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In this ChatGPT Course for beginners, we'll cover ChatGPT, GPT-4, and how ChatGPT works. We'll also explore its applications in various domains, including SEO and digital marketing, and share valuable tips and tricks.

00:00 ChatGPT Course For Beginners 2024
01:15 GPT 4
07:17 Automate Excel using ChatGPT
01:17:45 How To Use ChatGPT for SEO
02:09:20 ChatGPT For Content Creation
02:14:04 Master SEO Content with ChatGPT
02:25:11 ChatGPT For Marketing
02:33:58 How To Use ChatGPT to Build Website
03:17:00 ChatGPT For Programming
03:26:02 Can ChatGPT Solve LeetCode Problems?
04:54:23 How To Integrate ChatGPT With Whatsapp
05:32:19 Build a Game using Python and ChatGPT
05:47:32 ChatGPT in Cybersecurity

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Existe toda una rama de las matemáticas dedicada simplemente a los nudos… y ha cambiado el mundo. Te ayudaremos, puedes ir 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.

▀▀▀
Muchas gracias al Prof. Colin Adams por su excelente ayuda al guiarnos a través del mundo de los nudos.
Muchas gracias al Prof. Doug Smith, Dorian Raymer, el Prof. David Leigh y la Prof. Dorothy Buck por ayudarnos a comprender las aplicaciones de la teoría de nudos.
Muchas gracias al Prof. Dan Silver y al Prof. Jim Hoste por hablar con nosotros sobre la historia y la tabulación de nudos.

Si quieres aprender más sobre los nudos y jugar con ellos tú mismo, consulta:
La increíble herramienta KnotPlot: https://knotplot.com/. ¡Gracias a Rob Scharein por brindarnos ayuda técnica también!
Una tabla de nudos y todas sus invariantes: https://knotinfo.math.indiana.edu/
Knot Atlas para obtener información general sobre nudos: http://katlas.org/wiki/Main_Page

▀▀▀
Referencias en vídeo de la teoría de nudos: https://ve42.co/KnotTheoryRefs

Imágenes y vídeo:
Alexander cortando el nudo gordiano por Donato Creti vía Fine Art America - https://ve42.co/GordianCut
Tablet de Indus Valley a través de Quora - https://ve42.co/IndusValley
Páginas del Libro de Kells a través del National Trust of Scotland: https://ve42.co/BookOfKells
Diseños celtas medievales de @thebookofkellsofficial a través de Instagram - https://ve42.co/KellsInsta
Nudos chinos de YWang9174 a través de Wikimedia Commons - https://ve42.co/Panchang
Cables Quipu de Pi3.124 vía Wikimedia Commons - https://ve42.co/Quipu
Heráldica de Borromeo a través de Terre Borromeo - https://ve42.co/Borromeo
Carta de Birman/Jones vía Celebratio Mathematica - https://ve42.co/JonesBirman
Nudo de trébol molecular de piedra M a través de Wikimedia Commons - https://ve42.co/TrefoilMolecule
Estructura de rayos X del nudo trébol por Ll0103 a través de Wikimedia Commons - https://ve42.co/XrayTrefoil
Animación de bacterias de Your Body's Molecular Machines por Drew Berry a través del Instituto de Investigación Médica Walter y Eliza Hall - http://wehi.tv
Topoisomerasa y nudos de Orlandini et al. Sinergia de la topoisomerasa. PNAS, vol. 116, núm. 17, 2019, págs. 8149–8154. – https://ve42.co/Orlandini2019
KnotProt 2.0: una base de datos de proteínas con nudos y nudos corredizos - https://ve42.co/Knotprot

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Un agradecimiento especial a nuestros seguidores de Patreon:
Anton Ragin, Balkrishna Heroor, Bernard McGee, Bill Linder, Burt Humburg, Dave Kircher, Diffbot, Evgeny Skvortsov, Gnare, Jesse Brandsoy, John H. Austin, Jr., john kiehl, Josh Hibschman, Juan Benet, KeyWestr, Lee Redden, Marinus Kuivenhoven, Mario Bottion, MaxPal, Meekay, meg noah, Michael Krugman, Orlando Bassotto, 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 Fabio Albertelli, Ivy Tello, Jakub Misiek y Mike Radjabov.
Filmado por Derek Muller, Raquel Nuno y Emily Zhang
Producido por Emily Zhang y Derek Muller

Miniatura de Ignat Berbeci y Mike Radjabov
Vídeo/fotos adicionales proporcionadas por Getty Images y Pond5
Música de Epidemic Sound

____


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In this video, let's learn about a very important CI/CD tool, i.e., Jenkins. This video demonstrates the installation of Jenkins in Windows, building a CI/CD pipeline in Jenkins using GitHub, and configuring email notifications. This video is the perfect starting point to understand the importance of Jenkins. Join us as we unravel the functionalities of Jenkins!

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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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This is a 1 hour general-audience introduction to Large Language Models: the core technical component behind systems like ChatGPT, Claude, and Bard. What they are, where they are headed, comparisons and analogies to present-day operating systems, and some of the security-related challenges of this new computing paradigm.
As of November 2023 (this field moves fast!).

Context: This video is based on the slides of a talk I gave recently at the AI Security Summit. The talk was not recorded but a lot of people came to me after and told me they liked it. Seeing as I had already put in one long weekend of work to make the slides, I decided to just tune them a bit, record this round 2 of the talk and upload it here on YouTube. Pardon the random background, that's my hotel room during the thanksgiving break.

- Slides as PDF: https://drive.google.com/file/....d/1pxx_ZI7O-Nwl7ZLNk (42MB)
- Slides. as Keynote: https://drive.google.com/file/....d/1FPUpFMiCkMRKPFjhi (140MB)

Few things I wish I said (I'll add items here as they come up):
- The dreams and hallucinations do not get fixed with finetuning. Finetuning just "directs" the dreams into "helpful assistant dreams". Always be careful with what LLMs tell you, especially if they are telling you something from memory alone. That said, similar to a human, if the LLM used browsing or retrieval and the answer made its way into the "working memory" of its context window, you can trust the LLM a bit more to process that information into the final answer. But TLDR right now, do not trust what LLMs say or do. For example, in the tools section, I'd always recommend double-checking the math/code the LLM did.
- How does the LLM use a tool like the browser? It emits special words, e.g. |BROWSER|. When the code "above" that is inferencing the LLM detects these words it captures the output that follows, sends it off to a tool, comes back with the result and continues the generation. How does the LLM know to emit these special words? Finetuning datasets teach it how and when to browse, by example. And/or the instructions for tool use can also be automatically placed in the context window (in the “system message”).
- You might also enjoy my 2015 blog post "Unreasonable Effectiveness of Recurrent Neural Networks". The way we obtain base models today is pretty much identical on a high level, except the RNN is swapped for a Transformer. http://karpathy.github.io/2015..../05/21/rnn-effective
- What is in the run.c file? A bit more full-featured 1000-line version hre: https://github.com/karpathy/ll....ama2.c/blob/master/r

Chapters:
Part 1: LLMs
00:00:00 Intro: Large Language Model (LLM) talk
00:00:20 LLM Inference
00:04:17 LLM Training
00:08:58 LLM dreams
00:11:22 How do they work?
00:14:14 Finetuning into an Assistant
00:17:52 Summary so far
00:21:05 Appendix: Comparisons, Labeling docs, RLHF, Synthetic data, Leaderboard
Part 2: Future of LLMs
00:25:43 LLM Scaling Laws
00:27:43 Tool Use (Browser, Calculator, Interpreter, DALL-E)
00:33:32 Multimodality (Vision, Audio)
00:35:00 Thinking, System 1/2
00:38:02 Self-improvement, LLM AlphaGo
00:40:45 LLM Customization, GPTs store
00:42:15 LLM OS
Part 3: LLM Security
00:45:43 LLM Security Intro
00:46:14 Jailbreaks
00:51:30 Prompt Injection
00:56:23 Data poisoning
00:58:37 LLM Security conclusions
End
00:59:23 Outro

Direct Preference Optimization (DPO) is a method used for training Large Language Models (LLMs). DPO is a direct way to train the LLM without the need for reinforcement learning, which makes it more effective and more efficient.
Learn about it in this simple video!

This is the third one in a series of 4 videos dedicated to the reinforcement learning methods used for training LLMs.

Full Playlist: https://www.youtube.com/playli....st?list=PLs8w1Cdi-zv

Video 0 (Optional): Introduction to deep reinforcement learning https://www.youtube.com/watch?v=SgC6AZss478
Video 1: Proximal Policy Optimization https://www.youtube.com/watch?v=TjHH_--7l8g
Video 2: Reinforcement Learning with Human Feedback https://www.youtube.com/watch?v=Z_JUqJBpVOk
Video 3 (This one!): Deterministic Policy Optimization

00:00 Introduction
01:08 RLHF vs DPO
07:19 The Bradley-Terry Model
11:25 KL Divergence
16:32 The Loss Function
14:36 Conclusion

Get the Grokking Machine Learning book!
https://manning.com/books/grok....king-machine-learnin
Discount code (40%): serranoyt
(Use the discount code on checkout)

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

November 18, 2025
This lecture covers career advice and a guest speaker.

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/

View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

Guest Speaker
Laurence Moroney
Best-selling AI author and award-winning researcher

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

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🔥Edureka Machine Learning Engineer Masters Program: https://www.edureka.co/masters....-program/machine-lea
This Edureka video on Machine Learning Python is a part of the Python Tutorial Series which will give you an introduction to Machine Learning. Python is a powerful language and is a widely used high-level language for general-purpose programming. With features like open-sourced, clear syntax, object-oriented, interpreted, and interactive programming language Python has become the number one choice when it comes to Machine learning. This Python Tutorial on Machine Learning Python also talks about how to implement machine learning algorithms in Python along with the topics like:
00:00 Introduction to Python Tutorial
00:54 Why Machine Learning with Python?
02:39 What is Machine Learning?
05:09 Types of Machine Learning
06:25 Supervised Learning
07:28 KNN algorithm
18:10 Unsupervised Learning
19:03 K-means Clustering Algorithm

Python Tutorial Playlist: https://goo.gl/Na1p9G
Python Tutorial Blog Series: https://goo.gl/wd28Zr

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#edureka #pythonEdureka #machinelearningPython #Python #PythonTutorial #PythonMachineLearning #PythonTraining #machinelearningTutorial #machinelearning
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About the ML Master's Program

Edureka’s Machine Learning Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.

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

Why Learn Machine Learning?

Machine Learning is a concept that allows the machine to learn from examples and experience, and that too without being explicitly programmed. So instead of you writing the code, what you do is you feed data to the generic algorithm, and the algorithm/machine builds the logic based on the given data.
-------------------------------------

Edureka's Machine Learning Masters Program Curriculum

🟠 Live Courses 🟠
Python Programming Certification Course: https://www.edureka.co/python-....programming-certific
Machine Learning Certification Training using Python: https://www.edureka.co/machine....-learning-certificat
Graphical Models Certification Training: https://www.edureka.co/graphical-modelling-course
Reinforcement Learning: https://www.edureka.co/reinfor....cement-learning-cour
Natural Language Processing with Python Certification: https://www.edureka.co/python-....natural-language-pro
Deep Learning with TensorFlow 2.0 Certification Training: https://www.edureka.co/ai-deep....-learning-with-tenso
Python Spark Certification Training using PySpark: https://www.edureka.co/pyspark....-certification-train

🟠 Elective Course 🟠
Python Scripting Certification Training: https://www.edureka.co/python-scripting

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Prerequisites

There are no prerequisites for enrollment to the Masters Program. However, as a goodwill gesture, Edureka offers a complimentary self-paced course in your LMS on SQL Essentials to brush up on your SQL Skills. This program is designed and developed for an aspirant planning to build a career in Machine Learning or an experienced professional working in the IT industry.

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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

🔥Become Data Analytics Expert Today: https://taplink.cc/simplilearn_data_analytics

Data analytics has been rapidly growing, with companies looking to generate insights and drive their business with the help of data. In this Data Analytics Full Course video, you will learn what data analytics is, why data analytics is necessary, the types of data analytics, and the various data analytics applications. You will then understand a case study and perform analysis of data using Python and R. In addition to that; we will see the top 10 data analysis tools and understand the difference between a data scientist and a data analyst. Finally, we’ll see the top interview questions that will help you crack a data analyst interview.

Dataset Link - https://drive.google.com/drive..../folders/1U8CF0Xx3Ni

00:02:09 Data Analytics for Beginners
00:03:01 What is Data Analytics
00:04:30 Steps involved in Data Analytics
00:06:25 Tools used in Data Analytics
00:10:00 Data analytics applications
00:16:35 companies using data analytics
00:17:15 case study
00:21:15 Who is a data analyst
00:21:53 Top 10 skills to become a Data analyst in 2022
00:28:54 Top 10 Analysis Tools
00:47:10 Data Analytics Using Phyton
02:34:14 R tutorial
03:02:54 Time Series
04:13:12 Pivot table in Excel
04:41:20 Excel Dashboarding
06:12:03 Covid 19
06:53:31 Spotify
07:34:18 World Happiness Report
08:29:51 Olympics
09:27:32 Data Analyst Interview Questions

⏩ Check out the Data Analytics Playlist: link: https://www.youtube.com/playli....st?list=PLEiEAq2VkUU

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#DataAnalyticsFullCourse #2022 #DataAnalyticsForBeginners #LearnDataAnalytics #DataAnalysis #DataAnalytics #Simplilearn

➡️ 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=16Oct2023LGLoopEvening1-CaqJ65CIoMw&utm_medium=Description&utm_source=youtube
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🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

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Uno de los conceptos más importantes, aunque menos comprendidos, en toda la física. Visita https://brilliant.org/veritasium para comenzar tu prueba gratuita de 30 días, ¡y las primeras 200 personas obtienen un 20% de descuento en la suscripción anual premium!

Si estás buscando un kit de modelado molecular, prueba Snatoms, un kit que inventé donde los átomos se unen magnéticamente: https://snatoms.com

▀▀▀
Un agradecimiento enorme a quienes nos ayudaron a comprender diferentes aspectos sobre el tema del video: Dr. Ashmeet Singh, Supriya Krishnamurthy, Dr. Jos Thijssen, Dr. Bijoy Bera, Dr. Timon Idema, Álvaro Bermejillo Seco y Dr. Misha Titov.

▀▀▀
Referencias:
Carnot, S. (1824). Reflexiones sobre la fuerza motriz del calor y sobre las máquinas aptas para desarrollar esa fuerza. - https://ve42.co/Carnot1890

Aprovechando el verdadero poder de los átomos | Documentales sobre Orden y Desorden, Spark a través de YouTube - https://ve42.co/OrderDisorder

Una mejor descripción de la entropía, Steve Mould a través de YouTube - https://ve42.co/Mould2016

Dugdale, J. S. (1996). Entropía y su significado físico. CRC Press. - https://ve42.co/Dugdale1996

Schroeder, D. V. (1999). Introducción a la física térmica. - https://ve42.co/Schroeder2021

Fowler, M. Motores de calor: el ciclo de Carnot, Universidad de Virginia. - https://ve42.co/Fowler2023

Chandler, D.L. (2010). Explicado: El límite de Carnot, MIT News - https://ve42.co/Chandler2010

Entropía, Wikipedia - https://ve42.co/EntropyWiki

Clausius, R. (1867). La teoría mecánica del calor. Van Voorst. - https://ve42.co/Clausius1867

¿Qué es la entropía? TED-Ed a través de YouTube - https://ve42.co/Phillips2017

Thijssen, J. (2018) Apuntes de Física Estadística, TU Delft.

Schneider, E. D., & Kay, J. J. (1994). La vida como manifestación de la segunda ley de la termodinámica. Modelado matemático y computacional, 19(6-8), 25-48. - https://ve42.co/Schneider1994

Lineweaver, C. H., & Egan, C. A. (2008). Vida, gravedad y la segunda ley de la termodinámica. Revisiones de la física de la vida, 5(4), 225-242. - https://ve42.co/Lineweaver2008

Michaelian, K. (2012). Opiniones de la HESS "Catalización biológica del ciclo hidrológico: función termodinámica de la vida". Hidrología y Ciencias de la Tierra, 16(8), 2629-2645. - https://ve42.co/Michaelian2012

England, J. L. (2013). Física estadística de la autoreplicación. El Journal of chemical physics, 139(12), 09B623_1. - https://ve42.co/England2013

England, J. L. (2015). Adaptación disipativa en la autoorganización impulsada. Nanotecnología de la naturaleza, 10(11), 919-923. - https://ve42.co/England2015

Wolchover, N. (2014). Una nueva teoría de la física de la vida, Quantamagazine - https://ve42.co/Wolchover2014

Lineweaver, C. H. (2013). La entropía del universo y el principio de la máxima producción de entropía. En Más allá de la segunda ley: Producción de entropía y sistemas no equilibrio (pp. 415-427). Berlín, Heidelberg: Springer Berlin Heidelberg. - https://ve42.co/LineweaverEntropy

Bekenstein, J.D. (1972). Agujeros negros y la segunda ley. Lett. Nuovo Cimento 4, 737–740. - https://ve42.co/Bekenstein1972

Carroll, S.M. (2022). Las Ideas Más Grandes del Universo: Espacio, Tiempo y Movimiento. Grupo Editorial Penguin. - https://ve42.co/Carroll2022

Termodinámica de agujeros negros, Wikipedia - https://ve42.co/BlackHoleTD

Cosmología y la flecha del tiempo: Sean Carroll en TEDxCaltech, Charlas TEDx a través de YouTube - https://ve42.co/CarrollTEDx

Carroll, S. M. (2008). Los orígenes cósmicos de la flecha del tiempo. Scientific American, 298(6), 48-57. - https://ve42.co/Carroll2008

El Paso del Tiempo y el Significado de la Vida | Sean Carroll (Charla + Preguntas y Respuestas), Fundación Long Now a través de YouTube - https://ve42.co/CarrollLNF


▀▀▀
Escrito por Casper Mebius, Derek Muller y Petr Lebedev
Editado por Trenton Oliver y Jamie MacLeod
Animado por Mike Radjabov, Ivy Tello, Fabio Albertelli y Jakub Misiek
Filmado por Derek Muller, Albert Leung y Raquel Nuno
Video de colisiones moleculares por CSIRO's Data61 a través de YouTube: Simulación de aire
Video/fotos adicionales proporcionados por Getty Images, Pond5 y cortesía de NASA, NASA's Goddard Space Flight Center, NASA Goddard Flight Lab/ CI Lab, equipos científicos AIA, EVE, HMI y WMAP. Además, el Laboratorio de Visualización Avanzada en el Centro Nacional de Supercomputación, B. Robertson, L. Hernquist
Música de Epidemic Sound y Jonny Hyman
Producido por Derek Muller, Petr Lebedev, Emily Zhang y Casper Mebius.

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Video en Inglés del Canal @Veritasium: https://www.youtube.com/watch?v=DxL2HoqLbyA

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

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#veritasiumenespañol

🔥Enroll for Advanced Certification in Full Stack Web Development: https://intellipaat.com/full-s....tack-web-developer-m

#FullStackWebDeveloperCourse2024 #CompleteFullStackWebDeveloperCourse #FullStackWebDevelopment #FullStackWebDeveloper #WebDevelopment #Intellipaat

This Full Stack Web Development Course covers all the important skill sets required to get you a Full Stack Web Developer job!

This Full Stack Web Development Course will cover Introduction To Full Stack Web Development, Pre - Requisites of HTML, Fundamentals of HTML, Fundamentals of CSS, Interactivity using, Javascript, Introduction to NodeJS, NodeWebToolkit, Introduction to UI with React, Fundamentals of ReactJS, Project on Creating React App, Difference Between Angular and React. In the end, we’ll also discuss Full Stack Web Development Interview Questions.

This Full Stack Web Development Course video is suitable for those individuals who aspire to become Full Stack Web Developers.

✅ What is the full-stack developer in 2024?
Every full-stack developer should be familiar with skills: CSS and HTML ExpressJS and NodeJS Database administration systems Version management Platforms for web hosting, etc. Which stack will be in demand in 2024? In 2024, ReactJS and NodeJS will be two of the most popular web development stacks

🔵 Following topics are covered in this Full Stack Web Development Course:
00:00:00 - Introduction
00:02:47- Introduction To Full Stack Web Development
00:04:44 - Basic Components of a WebPage
00:10:56 - What is Web Hosting?
00:21:21 - Types of Networks
00:24:14 - EMBED Tag
00:46:36 - Who to Register a Website Name?
00:54:23 - Overview of CPanel
01:19:52 - Upload HTML Files Using the FTP Application
01:36:23 - Different Ways to Add CSS
01:42:35 - Fundamentals of CSS
01:54:59 - CSS Selectors
02:14:22 - What is Pseudo-Elements?
02:41:42 - Static Webpage vs Dynamic Webpage
02:45:47 - Embedding JavaScript in HTML Files
02:59:49 - Operators in JavaScript
03:08:54 - Loops in JavaScript
03:18:46 - Hands-On: DOM Manipulation
03:36:17 - Pop-Up Boxes in JavaScript
03:48:39 - Introduction to NodeJS
05:00:27 - NodeWebToolkit
07:03:21 - Fundamentals of ReactJS
08:53:15 - What is ReactJS?
09:03:41 - UI without ReactJS
09:53:56 - Lifecycle Methods
10:10:49 - Hands-On: Creating a React Component
10:53:48 - What is Bootstrap?
11:13:14 - Difference Between Angular and React
11:23:40 - Full Stack Web Development Interview Questions

➡️ About the Course
Our Full Stack Developer course will help you gain expertise in the latest front-end and back-end technologies of Application Development. Gain hands-on experience with skills and technologies such as SQL, Java, Data Structures, JavaScript, HTML, CSS, NodeJS, React, etc. under the guidance of industry professionals. Get an executive post-graduation Full Stack Developer certification from iHUB DivyaSampark, IIT Roorkee & Microsoft to advance your career!

➡️Who should take this course?
☑️Anyone with a bachelor’s degree and a passion for full-stack web development
☑️Professionals looking to grow their careers in software development
☑️IT professionals with a bachelor’s degree looking to transition to full-stack development
☑️Project managers/product managers looking to up-skill
☑️Anyone who wants to build a full-stack developer portfolio

✅ Key Features - (Course Features)
👉🏼12 months live online instructor-led sessions
👉🏼100 Hrs Self-paced Videos
👉🏼300 Hrs Project & Exercises
👉🏼Learn from IIT Faculty & Industry Experts

What’s Covered in This Program? -
✅Programming Fundamentals
✅Object Oriented Programming
✅Exception Handling and Threading
✅Data Structures and Algorithms
✅Front End Development
✅Back End Development
✅System Design

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🚀 Intellipaat Advanced Certifications
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🌕 Advanced Certification in Full Stack Web Development: https://intellipaat.com/web-de....velopment-certificat

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📚For more information, please write back to us at [email protected] or call us at IND: 7847955955 / USA: 1-800-216-8930

With their ability to generate human-like language and complete a variety of tasks, generative AI has the potential to revolutionise the way we communicate, learn and work. But what other doors will this technology open for us, and how can we harness it to make great leaps in technology innovation? Have we finally done it? Have we cracked AI?
Join Professor Michael Wooldridge for a fascinating discussion on the possibilities and challenges of generative AI models, and their potential impact on societies of the future.
Michael Wooldridge is Director of Foundational AI Research and Turing AI World-Leading Researcher Fellow at The Alan Turing Institute. His work focuses on multi-agent systems and developing techniques for understanding the dynamics of multi-agent systems. His research draws on ideas from game theory, logic, computational complexity, and agent-based modelling. He has been an AI researcher for more than 30 years and has published over 400 scientific articles on the subject.
This lecture is part of a series of events - How AI broke the internet - that explores the various angles of large-language models and generative AI in the public eye.
This series of Turing Lectures is organised in collaboration with The Royal Institution of Great Britain.

Python Certification Training for Data Science : https://www.edureka.co/python-....programming-certific
This Edureka session on "Scikit-learn Tutorial" will introduce you to machine learning in Python. It will also take you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset.
Topics covered in this video are:
00:00:00 Introduction
00:03:00 Machine Learning Overview
00:09:31 Introduction to Scikit-learn
00:16:48 Installation of Scikit-learn
00:17:10 Regression & Classification
00:18:00 SVM &KNN
00:20:00 Hands-on

Subscribe to our channel to get video updates. Hit the subscribe button above.
Check out our Python Training Playlist: https://goo.gl/Na1p9G

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

🔵 DevOps Online Training: https://bit.ly/2BPwXf0
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🟣 Tableau Online Training: https://bit.ly/3g784KJ

-----------------------------------------Edureka Masters Programs---------------------------------------------------

🔵DevOps Engineer Masters Program: https://bit.ly/2B9tZCp
🟣Cloud Architect Masters Program: https://bit.ly/3i9z0eJ
🔵Data Scientist Masters Program: https://bit.ly/2YHaolS
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🟣Business Intelligence Masters Program: https://bit.ly/2BPLtn2
🔵Python Developer Masters Program: https://bit.ly/2Vn7tgb
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🔵 Artificial and Machine Learning PGD: https://bit.ly/3AylL0q

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#Python #PythonForDataScience #PythonTutorial #PythonForBeginners #PythonOnlineTraining
#learnmachinelearning #withme
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 Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR.
During our Python Certification Training, our instructors will help you to:

1. Master the basic and advanced concepts of Python
2. Gain insight into the 'Roles' played by a Machine Learning Engineer
3. Automate data analysis using python
4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
6. Explain Time Series and it’s related concepts
7. Perform Text Mining and Sentimental analysis
8. Gain expertise to handle business in future, living the present
9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience

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

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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Earn a Generative AI certificate today → https://ibm.biz/BdKUNX
Learn more about watsonx: https://ibm.biz/BdvDnr

AI promises to touch every aspect of work and life, but how do they get made?

In this video Martin keen walks through a five step framework for how to build and deploy AI models.

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdKUNr

Sometimes, a large language model is too much horse, so to speak. For tasks that are smaller in scope and more fine-tuned for a specific task, a small language model, or SLM, might be a better fit. SLMs are cheaper than LLMs, so they're great for smaller businesses or just automation tasks that don't need all the bells and whistles an LLM offers.

🔎 Read more:
SLM defined ➡️ https://www.techtarget.com/wha....tis/definition/small

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