. Именно это объясняет наличие массы у вас и фактически у всего вокруг.

Перевод: Варя Феофанова
Редактура: Алексей Малов
Озвучка: Виктор Сандаков, Дмитрий Чепусов
Монтаж: Александр Штанг, Юрий Хомяков
Обложка: Мирана Эрнестовна

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© https://www.youtube.com/user/1veritasium

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A combination of Prompt Engineering and ChatGPT to learn the internals and technology behind large language models powering the new era of Artificial Intelligence.

A List of all prompts and keywords use in this video:
- https://github.com/prodramp/De....epWorks/blob/main/Ch

== Video Timeline ==
(00:00) Content Intro
(01:30) ChatGPT
(02:43) Transformer architecture
(09:10) Keywords Generation
(13:38) Text embedding
(17:43) Encoder and Decoder
(18:32) Self attention
(18:45) Multi-head self attention
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(29:49) LLM Parameter Count (Python Code)
(31:38) DALL-E large language model
(33:17) Key Differences DALL-E & ChatGPT
(34:20) List all Prompts
(34:40) ChatGPT Session Summary

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Tags:
#llm #chatgpt #promptengineering #openai #python #artificialintelligence

LLMs or Large Language Models are everywhere these days. But how do they work exactly? In this video, a deep-fake version of Ryan Gosling (created with ElevenLabs and SyncLabs) explains the high-level workings of an LLM like ChatGPT.

🔵TOPICS 🔵
0:42 What are Large Language Models (a.k.a. LLMs)
1:10 Well known commercial and open source examples of LLMs
1:41 The inner workings of an LLM
3:00 What embeddings are
6:34 LLMs compared to how our live is generated
7:43 What the acronym GPT stands for

🔵ATTRIBUTION 🔵
Ground Truth Video Used to Create this video
KEN Things Ryan Gosling Can't Live Without | GQ
https://youtu.be/aNJ_JPkbH2M?si=rPGZEJndw-JP_lMw

🔵FAIR USE 🔵
This video is for educational purposes and is in line with the guidelines for Fair Use
https://support.google.com/youtube/answer/9783148?hl=en&ref_topic=2778546

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Learn how to build AI agents from scratch in this beginner-friendly tutorial!

Jan breaks down the complex world of AI agents into simple, understandable components and shows you how to create a fun "Santa's Helper" agent that researches toys and sends wish lists - all without writing a single line of code.

What you'll learn:
▶︎ Understanding AI agent components and architecture
▶︎ Setting up large language models (LLMs)
▶︎ Implementing agent memory systems
▶︎ Writing effective agent system prompts
▶︎ Connecting research and web search tools
▶︎ Automating email communications
▶︎ Testing and debugging agents
▶︎ Real-world business applications

⏰ TIMESTAMPS
00:00 What are AI agents and how do they work
00:30 AI agent components and architecture explained
02:31 Understanding agent tools and capabilities
05:19 Building your first agent in n8n
06:30 Connecting language models to your agent
07:37 Why memory is crucial for AI agents
09:35 Creating effective system prompts for agents
12:08 Adding web search capabilities to agents
14:38 Setting up email automation tools
15:26 Testing and debugging your AI agent
18:22 Business use cases and advanced applications

📚 RESOURCES MENTIONED
https://www.youtube.com/watch?v=SzPE_AE0eEo
https://www.youtube.com/watch?v=6BRyynZkvf0
https://www.youtube.com/watch?v=vgncj7MJbVU
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In this video, I dive into the world of agentic AI, a concept that's set to be a major buzzword in 2025. We explore how agentic AI combines multiple agents—some of which are language models (LLMs) and others that are tools—to create more sophisticated and effective AI workflows. We walk through a simple example of how an AI agent can draft a marketing plan, another can critique it, and a third can refine it based on the feedback. We also discuss how some agents can be tools, like a Google search to gather data, and how an orchestrator agent can manage the entire process, adjusting steps as needed.

Leave me a comment if you have new topics I should be discussing.

Check out my LinkedIn: https://www.linkedin.com/in/donwoodlock/

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YouTube Timestamps
0:00 – 0:20 Welcome
0:21 – 0:50 Introduction to the concept of Agentic AI
0:51 – 3:50 Explanation of how Agentic AI works
3:50 – 4:08 Advertisement plug-in
4:08 – 5:20 Example of using compound LLM’s
5:21 – 5:55 Why you should use a compound LLM approach
5:57 – 6:45 Best way to train and use LLM’s for optimal outcome
6:45 – 7:40 How to think of LLM as agents
7:41 – 10:05 Not every agent needs to be an LLM
10:06 - 10:50 Possibility of having an orchestrator agent
10:51 – 12:50 How to use these agents
12:51 – 13:26 Closing remarks

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ABOUT INTERSYSTEMS
Established in 1978, InterSystems Corporation is the leading provider of data technology for extremely critical data in healthcare, finance, and logistics. It’s cloud-first data platforms solve interoperability, speed, and scalability problems for large organizations around the globe. InterSystems Corporation is ranked by Gartner, KLAS, Forrester and other industry analysts as the global leader in Data Access and Interoperability. InterSystems is the global market leader in Healthcare and Financial Services.

Website: https://www.intersystems.com/
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#agenticai #aiagents #LLM