🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 : https://www.edureka.co/microse....rvices-architecture- (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka Microservices Full Course video will help you learn Microservices from scratch with examples. This Microservices Tutorial is ideal for both beginners as well as professionals who want to master the Microservices Architecture. Below are the topics covered in this Microservices Tutorial:
00:00:00 Introduction
00:01:03 Agenda
00:02:02 What are Microservices?
00:08:37 What is Microservice Architecture
00:10:38 Microservices Architecture
00:12:57 Features of Microservice Architecture
00:14:08 Advantages of Microservices Architecture
00:21:16 Monolithic Vs Microservices Architecture
00:25:55 Top Microservices Tools
00:44:36 Top 10 Reasons to learn Microservices
00:51:16 Microservice Design Pattern
01:19:04 Installing Spring Boot CLI & Spring Tool Suite
01:36:05 Microservices Spring Boot
01:55:19 Building REST Web Services with Spring Boot
01:58:57 How to Setup REST Services for Spring Boot Application
02:08:30 Microservices Security
02:25:15 Microservices Vs SOA
02:36:49 Use Case
02:42:57 Which is Better SOA Vs Microservices
02:43:06 Microservices Vs API
02:53:23 Microservices Interview Question & Answers

🔴 Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

🌕 Professional Certificate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh

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📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 2023 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain
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📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN
📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

🔥 CSPO® Certification Training: https://www.edureka.co/cspo-certification-training **
This Edureka video on "Product Owner Interview Questions" will help you prepare for your scrum job interviews. The topics discussed in this course are listed below:
Beginner Level Product Owner Interview Questions
Advances Level Product Owner Interview Questions
Comparison Based Product Owner Interview Questions
Real-World Scenario Based Product Owner Interview Questions

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

Join Edureka’s Meetup community and never miss any event – YouTube Live, Webinars, Workshops etc. https://bit.ly/2EfTXS1

Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

SlideShare: https://www.slideshare.net/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
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LinkedIn: https://www.linkedin.com/company/edureka

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#edureka #EdurekaCSPO #productownerinterviewquestions #scrumproductowner #scrumedureka #scrum #productowner #agile #scrumtraining

About the Course

Scrum is an Agile Project Management Framework that can be used primarily to manage iterative and incremental projects of all types. In this course, you will learn Agile Project Management with Scrum. You will be given an overview of the principles and practices that make Scrum effective at managing projects.
At the end of the course, you will have the confidence and understanding to implement Scrum in your organization and support teams in improving their processes.

1. Get a brief introduction to Agile and Scrum methodology
2. Understand the advantages of Agile over traditional methods
3. Get to know the core practices and philosophies behind Scrum Framework
4. Learn about Roles, Events and Artifacts, and the Process flow
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Who should go for the course?

1. Developers
2. Project Managers
3. Managers-Software Development
4. Architects-Software Development
5. Product Managers
6. Software Developers
7. Software Coders
8. Software Testers

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For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).

#ai #artificialintelligence #chatgpt #midjourney #llm #largelanguagemodels #largelanguagemodel

This is a short and very simple video I created to explain some of the concepts that go into large language models like Chat-GPT4. It covers various aspects of LLM artificial intelligence like foundations, architecture, pre-training, fine-tuning, tokenization, embeddings, self-attention, positional encoding, layers and headers, and decoding. The text was created with the use of chat gpt4. All of the images were created using Midjourney AI based on the text.

🔥Professional Certificate Program in Generative AI and Machine Learning - IITG (India Only) - https://www.simplilearn.com/iitg-generative-ai-machine-learning-program?utm_campaign=labxPbQ88Zk&utm_medium=DescriptionFirstFold&utm_source=Youtube
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In this video on openAI chatgpt 4o, we will be exploring the latest open ai model gpt4o, in this we will cover what is gpt40, how it different from the other gpt mode, chatgpt features demo like chitchatting with gpt, uploading image etc, at the end we will cover safety and limitations of gpt 4o.
GPT-4o is a big upgrade in OpenAI's smart tech. It's better at understanding and talking like a human than previous versions. It's super good at tasks like answering questions or writing stories. OpenAI keeps making it better. Now, it can even understand pictures! This makes it even more useful.

Below are the topics covered in how to use chatgpt 4o video:

00:00 Introduction To Open AI ChatGPT-4o
00:24 What Is Gpt 4o
02:21 How Gpt 4o is different from other models
02:44 Chatgpt 4o Demo Features
11:25 Safety and limitations of gpt 4o

✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

#chatgpt4o # GPT4o #AI #ArtificialIntelligence #MachineLearning #2024 #Simplilearn

⏩ Check out More AI Videos By Simplilearn: https://www.youtube.com/watch?v=ad79nYk2keg&list=PLEiEAq2VkUULyr_ftxpHB6DumOq1Zz2hq&pp=gAQBiAQB

➡️ About Artificial Intelligence Engineer

This Artificial Intelligence Engineer course Created in partnership with IBM, this course introduces students to blended learning and prepares them to be AI and Data Science specialists. In Armonk, New York, IBM is a significant cognitive service and integrated cloud solution firm that provides many technology and consulting solutions.

IBM is a leader in AI and Machine Learning technology verticals for 2021. This AI masters course will prepare students for Artificial Intelligence and Data Analytics careers.

✅ Key Features

- Add the IBM Advantage to your Learning
- 25 Industry-relevant Projects and Integrated labs
- Immersive Learning Experience
- Simplilearn's JobAssist helps you get noticed by top hiring companies

✅ Skills Covered

- ChatGPT
- Flask
- Matplotlib
- django
- Python
- Numpy
- Pandas
- SciPy
- Keras
- OpenCV
- And Many More…

👉 Enroll Now: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=17May24ChatGPT4o&utm_medium=Description&utm_source=youtube
🔥🔥 *Interested in Attending Live Classes? Call Us:* IN - 18002127688 / US - +18445327688

In this beginner-friendly crash course, I’ll show you how to use real-world data with Python to create something cool from scratch.

Timeline:

00:00 - Getting Started
0:54 - What is Machine Learning
3:19 - Machine Learning Pipeline
7:18 - Popular ML Libraries & Tools
12:42 - Importing Data
20:17 - Jupyter Shortcuts
25:16 - A Real Machine Learning Problem
29:13 - Preparing the Data
32:34 - Learning & Predicting
37:28 - Calculating Model Accuracy
44:10 - Persisting Models
48:32 - Visualizing Decision Trees

Playlist Link: https://www.youtube.com/watch?v=rE9XJGakAUM&list=PLNIQLFWpQMRVKC_zohdgl3pjDk4rlN3jc

🚩 Subscribe ➜ https://bit.ly/45IwoxJ

👇 Follow Me On Social Media:

Github: https://github.com/harishneel1
LinkedIn: https://www.linkedin.com/in/harishneel/
Instagram: https://www.instagram.com/hari....sh_neel?igsh=MXEwNXA

Was this video on Python using Machine Learning helpful? Let me know your thoughts in the comments.

#python #artificialintelligence #machinelearning

Shortform link:
https://shortform.com/artem

In this video we will talk about backpropagation – an algorithm powering the entire field of machine learning and try to derive it from first principles.

OUTLINE:
00:00 Introduction
01:28 Historical background
02:50 Curve Fitting problem
06:26 Random vs guided adjustments
09:43 Derivatives
14:34 Gradient Descent
16:23 Higher dimensions
21:36 Chain Rule Intuition
27:01 Computational Graph and Autodiff
36:24 Summary
38:16 Shortform
39:20 Outro

USEFUL RESOURCES:
Andrej Karpathy's playlist: https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&si=zBUZW5kufVPLVy9E

Jürgen Schmidhuber's blog on the history of backprop:
https://people.idsia.ch/~juerg....en/who-invented-back


CREDITS:
Icons by https://www.freepik.com/

Top Christmas Songs of All Time 🎅🏼 Best Christmas Music Playlist

🎄 Immerse yourself in the joyful and spirited atmosphere of Christmas as we present a carefully curated selection of the best Christmas songs from various eras.

"We Wish You A Merry Christmas" and other timeless classics come together to create a harmonious blend of holiday cheer. Whether you're preparing for a festive gathering, decorating your home, or simply enjoying the magic of Christmas, our playlist is designed to add the perfect musical touch to your celebrations.

🎅 Experience the magic of the season with melodies that have stood the test of time and continue to bring joy to hearts around the world. From traditional carols to contemporary hits, our compilation captures the essence of Christmas, making it a memorable and heartwarming experience for everyone.

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👉 Merry Christmas to all the music lovers. This playlist is perfect for setting the mood for the holiday season and includes all the Christmas songs you know and love. Enjoy!
May the magic of Christmas fill your heart with warmth and love. Merry Christmas!

💌 Contact: [email protected]
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Best courses to learn all about AI agents and AI engineering:
DataCamp's Associate AI Engineer for Developers Track - https://datacamp.pxf.io/X4reD4
Datacamp's AI Agents Fundamentals Track - https://datacamp.pxf.io/1GgN0a
Get 25% Off DataCamp - https://datacamp.pxf.io/WO9kmG

Everyone is talking about AI agents. Almost nobody explains what they actually are or how to build one. That changes right now.

Here's the honest one-sentence version: an AI agent is a language model that can use tools, running in a loop until it finishes a job. That's it. Everything else is just details — and in this video I break all of those details down clearly, then show you how to build the same agent in four completely different ways.

Want to make real money with coding? I share high-signal insights on careers, monetization, and leverage in my free newsletter. Join here and get my guide How to Make Money With Coding instantly: https://techwithtim.net/newsletter

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⏳ Timestamps ⏳
00:00 | Overview
00:52 | What Are Agents
01:53 | The Key Building Blocks
07:15 | The Landscape/Options
08:25 | DataCamp
09:52 | Tier 1 - No Code
12:36 | Tier 2 - Low Code
15:02 | Tier 3 - Agent Harness
17:44 | Tier 4 - Full Code
19:54 | How to Pick?

Hashtags
#AIAgents #ArtificialIntelligence #AITutorial

UAE Media License Number: 3635141

Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education course: https://stanford.io/4zOyjPN
- XCS329 graduate course: https://online.stanford.edu/co....urses/cs329a-self-im

A similar curriculum is covered in XCS329z https://online.stanford.edu/co....urses/cs329z-enginee

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

View the course playlist: https://www.youtube.com/playli....st?list=PLangBM27OtE

Video Summary:
This first lecture videoof Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery and Azalia Mirhoseini on September 22, 2025, opens with an overview of scaling laws that link model parameters, training compute, and dataset size to lower test loss in large language models from GPT-2 through GPT-4. It covers few-shot and zero-shot learning, the emergence of chain-of-thought reasoning in larger models, and the role of instruction tuning and reinforcement learning from human feedback in the development of ChatGPT. The lecture introduces inference-time scaling through the Large Language Monkeys project, which repeatedly samples a model's outputs and selects correct answers with a verifier to improve performance without retraining. It then traces the shift from single-turn chatbots to agent workflows such as prompt chaining, routing, parallelization, and orchestrator-worker patterns, using Claude Code and deep research tools as examples. The session closes with logistics for the course.

Speaker Bios:
Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University

Dr. Aakanksha Chowdhery is pushing the frontier of agentic LLMs, focusing on recursive self-improvement and long-horizon agents that learn and deploy in the real world. She is one of the few researchers globally who has led frontier model training end-to-end, across both dense and mixture-of-experts (MoE) architectures. At Google, she led the 540B PaLM model, the largest densely trained language model in the world at the time. She subsequently drove pre-training and scaling of Gemini's MoE models across multiple generations, and contributed key components to PaLM-E, Med-PaLM, and the Pathways infrastructure underpinning Google's large-model efforts. She went on to build and lead pretraining teams for open intelligence efforts at Reflection and Meta. Earlier, she held research roles at Microsoft Research and Princeton. At Stanford, where she earned her PhD, she teaches CS329A (Self-Improving AI Agents) and serves as Program Chair for MLSys 2026.

Azalia Mirhoseini
Assistant Professor of Computer Science, Stanford University

Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Research Grant, the Google ML and Systems Junior Faculty Award, MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including WSJ, NYT, Forbes, MIT Technology Review, IEEE Spectrum, WIRED, and TechCrunch.

We present the 1st True 8K drone film by Film Edge, created right here in Kuala Lumpur, Malaysia.

After 2 months of prep, 2 months of shooting, and 6 months of post-processing, we’re finally posting our Airpixel 8K Kuala Lumpur Drone Film.

With worries about the pandemic weighing on our minds, we hope this drone film sends uplifting positive vibes across the nation. We hope it can showcase the beauty of Kuala Lumpur, captured with a high dynamic range and high-resolution camera, and maybe remind us not to lose hope.

Throughout the shoot, we met countless people who shared their knowledge and techniques. We sincerely appreciate these good souls, who volunteered to lend a hand and make our work smoother in completing the film.

Special thanks to Canon Marketing Malaysia — they were the first to believe in us and back us, when we shared our project vision and objectives during our first meeting back in August 2020.

It wasn’t an easy ride throughout the entire process, but we’re so relieved that we managed to overcome all the obstacles in producing this film, with your support.

Hopefully with the kickstart of these Airpixel 8K Kuala Lumpur Drone Films, we are able to inspire many other videomakers to generate 8K films and bring more beauty into this world.

Shot with
DJI Matrice 600 Pro
DJI Matrice 600
Ronin Mx Gimbal
Canon R5, Raw Format (8192 x 4320)
Lens: Canon RF Prime Lenses 35mm, 50mm
Sigma Art Prime Lens 35mm, 50mm, 85mm

BGM: Human-Spirit—Instrumental-Version by Hans-Johnson (artist)

Credit List:
Director : Andy Tan
Executive Producer : Nic Yeow
Producer : Sandra Khor
Drone Pilot : Wayne Ooi
Drone Gimbal Operator : Andy Tan
Drone Tech Supervisor : Adrian Loo
Drone Tech Assistant : Hong Xing Hang
Drone 1st Spotter : Teh Kelven
Production Assistant : Carol Hoo
Production Assistant : James Lam
Production Assistant : James Yap
Editor : Andy Tan
Graphic Designer : Teh Kelven
VFX : Chern Liang
Color Grading : Beh Jing Qiang
ScriptWriter : Zoey Moo
TMO Director : Chris Lee Chee Khoon
TMO Cinematographer : WenChuen Boy
TMO Editor: Teh Kelven, James Yap
Social Media Admin: James Lam

Special Thanks:
Canon Marketing Malaysia
Jin Xi Cheong @ Poladrone
Syahir @ EQ Kuala Lumpur
Cui Yun @ TREC
MonopodCo
Film Troop
Rocket Science Productions
Alex Chu @ March Equipment Store
Andrew @ AKP Studio
Bahrum Jaili
Adam Lokman
Yap Khai Soon
Darryl Chong
L33 Capital Sdn Bhd

Supported by
Malaysia Sports Aviation Federation - MSAF
DJI Malaysia
Tunku Abdul Rahman University College - TAR UC

Inspired by:
AnakAnakMalaysia by EcoWorld
TheNiteNiteShow With AlvinAnthons

#AirPixel8kkl #8kdronefilm #KualaLumpur8k #8kKualaLumpur #8kDrone #AirPixel #Filmedgeproduction #Kualalumpur #Malaysia #StayHealthy #KitaJagaKita #Southeastasia #AnakAnakMalaysia #TheNiteNiteShowWithAlvinAnthons

© 2021 Film Edge Sdn Bhd. All Rights Reserved.

This course will teach you to build real-world apps with React Router 6. Click here to get to the interactive version 👉 https://scrimba.com/links/react-router-6-course

Throughout the course, you’ll be building an app called “VanLife” – an Airbnb-style web app dedicated to renting out travel vans for your next big road trip!

As you build “VanLife”, you will learn all the important parts of React Router, such as layout and index routes, nested routes, filtering results with search parameters, protecting routes for authenticated users, and more. You will also learn about the new Remix-inspired data router APIs, including Loaders and Actions.

This course was created by Bob Ziroll, Scrimba’s Head of Education.
🔗 Bob on Twitter here: https://twitter.com/bobziroll
🔗 Scrimba on YouTube: https://www.youtube.com/c/Scrimba

⭐️ Get the code ⭐️
🔗 Scrimba course: https://scrimba.com/links/react-router-6-course
🔗 GitHub repo: https://scrimba.com/links/reac....t-router-course-gith

💫 Links mentioned in course:
🔗 Scrimba’s Learn React Course - https://scrimba.com/learn/learnreact
🔗 VanLife Figma Design - https://scrimba.com/links/figma-vanlife
🔗 Firebase - https://scrimba.com/links/firebase-homepage
🔗 Firestore Docs, get all docs in collection - https://scrimba.com/links/fire....store-docs-get-all-d
🔗 Netlify - https://scrimba.com/links/netlify-home-page
🔗 GitHub Desktop - https://desktop.github.com/
🔗 Mirage JS - https://miragejs.com/

0:00 1: Introduction to React Router 6
4:56 2: Multi-page vs single-page apps
10:12 Extra: Local Development & GitHub Repo
12:25 3: React Router Setup & BrowserRouter
15:40 4: Routes
18:23 5: BrowserRouter & Routes Challenge
19:32 6: Route, Path, & Element
23:48 7: Quick Re-org
24:53 8: Link
28:55 9: VanLife project bootstrapping
37:01 10: Initial Deploy to Netlify
48:47 11: Mirage JS Server
50:41 12: Challenge: Vans Page - Part 1
1:02:09 14: Route Params
1:25:09 19: Nested Routes Intro
1:34:04 20: Fixing the Navbar with a Layout Route
1:46:45 22: Bootstrap the Host pages
1:50:45 23: Nesting the /host routes
1:54:34 24: Creating the Host Layout
2:01:04 25: Relative Paths
2:05:32 26: Index Routes
2:09:24 27: To nest or not to nest?
2:14:34 28: Nested Routes Quiz
2:19:26 29: Add Footer
2:22:47 30: NavLink
2:30:03 31: Active Link Styling with NavLink
2:39:14 33: Adding Host Vans Routes
2:44:28 34: Optimal Side Quest
2:47:49 35: Building the Host Van Detail page
2:56:47 36: Relative Links
3:03:51 37: Back to all vans
3:09:08 38: Add /host/vans/:id Nested Routes
3:17:17 39: Add the Final Navbar
3:23:53 40: Outlet Context
3:29:27 41: Update deployed version on Netlify
3:32:45 42: Search Params Intro
3:40:04 43: useSearchParams
3:48:55 45: Filter the array w/ the search param
3:55:47 47: Using Links to add search params
4:01:57 49: Using the search params setter function
4:08:05 51: Caveats to setting params
4:09:38 52: Merging search params
4:21:13 54: Challenge: Conditional rendering practice
4:25:56 55: Fix remaining absolute paths
4:27:50 56: Back to all vans
4:30:05 57: Link state
4:36:37 58: useLocation
4:47:31 60: 404 Page
4:53:22 61: Happy Path vs Sad Path
4:56:01 62: Update to our fetching code
4:59:02 63: Coding the Sad Path
5:07:37 65: Loaders intro
5:12:03 66: createBrowserRouter
5:18:13 67: Setting up the data router
5:21:00 68: Loader function
5:25:17 70: useLoaderData
5:29:52 72: Use the loader data instead of the useEffect
5:33:51 73: Loaders Quiz
5:37:06 74: Handling errors
5:39:49 75: Add errorElement to vans route
5:42:40 76: useRouteError
5:49:06 77: Initial Login Form
5:51:31 78: Importing image assets in Vite
5:54:22 79: Protected Routes
6:18:28 85: Parallel Loaders Demo
6:22:02 86: Challenge - Protected Routes
6:43:51 91: Send login message prompt to login page
6:46:56 92: Consume message
6:54:37 93: Pass message to Login page
6:58:26 94: Hot Take: Forms in React are bad
7:00:58 95: Setting up for auth
7:13:57 97: useNavigate()
7:17:44 98: React Router Form Component
7:20:41 99: Setting up the action function
7:25:13 100: Add form and action to VanLife
7:27:31 101: Action function
7:32:39 103: Get form data in VanLife
7:34:22 104: Use data in action to log in
7:36:57 105: Better (but still fake) auth
7:44:08 107: Form replace
7:49:23 108: useActionData
7:53:50 109: Action error handling
8:00:00 111: useNavigation()
8:08:07 113: Get previous route pathname
8:15:05 114: redirectTo
8:30:37 117: Deferring data
8:33:35 118: Promises and defer()
8:39:19 119: defer getVans()
8:41:04 120: Await component
8:55:28 123: React Suspense
9:00:27 124: Suspense in VanLife
9:03:51 125: Putting it all together - Defer, Await, Suspense in HostVans
9:08:31 126: errorElements in remaining van loading pages
9:11:55 127: Placeholders are gone!
9:13:57 128: Cloud Firestore
9:23:25 130: Collection reference and getVans() function
9:30:54 131: Create getVan() function
9:35:42 132: Refactor getHostVans function
9:39:29 133: Final loose ends

The truth, with photons.
I hope I've articulated everything clearly in this video. If not, I'll clarify in comments. Thanks to everyone who appears in this video and thanks to everyone who watches this video!

Veritasium is of course a combination of the latin 'veritas' meaning truth, and the common element ending 'ium'. I guess this is my version of the 'draw my life' craze that rolled through YouTube many years ago. Except I wanted to tell my story with the actual moments, the photons, the stored magnetic states. There's something about that which is so important to me (because I think the alternative involves fooling yourself) which is why I'm so fascinated by film and video.

One of my inspirations for the name Veritasium came from the end of the poem Ode on a Grecian Urn by John Keats, in which he writes:
"Beauty is truth, truth beauty,—that is all
Ye know on earth, and all ye need to know."

Special thanks to Patreon supporters:
Tony Fadell, Donal Botkin, Michael Krugman, Jeff Straathof, Zach Mueller, Ron Neal, Nathan Hansen, Yildiz Kabaran,
Terrance Snow, Stan Presolski

Music from http://epidemicsound.com
Magnified X1 - Gunnar Johnsen
Fluorescent Lights - Martin Gauffin
Dissolving Patterns - Ebb & Flod
Luna - Ebb & Flod

Additional music by Kevin MacLeod: http://incompetech.com
Sneaky Snitch

( Data Science Training - https://www.edureka.co/data-sc....ience-r-programming- )
This Logistic Regression Tutorial shall give you a clear understanding as to how a Logistic Regression machine learning algorithm works in R. Towards the end, in our demo, we will be predicting which patients have diabetes using Logistic Regression!

In this Logistic Regression Tutorial video you will understand:

1) The 5 Questions asked in Data Science
2) What is Regression?
3) Logistic Regression - What and Why?
4) How does Logistic Regression Work?
5) Demo in R: Diabetes Use Case
6) Logistic Regression: Use Cases

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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
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4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!

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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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Why Learn Data Science?

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.

After the completion of the Data Science course, you should be able to:
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2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R

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Who should go for this course?

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:

1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
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Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "