Einstein was wrong about black holes, what else? Use code veritasium at the link below to get an exclusive 60% off an annual Incogni plan: https://incogni.com/veritasium

A massive thank you to Prof. Geraint F. Lewis and Prof. Juan Maldacena for their expertise and help with this video.

A huge thank you to those who helped us understand this complicated topic: Dr. Suddhasattwa Brahma, Prof. Carlo Rovelli, Dr. Hal Haggard, Prof. Martin Bojowald, Dr. Francesca Vidotto, Prof. Andrew Hamilton, and Dr. Carl-Fredrik Nyberg Brodda.

A special thanks to Alessandro Roussel from ScienceClic for his spectacular simulations and feedback on the video. Check out his channel here: https://ve42.co/ScienceClic

An excellent book on this topic and an inspiration for this video: Cox, B., & Forshaw, J. (2023). Black holes: the key to understanding the universe.

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Special thanks to our Patreon supporters! Join the community to help us keep our videos free, forever: https://ve42.co/PatreonDE

Adam Foreman, Anton Ragin, Balkrishna Heroor, Bertrand Serlet, Bill Linder, Blake Byers, Burt Humburg, Chris Harper, Dave Kircher, David Johnston, Evgeny Skvortsov, Garrett Mueller, Gnare, gpoly, I. H., John H. Austin, Jr., john kiehl, Josh Hibschman, Juan Benet, KeyWestr, Kyi, Lee Redden, Marinus Kuivenhoven, Martin, Matthias Wrobel, Max Paladino, Meekay, meg noah, Michael Krugman, Orlando Bassotto, Paul Peijzel, Richard Sundvall, Sam Lutfi, Stephen Wilcox, Tj Steyn, Toni , TTST, Ubiquity Ventures, wolfee

If you’re looking for a molecular modeling kit, try Snatoms, a kit I invented where the atoms snap together magnetically - https://ve42.co/SnatomsV

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References:
Thorne, K. (1995). Black Holes & Time Warps: Einstein's Outrageous Legacy.
Relativity Playlist by ScienceClic - https://ve42.co/SCPlaylist
Hamilton, A. J. S. (2021). General Relativity, Black Holes, and Cosmology - https://ve42.co/Hamilton2021
Black Hole Events by PBS Space Time - https://youtu.be/vNaEBbFbvcY?l....ist=PLsPUh22kYmNBl4h
Newton’s Letters via The Newton Project - https://ve42.co/NewtonMail
Einstein, A. (1915). Die feldgleichungen der gravitation. - https://ve42.co/Einstein1915
Why Time and Space Swap by ScienceClic - https://youtu.be/GQZ3R81iyE0
Schwarzschild, K. (1916). Über das Gravitationsfeld eines Massenpunktes nach der Einsteinschen Theorie. - https://ve42.co/Schwarzschild1916
Wali, K. C. (1982). Chandrasekhar vs. Eddington—an unanticipated confrontation. - https://ve42.co/Wali1982
How to Build a Black Hole by PBS Space Time - https://www.youtube.com/watch?v=xx4562gesw0
Oppenheimer, J. R., & Volkoff, G. M. (1939). On massive neutron cores. - https://ve42.co/TOVLimit
Oppenheimer, J. R., & Snyder, H. (1939). On continued gravitational contraction. - https://ve42.co/Oppenheimer1939
Schwarzschild Geometry by Andrew Hamilton - https://ve42.co/SchwarzGeom
Why all world maps are wrong by Vox - https://www.youtube.com/watch?v=kIID5FDi2JQ
Hamilton, A. J., & Lisle, J. P. (2008). The river model of black holes. - https://ve42.co/HamiltonLisle2008
Mapping The Multiverse by PBS Space Time - https://www.youtube.com/watch?v=4v9A9hQUcBQ
Rotating black hole via Wikipedia - https://ve42.co/WikiRBH
Wormhole Travel by PBS Space Time - https://www.youtube.com/watch?v=ldVDM-v5uz0
Morris, M. S., & Thorne, K. S. (1988). Wormholes in spacetime and their use for interstellar travel. - https://ve42.co/MorrisThorne1988

Images & Video:
D3 Geo Projection Library by Mike Bostock https://ve42.co/d3geo
Interrupted Maps by Jason Davies https://ve42.co/DaviesMaps
Kazmierczak, J. et al. (2021). NASA’s NICER Tests Matter’s Limits. - https://ve42.co/NasaNICER
Bridgman, T. et al. (2024). M5.1 flare 'Double Whammy', at Active Regions 13559 and 13561. NASA SVS. - https://ve42.co/NasaFlare
Schnittman, J. et al. (2019). Black Hole Accretion Disk Visualization. - https://ve42.co/NasaAccrDisk
Wiessinger, S. et al. (2020). A Decade of Sun. NASA SVS. - https://ve42.co/NasaSunDecade
Skelly, C. et al. (2017). What is a Neutron Star? NASA SVS. - https://ve42.co/NasaNeutron
What would we see if we fell into a black hole by ScienceClic - https://youtu.be/4rTv9wvvat8
Earth texture - https://ve42.co/NASAEarth
First image of Sgr A* - https://ve42.co/EHT1
Image of M87 - https://ve42.co/EHT2
Polarized light image of Sgr A* - https://ve42.co/EHT3

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Directed by Casper Mebius
Written by Casper Mebius, Derek Muller and Will Wood
Edited by Trenton Oliver
Animated by Fabio Albertelli, Ivy Tello, Mike Radjabov, David Szakaly, Jonny Hyman, and Alessandro Roussel
Illustrated by Jakub Misiek
Filmed by Derek Muller
Additional research by Gregor Čavlović
Produced by Casper Mebius, Derek Muller, Will Wood, Giovanna Utichi, Rob Beasley Spence, Gregor Čavlović, and Emily Taylor

Thumbnail contributions by Jakub Misiek, Ren Hurley and Peter Sheppard
Additional video/photos supplied by Getty Images, Storyblocks, and NASA SVS
Music from Epidemic Sound

🔥PROMPT ENGINEERING WITH GENERATIVE AI: https://www.edureka.co/prompt-....engineering-generati
Explore the field of artificial intelligence through our Generative AI Tutorial. This video will break down the intricate workings of the generative AI model and give you useful information and pointers for utilizing generative AI in your projects, covering everything from the fundamentals of the technology to its applications, frameworks, and changing the landscape of industries. This course is a crucial place to start, whether you're a professional looking to implement AI-driven solutions or a newcomer excited to learn about AI's creative potential.

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

What is Prompt Engineering?

Prompt engineering involves optimizing artificial intelligence engineering for multiple purposes. It includes refining large language models (LLMs) using specific prompts and recommended outputs. Additionally, it focuses on enhancing input to different generative AI services to make text or images. With advancements in generative AI tools, prompt engineering becomes crucial for generating diverse content, such as robotic process automation bots, 3D assets, scripts, robot instructions, and various digital artifacts.
- - - - - - - - - - - - - -

What kinds of jobs can you get with Prompt Engineering skills?‎

Here are some potential job roles:

Machine Learning Engineer
Data Scientist
Natural Language Processing (NLP) Engineer
AI Research Scientist
Software Engineer (AI/ML)
Data Engineer
Content Generation Specialist
Conversational AI Developer
AI Product Manager
AI Ethicist

Remember that the job market and the demand for specific skills can change over time, so staying updated on industry trends and job postings is essential.

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

Who are the instructors for Prompt Engineering Course?

All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.

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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In this tutorial, we'll explore how RAG works, why it’s essential for modern AI, and how it solves the limitations of traditional language models. Whether you're a developer, tech enthusiast, or just curious about AI advancements, this guide will show you how RAG is shaping the future of AI by making models smarter and more relevant.RAG allows AI models to search for real-time information from external sources, combine it with their existing knowledge, and generate accurate, up-to-date responses. It’s like giving AI the ability to "Google" for answers while keeping their knowledge sharp.

00:00 Introduction

-- Frequently Asked Questions

✅ Question 1 What is Retrieval Augmented Generation (RAG)?

Answer:RAG is a hybrid AI technique that combines two key processes: retrieving external data from a knowledge base or web and then generating a response based on both the retrieved information and the model’s pre-trained knowledge. This allows the model to provide more accurate and up-to-date answers compared to traditional language models that only rely on their training data.


✅ Question 2 How does RAG improve the accuracy of AI models?

Answer:RAG enhances accuracy by pulling in relevant, real-time information from external sources. Traditional language models can only answer questions based on data they were trained on, which can become outdated. With RAG, AI models can retrieve the latest information, combine it with what they already know, and generate a response that’s both current and highly informed.


✅ Question 3 What are the main applications of RAG?

Answer:RAG is used in various fields such as customer support, healthcare, finance, and legal research. It powers AI models to deliver real-time, accurate responses by retrieving the most relevant and recent data. For instance, it’s used in chatbots for better customer service, in healthcare for retrieving the latest research, and in search engines to provide more relevant search results.

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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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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐂𝐨𝐮𝐫𝐬𝐞 - 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 𝐭𝐨 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝: https://www.edureka.co/openai-....chatgpt-training-cou
This Edureka video on 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐂𝐫𝐚𝐬𝐡 𝐂𝐨𝐮𝐫𝐬𝐞 will explain what ChatGPT is all about. In this ChatGPT tutorial, you will learn about how ChatGPT works and the language model that ChatGPT uses.

ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and has been fine-tuned using both supervised and reinforcement learning techniques.

📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟒 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU

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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.

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This Gen AI Full Course video by simplilearn helps us learn everything about Generative AI. The Introduction to Gen AI Full Course covers key topics such as What is AI and How it Works, the Evolution of AI, and a Roadmap to Become a Gen AI Engineer, along with advanced subjects like Generative Adversarial Tutorial, Transformers in AI, Langchain, ML Projects for Resume, and Top 10 AI Technologies, including deep dives into OpenAI ChatGPT-01 Model, LLM Benchmarking, and AI Career Opportunities.

Following are the topics covered in the Gen AI Full Course:

00:00:00 Introduction to Gen AI Full Course
00:08:08 Introduction to What is AI and How it works
00:08:53 What is Gen AI
00:19:32 Roadmap to become a Gen AI Engineer
00:35:11 AI Trends for future
00:36:31 Evolution of AI
01:00:50 Open ai chatgpt o1 model
01:02:40 AI Carrer opportunities
01:10:03 Top 10 AI Technologies
01:25:03 Deep Learning
01:33:20 Search GPT
01:35:35 Langchain
01:48:06 ML Projects for resume
02:50:29 Generative Adversarial Tutorial
02:59:51 What are Gans
03:00:52 Transformers in AI
03:13:21 LSTM
03:14:36 Gen Ai vs AI
03:23:39 Introduction to LLM
04:24:52 What is ML
05:21:24 ML Tutorial for beginners
07:26:47 Reinforcement Learning
08:56:26 Recurrent Neural Network
09:16:17 CHatgpt analyse
09:25:10 LLM Benchmarkeing
10:15:53 Hugging Face and its tutorial

✅ What is Gen AI?
Generative artificial intelligence, also known as generative AI or gen AI for short, is a type of artificial intelligence (AI) that can create new content and ideas, including conversations, stories, images, videos, and music. It can learn human language, programming languages, art, chemistry, biology, or any complex subject matter. It reuses what it knows to solve new problems.

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

⏩ Check out More AI Videos By Simplilearn: https://youtube.com/playlist?l....ist=PLEiEAq2VkUULyr_

#generativeai #genai #ai #machinelearning #llm #simplilearn #2024

➡️ About Professional Certificate Program in Generative AI and Machine Learning

The Generative AI and Machine Learning course enriches your career journey with comprehensive coverage of machine learning, deep learning, NLP, generative AI, reinforcement learning, computer vision, and more. Combining theory with hands-on practice, it offers live virtual sessions, projects with integrated labs, and masterclasses by IIT Guwahati faculty.

Key Features

✅ Program completion certificate from E&ICT Academy, IIT Guwahati
✅ Curriculum delivered in live virtual classes by seasoned industry experts
✅ Exposure to the latest AI advancements, such as generative AI, LLMs, and prompt engineering
✅ Interactive live-virtual masterclasses delivered by esteemed IIT Guwahati faculty
✅ Opportunity to earn an Executive Alumni Status from E&ICT Academy, IIT Guwahati
✅ Eligibility for a campus immersion program organized at IIT Guwahati
✅ Exclusive hackathons and “ask-me-anything” sessions by IBM
✅ Certificates for IBM courses and industry masterclasses by IBM experts
✅ Practical learning through 25+ hands-on projects and 3 industry-oriented capstone projects
✅ Access to a wide array of AI tools such as ChatGPT, Hugging Face, DALL-E 2, Midjourney and more
✅ Simplilearns JobAssist helps you get noticed by top hiring companies

Learning Path
✅ IITG AI: Program Induction
✅ IITG AI: Programming Fundamentals
✅ IITG AI: Python for Data Science (IBM)
✅ IITG AI: Applied Data Science with Python
✅ IITG AI: Machine Learning
✅ IITG AI: Deep Learning with TensorFlow (IBM)
✅ IITG AI: Deep Learning Specialization
✅ IITG AI: Essentials of Generative AI, Prompt Engineering & ChatGPT
✅ IITG AI: Advanced Generative AI

Skills Covered

✅ Generative AI
✅ Prompt Engineering
✅ Chatbot Development
✅ Supervised and Unsupervised Learning
✅ Model Training and Optimization
✅ Model Evaluation and Validation
✅ Ensemble Methods
✅ Deep Learning
✅ Natural Language Processing
✅ Computer Vision
✅ Reinforcement Learning
✅ Machine Learning Algorithms
✅ Speech Recognition
✅ Statistics

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CORRECTION: at 13:41, the probability is 6.1e-5 and not 4.8e-4 (however, the entropy is 1.75, which is correct). Thank you @dlyChimi!

Learn Shannon entropy and information gain by playing a game consisting in picking colored balls from buckets.

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40% discount code: serranoyt

Accompanying blog post: https://medium.com/p/5810d35d54b4/

0:00 Shannon Entropy and Information Gain
2:22 What ball will we pick?
4:33 Quiz
5:06 Question
5:14 Game
7:17 Probability of Winning
7:45 Products
11:00 What if there are more classes?
12:34 Sequence 2
13:44 Sequence 3
14:57 Naive Approach
15:34 Sequence 1
19:44 General Formula

What exactly is Agentic AI? In this video, we will go through an extremely simple explanation of Agentic AI. We will compare Agentic AI systems with Workflows that use LLMs but are not agentic in nature.

00:00 Introduction
00:13 RAG Based AI System
01:06 Tool Augmented AI System
02:08 Agentic AI System
08:02 Agentic AI Apps with Code
09:31 Low Code Agentic AI
11:16 AI Agent vs Agentic AI
11:42 Gen AI vs Agentic AI

Anthropic agent guide: https://www.anthropic.com/engi....neering/building-eff
RAG and tool ammended AI system project 1: https://youtu.be/CO4E_9V6li0?si=4BhXW9c6HnPC2uww
RAG and tool ammended AI system project 2: https://youtu.be/jLM6n4mdRuA?si=oKer2d5eyixiTgF5
Agno reasoning agent: https://docs.agno.com/reasonin....g/reasoning-agents#r

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Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses.

Need help building software or data analytics/AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website.

🎥 Codebasics Hindi channel: https://www.youtube.com/channe....l/UCTmFBhuhMibVoSfYo

#️⃣ Social Media #️⃣

🧑‍🤝‍🧑 Discord for Community Support: https://discord.gg/r42Kbuk
📸 Codebasics' Instagram: https://www.instagram.com/codebasicshub/
📝 Codebasics' Linkedin : https://www.linkedin.com/company/codebasics/

------

📝 Dhaval's Linkedin : https://www.linkedin.com/in/dhavalsays/
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📽️ Hem's Instagram for daily tips: https://www.instagram.com/hemvadivel/
📸 Dhaval's Personal Instagram: https://www.instagram.com/dhavalsays/

🔗 Patreon: https://www.patreon.com/codeba....sics?fan_landing=tru

If you're prompting Claude Code from scratch every time, you're working way too hard. I was too — until I figured this out.

Claude Code skills are the most underleveraged feature in AI right now. This video walks through the folder workflow — a dead-simple pattern for using AI tools on any kind of work, not just software — and then shows how to turn that workflow into a skill that your AI remembers permanently. Two skills are built from scratch on camera: a weekly meal planner that tracks what your family likes, and a personal people directory that works from anywhere. No templates. No downloads. Just a conversation.

If you've been using ChatGPT, Claude, or any AI tool and feel like you keep re-explaining yourself, this is the video that fixes that. Whether you're a developer looking to level up Claude Code or someone who's never written a line of code but wants AI to actually work for your life — meal planning, meeting notes, contacts, anything — this covers the pattern and the permanence. Especially relevant if you've heard about Claude Code skills but haven't built one yet.

Claude Code VS Code Extension: https://marketplace.visualstud....io.com/items?itemNam

#ClaudeCode #AISkills #AI #Productivity #AICoding
00:00 - Intro
01:13 - Meeting Transcripts
03:46 - Image Processor
04:25 - Meal Planner
05:34 - But ... one-shots?
06:08 - Not with Skills!
07:07 - Now, what is a skill?
07:29 - Let's try it!
08:14 - One more time... huh?
10:13 - A PEEPS Skill
11:06 - Local vs. Global?
12:13 - AI didn't fully listen?
14:28 - And, it's that easy

In this video we do a paper walk through of the GCN (Graph Convolutional Network) paper that is the most cited and one of the impactful in graph neural networks. We understand how they derived it and how it works in detail.

Big thanks to MakinaRocks for sponsoring this video, and I encourage you to check out Link which is a jupter lab extension they have developed to make notebooks more intuitive and easier to use. A few of the features I’ve found useful are:
1️⃣ Caching result from running cells in notebooks to avoid re-running them
2️⃣ Visualizing cell dependencies
3️⃣ Integrated version control

Check it out⬇️
https://bit.ly/3F4COvv

Link Demo Video 👉 https://youtu.be/uM2uPG-1eQQ
Link Documentation 👉https://makinarocks.gitbook.io/link/

Timestamps:
0:00 - Introduction
1:00 - Sponsored Segment: Link
2:09 - Abstract and overview
4:13 - Introduction to the problem
8:53 - How GCNs work
13:47 - Theory of GCN derivation
21:25 - GCN Node classification example
24:33 - Conclusions & Results
25:13 - Ending thoughts

#link #gcn

Welcome to this No Black Box Machine Learning Course in JavaScript. It’s a course where we code without using libraries because it’s the best way to learn all inner workings of a machine learning system and you’ll greatly improve your software development skills as well.

The goal in this course is to build a web app that learns to recognize drawings. This is phase 2, where we increase the accuracy of the method we developed in Phase 1. We do this by implementing more sophisticated features and using other classification methods (like the Neural Network). In Phase 2 we also learn about Data Cleaning, Confusion Matrices, Geometry and the difference between Vector and Raster data (pixels).

🎥 No Black Box Phase 1 Course: https://youtu.be/vDDjtwQDw2k

✏️ Course created by @Radu (PhD in Computer Science)

📁 Data: https://github.com/gniziemazity/drawing-data
💻 Code: https://github.com/gniziemazity/ml-course-phase-2
💻 Ilya's code: https://gist.github.com/id-ily....ch/8630fb273e5c5a0b6
💻 Neural Network Code: https://github.com/gniziemazity/neural-network
Phase 3 Poll: https://forms.office.com/e/QTMCLLaV24

⭐️ Other Resources ⭐️
Recognizer we build in this course: https://radufromfinland.com/projects/ml/recognizer
Euclidean Distance Video: https://youtu.be/3rPwfmrCwVw
Interpolation Video: https://youtu.be/J_puRs40GhM
Draw the Portal Game Tutorial (Inspired from Dr. Strange): https://youtu.be/0SxiyLk2IMM
Why the Circle has the Largest Area: https://youtu.be/CFBa2ezTQJQ
Recognizing drawings via webcam: https://youtu.be/QXB1ytG95gs
Self-driving Car Course: https://youtu.be/Rs_rAxEsAvI
Discord Server: https://discord.com/invite/gJFcF5XVn9
Scikit-learn documentation: http://scikit-learn.org/stable..../modules/generated/s

⭐️ Contents ⭐️
0:00:00 Introduction
0:04:07 Phase 1 Code Review
0:23:11 Data Cleaning
0:41:30 Confusion Matrix
1:16:00 Euclidean Distance Marker
1:16:06 Measuring the Elongation
1:39:23 Measuring the Roundness
1:59:20 Vector vs Raster (Pixels)
2:22:40 Neural Networks
3:04:49 Optimizing Neural Networks
3:25:15 Deep Neural Networks

🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
👾 jedi-or-sith
👾 南宮千影
👾 Agustín Kussrow
👾 Nattira Maneerat
👾 Heather Wcislo
👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan
👾 Oscar Rahnama

--

Learn to code for free and get a developer job: https://www.freecodecamp.org

Read hundreds of articles on programming: https://freecodecamp.org/news

🔥Edureka Tensorflow Training (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"):
https://www.edureka.co/ai-deep....-learning-with-tenso
This Edureka "𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝" video will help you in understanding why we need Transformers and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using Transformers.

🔹Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
🔹Check our complete Deep Learning With TensorFlow Blog Series: http://bit.ly/2sqmP4s
🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV

00:00 Introduction
00:41 Introduction to Deep Learning
04:48 NLP Using RNN
06:16 Scaling up NLP Task
08:13 Transformers Walk Through
10:16 Top Language Models


📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain
📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka
📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/
📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/
📌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/
00:00 Agenda
01:28 Introduction to Deep Learning
08:24 What is Deep Learning
13:02 NLP using RNN
15:59 Scaling up NLP task
16:49 Transformers WalkThrough
19:51 Top Language Models

#Edureka #EdurekaDeepLearning #Transformersxplained #TransformerNeuralNetworks #DeepLearningLanguageModels #DeepearningTutorial #EdurekaTraining

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

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

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🌕 RPA Developer Masters Program: http://bit.ly/3nw4Rb2

----------------------------------
How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
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 have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

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

About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.

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

Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

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

Dive into the world of Language Model as I guide you through the process of training a small language model using GPT-2! In this tutorial, we'll explore how to leverage the powerful distilgpt2 transformer to understand diseases and symptoms better.

📋 Tutorial Highlights:

Dataset Loading: Learn how to load a relevant dataset on diseases and symptoms from Hugging Face datasets.
Tokenization and Model Setup: Understand the crucial steps of tokenization using GPT-2's tokenizer and initializing the language model.
Training Loop: Walk through the training loop, exploring each epoch, monitoring training and validation losses, and ensuring your model is learning effectively.
Hyperparameter Tuning: Fine-tune your model by adjusting batch sizes, learning rates, and more.
Text Generation: Witness the power of your trained model by generating meaningful text based on input strings.

🤖 Why Train a domain specific Language Model like MedLLM?
Training a language model allows you to teach your model about the relationships between diseases and symptoms, enabling it to generate informative and context-aware responses.

🔔 Don't forget to like, comment, and subscribe for more exciting tutorials on Gen AI and machine learning! Your support keeps the channel thriving.

Join this channel to get access to perks:
https://www.youtube.com/channe....l/UC-zVytOQB62OwMhKR

📁 Download Code: https://github.com/AIAnytime/T....raining-Small-Langua
📚 Resources:
Hugging Face Model: https://huggingface.co/distilgpt2
Dataset Source: https://huggingface.co/dataset....s/QuyenAnhDE/Disease

#generativeai #llm #ai

This Claude Code Tutorial teaches you what I wish I knew as a software developer about Claude Code before I started AI coding. Get started here: https://clau.de/kevinstratvert

You can vibe code with other tools, or you can build durable software with tools like Claude Code. First, I'll walk you through how to install it on Mac or Windows.

Next, you’ll connect Claude Code to VS Code and learn how to build your first app - including how best to structure your prompts and what details are useful for Claude to know.

You'll review Claude's Plan mode like a senior engineer and learn how to work faster once you trust the changes it makes.

Next, you'll learn how to use Claude Code with an existing codebase - focusing on how to get it to analyze your coding standards so future changes are consistent. (Heavy use of CLAUDE.md)

Finally, you’ll execute a major architecture change and learn how best to work with Claude Code throughout the process.

Host: David DeWinter
Sponsor: Anthropic

📚 RESOURCES
Commands and Prompts to Copy ➜ https://drive.google.com/drive..../folders/1ZFRZcOCfep
CLAUDE.md guidance ➜ http://humanlayer.dev/blog/wri....ting-a-good-claude-m
Claude Code Best Practices by Anthropic ➜ https://www.anthropic.com/engi....neering/claude-code-

⌚ TIMESTAMPS
0:00 - Install Claude Code
2:08 - Create New App
7:56 - Existing App
11:48 - Architecture Change
14:31 - Your Next Steps

📩 NEWSLETTER
- Get the latest high-quality tutorial and tips and tricks videos emailed to your inbox each week: https://kevinstratvert.com/newsletter/

🔽 CONNECT WITH ME
- Official website: http://www.kevinstratvert.com
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🎒 MY COURSES
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Some links are affiliate links. Purchasing through these links gives me a small commission to support videos on this channel. The price to you is the same.

#stratvert #claudeai

If you want to master AI agents in 2026 and actually build systems that can reason, plan, and take action on their own- this video is your complete roadmap.

I walk you through exactly what you need to learn, in what order, and which free resources will actually get you there- without wasting months chasing hype.

The space feels overwhelming right now. Everyone's throwing around words like agents, autonomy, multi-agent systems, tool use, planning, memory- and it all sounds exciting, but also kind of chaotic.
You're not too late. But you do need a plan. That's exactly what this video gives you.

Chapters:
00:00 – Why You Need a Roadmap for AI Agents
01:08 – Prerequisites: Python, APIs, ML Fundamentals
03:25 – Month 1: Foundations & Architecture
05:17 – Month 2: Agent Frameworks & Memory
06:55 – Month 3: Tools, APIs & Multi-Agent Systems
08:06 – Month 4: Evaluation, Safety & Deployment
08:55 – Month 5-6: Specialization, Advanced Topics & Capstone
10:12 – Final Advice & Resources

Free Resources Mentioned:

Prerequisites-
Google's Python Class: https://developers.google.com/edu/python
Python for Everybody (Charles Severance): https://www.py4e.com/

Month 1: Foundations

Hugging Face Agents Course: https://huggingface.co/learn/agents-course

Month 2: Frameworks & Memory

LangGraph Documentation: https://www.langchain.com/langgraph
CrewAI Documentation: https://docs.crewai.com/

Month 3-4: Tools, Multi-Agent & Production

OpenAI Function Calling Guide: https://platform.openai.com/do....cs/guides/function-c
LangSmith (Evaluation): https://www.langchain.com/langsmith

Month 5-6: Advanced & Capstone

Berkeley LLM Agents Course: https://llmagents-learning.org/f24

Drop a comment: Where are you in your AI agents journey? Just starting, already building, or stuck somewhere in the middle?

Synopsis: Two trained fighters engage in full combat.

Written/Directed/Edited by: Baldemar Garcia

FOLLOW ME ON SOCIAL MEDIA:
Instagram: @1stClassCinema
Facebook: @1stClassCinema

Cast:
Ray'Lin Francois
Bijan Bashiri

Crew:
Alejandro Arroyo
Jose David Niño
Jimena Covarrubias

In this video, you are going to learn about Hypothesis Testing in Statistics. We will discuss the null hypothesis, the alternate hypothesis, the statistical significance of a hypothesis test, and more. This video breaks down these concepts into easy-to-understand chunks so you can grasp their meaning and applications. We'll also go over a real-world example near the end to show how these concepts are applied.

Below topics will be covered in this Hypothesis Testing video:
00:00 What is Hypothesis Testing?
01:06 Research Question
02:22 Criteria for good hypothesis
03:37 Null hypothesis
04:31 Test Statistic
06:34 Hypothesis example
07:14 Significance level

🔥Free Data Science Course with Completion Certificate: https://www.simplilearn.com/data-science-free-course-for-beginners-skillup?utm_campaign=HypothesisTestingExplained&utm_medium=Description&utm_source=youtube

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#HypothesisTesting #HypothesisTestingInStatistics #HypothesisTestingExampleProblems #HypothesisTestingExplained #WhatIsHypothesisTesting #StatisticsBasics #LearnStatistics #Simplilearn

✅What is Hypothesis Testing?
Hypothesis Testing is a type of statistical analysis in which we put our assumptions about a population parameter to the test. It is used to estimate the relationship between 2 statistical variables. Whenever we want to make claims about the distribution of data or whether one set of results are different from another set of results in machine learning, we rely on hypothesis tests.


About Simplilearn Data Scientist Program:
This Data Science course, in collaboration with IBM, features exclusive IBM hackathons, masterclass, and Ask-me-anything sessions for the best training experience. This Data Scientist certification training provides hands-on exposure to key technologies including R, Python, Machine Learning, Tableau, Hadoop, and Spark via live interaction with practitioners, practical labs, and industry projects.

Learning Objectives of Simplilearn Data Scientist Program:
Data Scientist is one of the hottest professions. IBM predicts the demand for Data Scientists will rise by 28% by 2020. Simplilearn's Data Science certification course co-developed with IBM encourages you to master skills including statistics, hypothesis testing, data mining, clustering, decision trees, linear and logistic regression, data wrangling, data visualization, regression models, Hadoop, Spark, PROC SQL, SAS Macros, recommendation engine, supervised, and unsupervised learning and more.


Key Features of Simplilearn Data Scientist Program:
✅ Exclusive Hackathons and Live interaction with IBM leadership (Includes live Master Classes and Ask me anything sessions)
✅ 220+ hours of live interactive learning (Live Data Science online classes by industry experts)
✅ Capstone and 15+ real-life Data Science projects (Built on datasets of Amazon, UBER, Comcast)
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🔥 Generative AI Certification course: https://www.edureka.co/introduction-generative-ai

In this video, on *What is Generative AI* , we will dive into Generative AI, exploring its definition, key examples, and diverse applications across sectors like healthcare and education. We'll also discuss how generative AI shapes different fields, highlight its potential future impact, and showcase a practical mini-project. Using the YouTube Transcript API and Google's generative AI, we will learn how to build a video summarizer, providing a hands-on example of generative AI’s capabilities. This video is ideal for tech enthusiasts and beginners alike, as it unpacks generative AI's transformative role in the tech world.

✅ 00:00 - Introduction to Generative AI
✅ 03:05 - What is Generative AI?
✅ 04:10 - Applications of Generative AI
✅ 05:18 - How Generative AI Works?
✅ 06:24 - Examples of Generative AI Tools
✅ 07:40 - Future of Generative AI
✅ 08:32 - How to Build a Video Summarizer?

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What are the Outcomes after learning a Generative AI course ?

Upon completing the Introduction to Generative AI Fundamentals Course, participants will attain the following learning outcomes:
Develop a comprehensive understanding of generative AI
Gain practical experience through interactive sessions and exercises
Familiarize with popular generative AI algorithms
Explore the creative potential of generative AI in generating realistic images, text, music, and other forms of media, fostering innovation and experimentation.
Understand the ethical implications and considerations associated with generative AI.

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Why to Learn Generative AI course?

Enroll in the Introduction to Generative AI fundamentals Course to acquire a thorough grasp of generative models, covering everything from basic concepts to cutting-edge uses. Delve into ethical considerations and practical techniques for creating generative AI solutions for actual industry situations, equipping you for various positions in AI advancement and creativity.
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Who should learn Generative AI courses?

This course on Introduction to Generative AI is perfect for individuals aiming to pursue a career in AI, including data scientists, researchers, and developers who wish to explore generative models. It is also beneficial for professionals in various sectors, such as software development, marketing, and retail, who want to utilize AI for problem-solving and innovation.

For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US & Others: +18885487823 (toll-free)

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