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/

🔥 Introduction to Generative AI: https://www.edureka.co/introduction-generative-ai
🔥Generative AI Course: Masters Program: https://www.edureka.co/masters....-program/generative-

In this video, on *Generative AI Examples* , we will explore the fascinating world of *Generative AI* and how it's transforming industries. From creating stunning visual art to generating human-like text, Generative AI is opening new doors for creativity and innovation. We'll break down how AI models like *GPT* and *DALL-E* are used to generate everything from realistic images to engaging text content. By the end of this video, you’ll have a clear understanding of how Generative AI is shaping the future and what it means for businesses and creatives alike.

✅ 00:00 - Generative AI Examples
✅ 01:49 - Evolution of Generative AI
✅ 02:49 - What is Generative AI?
✅ 03:25 - Use-Cases of Generative AI
✅ 05:13 - Impact of Generative AI
✅ 05:57 - Examples of Generative AI

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

📝Feel free to share your comments below.📝

𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain
📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka
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📌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/

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

About Introduction to Generative AI Fundamentals Course

This course on Introduction to Generative AI is designed to equip learners with a solid understanding of generative models, from fundamental concepts to advanced applications. Starting with the basics, learners will grasp the principles of Generative AI, differentiate between generative and discriminative models, and explore popular tools in the field. Ethical considerations are addressed alongside practical skills like prompt engineering and deep learning techniques such as autoencoders and GANs. Advanced topics include working with large language models (LLMs), fine-tuning them for specific tasks, and applying reinforcement learning.

Who should take up this Generative AI learning path Course?

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 like software development, marketing, and retail who want to utilize AI for problem-solving and innovation. Regardless of your level of experience, whether you are a novice or a seasoned practitioner, this course provides valuable knowledge and practical skills to enhance your expertise in Generative AI.

What are the prerequisites for this Introduction to Generative AI fundamentals Course?

There are no prerequisites required for this course, Introduction to Generative AI. However, it is beneficial to have:
Basic Python Programming Knowledge
Problem-Solving Skills
Fundamentals of Machine Learning

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

#generativeaiexamples #generativeai #genai #aiexamples #artificialintelligence #aitools #aiapplications

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Following are the topics covered in this tutorial on Agentic AI Course 2026 :

00:00:05 - Introduction to Agentic AI and Multi-Agent Systems
00:01:35 - Quiz Question
00:01:55 - What Is Agentic AI And How It Works
00:02:00 - Generative AI, LLMs, RAG, And AI Workflows
00:02:39 - Practical Projects And Hands-On Learning Approach
00:03:11 - Tools And Frameworks For Building AI Systems
00:03:37 - Career Opportunities And Who Should Learn Agentic AI
00:04:03 - Learning Support, Mentorship, And Career Assistance
00:04:23 - Latest Trends In Generative AI And Agentic AI
00:04:39 - Outro

This video on Agentic AI and Multi-Agent Systems by Simplilearn explains how modern AI systems are moving beyond simple question-answering tools to intelligent systems that can plan tasks, use tools, and complete workflows. In this video, you will learn the fundamentals of Agentic AI, AI agents, multi-agent systems, LLMs, RAG, and workflow automation. The session covers how AI agents are designed, how different AI components work together, and how technologies like LangChain, Crew AI, AutoGen, MCP, and AI automation tools are used. You will also explore practical applications, real-world projects, and enterprise use cases of agentic workflows. This video is useful for developers, product managers, technology professionals, AI enthusiasts, and beginners who want to understand the future of AI systems. By the end of this video, you will understand how intelligent AI workflows are created and how skills in Agentic AI can support future career opportunities.



Related Videos:
✅ 1. https://youtu.be/2R-niMsB0QY?si=uoG8eqArZkZ8M88i
✅ 2. https://youtu.be/2MHjpOHQNNQ?si=hBzT0uI-B6ESuEU4
✅ 3. https://youtu.be/uFTrtMw8cIM?si=2GB6EM9HxTSQGVUL
✅ 4. https://www.youtube.com/live/s....TzbTqjsmIw?si=7VwvBU
✅ 5. https://youtu.be/8ykgB9lKA3M?si=UtUcxTb5grCmo7Fp

Basically a full day AI course crammed into 18 mins of drawing & talking. Target audience: Everyone.
NEWS: Book is now available: https://leanpub.com/ainutshell
NEWS: Course is now available: https://www.ymnig.ai/courses/p....rompt-engineering-in

Covers questions like What is generative AI, how does it work, how do I use it, what are some of the risks & limitations. Also covers things like autonomous agents, the role of us humans, prompt engineering tips, AI-powered product development, origin of ChatGPT, different types of models, and some tips about mindset around this whole thing.

Here is the full drawing:
https://blog.crisp.se/wp-conte....nt/uploads/2024/01/g

Voice translations:
- Playlist with a bunch of AI voice translations: https://www.youtube.com/watch?v=ojLUcaqbLBM&list=PL-u8jDo3b1vnzUtiOfLliB2tbDLwVXiqb
- French: https://www.youtube.com/watch?v=AkPW3zWHA7w
- French: https://www.youtube.com/watch?v=nTFL-GzSrWo
- German: https://www.youtube.com/watch?v=Dgu5utapmk0
- Hindi: https://www.youtube.com/watch?v=gZdUcxqKCe4
- Kurdish (Sorani): https://www.youtube.com/watch?v=bvscuBrd3b0
- Ukrainian: https://www.youtube.com/watch?v=rUqIzty9hkM
- Urdu: https://www.youtube.com/watch?v=VFy6mvcOXoc

Would you like to make a voice translation? ElevenLabs can do really voice translation which even sounds like me :)
You can also do a manual dub with your own voice if you prefer. Whatever works best.
1. Check out https://elevenlabs.io/docs/dubbing/studio
2. Make the video & upload it to youtube or somewhere.
3. Make sure the description shows that this is an AI translation, and include a link to the original video.
4. Send a link to me (for example via a comment) and I'll add it above.

You can decide if you want to host it on your youtube channel or if I should host it.

If you have practical questions you can ask Stefan Kirschnick, he made the German dub and is happy to provide support. https://www.linkedin.com/in/stefankirschnick/

Remy is giving you The AI Agent OS Playbook, everything from this episode in one free PDF → https://openresidency.com/remy-gaskell-playbook?utm_source=youtube&utm_medium=description&utm_campaign=remy_gaskell_playbook

Remy Gaskell is a self-taught AI operator who teaches founders to run their businesses on AI agents.

Most founders type prompts into a chat box one at a time and start from zero every session. Remy runs at 5-10x the output because he stopped chatting and built an operating system his AI already understands. In this episode he takes the whole thing apart: the shift to agents, the exact files on his own machine, and how to build one yourself.

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You re-explain your business in every new chat because your AI forgets the second you close the window. AI Onboard is Remy's Claude Code plugin that builds your whole AI operating system in one guided session, so it never forgets again. Half off for our audience → https://openresidency.com/onboard?utm_source=youtube&utm_medium=description&utm_campaign=ai-onboard

⏱️ CHAPTERS
00:00:00 Trailer
00:01:21 The shift from chat to agents
00:04:04 What "agent" actually means
00:11:08 The observe-think-act loop
00:18:01 Live demo: analyzing an Instagram Reel
00:20:24 Tuning an agent vs building one
00:22:45 Context files and the CLAUDE.md
00:44:03 A memory system that improves itself
00:52:06 MCP connectors, the universal translator
00:59:21 Security and permissions
01:02:19 Starter tool stack, Apify to Composio
01:18:21 Skills as SOPs for your AI
01:47:34 The Agent OS folder structure
02:06:46 The future of teams and intrapreneurship
02:16:20 Quickfire questions

🔗 AI WITH REMY
Course: https://go.theaicourse.ai/open-residency
Newsletter: https://go.aiwithremy.com/open-residency
Instagram: https://www.instagram.com/aiwithremy/
LinkedIn: https://www.linkedin.com/in/remygaskell/
Apify: 25% OFF all Apify plans for your first 3 months with code 25REMY → https://www.apify.com?fpr=remy

🎙️ OPEN RESIDENCY
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LinkedIn: https://www.linkedin.com/in/markmastrandrea/
Website: https://openresidency.com/
Email: [email protected]

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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 : https://www.edureka.co/digital-marketing (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka "Digital Marketing Career" video will help be your guide to build a career in Digital Marketing.

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

📝Feel free to share your comments below.📝

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🔵 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

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📌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/

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

🔥 Become A Data Scientist Today: https://taplink.cc/simplilearn_data_science

Data Science Course 2023 by simplilearn is dedicated to help aspiring data scientists with Data Science Full Course for Beginners 2023 that covers the major technical and theorotical data science concepts along side practical demonstrations to get a good learning experience. This Simplilearn video covers the following:

00:00:00 Data Science In 5 Minutes
00:04:14 Data Science Roadmap 2023
00:11:51 Data Science Life Cycle
00:28:53 Top 10 Data Science Certifications for 2022
00:40:40 Data Science Tutorial
01:22:35 AI vs ML vs DL vs Data Science - Difference Explained
01:28:46 Data Science With Python
02:21:50 Top 5 Python Libraries For Data Science
02:36:56 R Programming For Beginners
02:52:07 Statistics For Data Science
03:11:58 Probability Density Function With Example
03:35:49 Data Visualization In R
04:08:52 Population And Sample In Statistics Example
04:15:48 Types Of Distribution In Statistics
04:39:52 Mean Squared Error
04:44:40 Machine Learning Algorithms
05:54:10 Skewness And Kurtosis And Moments
05:59:18 Random Forest Algorithm - Random Forest Explained
06:43:20 Naive Bayes Classifier
07:24:45 Support Vector Machine - How Support Vector Machine Works
07:50:13 Bayes Theorem
07:54:59 What Is Anova?
08:02:27 What Is Correlation?
08:05:43 Types of Correlation
08:09:41 Correlation vs Regression
08:16:27 Data Science Interview Questions

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AI, Ameca, Elon Musk and Boston Dynamics. A big month for AI. Please visit https://brilliant.org/digitalengine - a great place to learn about AI and STEM subjects. You can get started for free and the first 200 people will get 20% off a premium annual subscription.

Thanks to Brilliant for sponsoring this video.

The surprising benefits of curiosity:
https://greatergood.berkeley.e....du/article/item/six_

A path towards autonomous machine intelligence, Yann LeCun
https://openreview.net/pdf?id=BZ5a1r-kVsf

The AI is GPT-3, which you can access via OpenAI. If you ask it similar questions, you'll usually get similar answers (with some variation based on settings and prior conversation).

The human-like avatar is from Synthesia (we created the Tesla robot avatar).

AI art resources (recommend midjourney for beginners):
https://www.midjourney.com/home/
https://stability.ai/blog/stab....le-diffusion-public-
https://openai.com/dall-e-2/

How your brain conjures up your sense of self.
https://www.newscientist.com/a....rticle/mg23931940-10
https://www.sciencedirect.com/....topics/psychology/so

Monkeys check themselves out in the mirror.
https://www.youtube.com/watch?v=mKl8UdTnzNE

Lightbulbs might be the best idea ever – just not for light. Head to https://brilliant.org/veritasium to start your free 30-day trial, and the first 200 people get 20% off an annual premium subscription.

A huge thanks to David Lovett for showing me his awesome relay and vacuum tube based computers. Check out his YouTube channel @UsagiElectric

▀▀▀
References:
Herring, C., & Nichols, M. H. (1949). Thermionic emission. Reviews of modern physics, 21(2), 185. – https://ve42.co/Herring1949

Goldstine, H. H., & Goldstine, A. (1946). The electronic numerical integrator and computer (eniac). Mathematical Tables and Other Aids to Computation, 2(15), 97-110. – https://ve42.co/ENIAC

Shannon, C. E. (1938). A symbolic analysis of relay and switching circuits. Electrical Engineering, 57(12), 713-723. – https://ve42.co/Shannon38

Boole, G. (1847). The mathematical analysis of logic. Philosophical Library. – https://ve42.co/Boole1847

The world’s first general purpose computer turns 75 – https://ve42.co/ENIAC2

Dylla, H. F., & Corneliussen, S. T. (2005). John Ambrose Fleming and the beginning of electronics. Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films, 23(4), 1244-1251. – https://ve42.co/Dylla2005

Stibitz, G. R. (1980). Early computers. In A History of Computing in the Twentieth Century (pp. 479-483). Academic Press.

ENIAC’s Hydrogen Bomb Calculations – https://ve42.co/ENIAC3


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Explore the various use cases and capabilities of ChatGPT, a powerful tool for generating text.

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This Edureka "Azure Machine Learning” tutorial will give you a thorough and insightful overview of Microsoft Azure ML Studio and also help you understand the fundamentals of machine learning.


🔴Subscribe to our Edureka YouTube channel to get video updates: https://goo.gl/6ohpTV

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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

Microsoft Azure Certification training from edureka will introduce you to the fundamental concepts of Microsoft Azure platform and services offered. This course offers you the opportunity to take an existing ASP.NET MVC application and expand its functionality by moving it to Azure Cloud and also focuses on the considerations necessary when building a highly available solution in the cloud.

This Microsoft Cloud training will prepare you for the 70-532: Developing Microsoft Azure Solutions certification exam. You can check the examination details and certification cost at Microsoft Azure 70-532 Exam


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Why Learn Azure?

Edureka's Azure Certification training helps you conceptualize the Microsoft Azure fundamentals and services provided on its platform. You will learn to configure and deploy Web applications and also master the creation and deployment of Azure Web apps. You will learn to create and configure Azure Virtual machines and will able to create and manage storage account, while handling blobs and containers present in it. This Azure Cloud training helps you master the SQL Database instance and the implications of importing a SQL standalone database. You will learn about the integrals of Azure Ad (Active Directory) instance and also learn to create a virtual network and implement a point-to-site network.

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In this video on ""What is LLM"" we'll dive into large language models (LLMs), powerful AI systems trained on massive text datasets to understand and generate human-like language. We'll introduce the concept of LLMs, explain how they leverage deep learning and self-attention mechanisms, and Discover examples of large language models like Google's LLaVA, OpenAI's GPT-3, and Microsoft's Turing-NLG, which power LLM's capabilities. We'll explore their impressive capabilities in tasks like text generation, summarization, question answering, and translation, as well as their real-world applications across industries.

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✅ 00:00 - Introduction to Large Language Model
✅ 01:03 - What is LLMs?
✅ 01:48 - Examples of LLMs
✅ 02:01 - Applications of LLMs
✅ 02:36 - How does LLM works?
✅ 03:57 - Understand Working of LLM With an Example

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

📝Feel free to share your comments below.📝

𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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About Large Language Models (LLMs) Course with Generative AI

Edureka's course on Large Language Models with Generative AI offers a transformative journey into the intricacies of LLMs. Delve deep into content generation and application development, gaining the expertise to create innovative solutions. Enroll now to become a trailblazer in this rapidly evolving domain.
- - - - - - - - - - - - - -

Who should go for this course?
This course on Large Language Models is suitable for data scientists, software developers, and AI researchers aiming to advance their expertise in natural language processing and utilize LLMs in their projects. Additionally, professionals in content creation, marketing, and customer service seeking to leverage LLMs for text generation and analysis can benefit from this course.

- - - - - - - - - - - - - -
What are the Prerequisites for this course?
The prerequisites for this course on Large Language Models include a basic understanding of machine learning concepts, proficiency in programming languages such as Python, and familiarity with natural language processing fundamentals. Additionally, knowledge of deep learning frameworks like TensorFlow or PyTorch would be beneficial for 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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---------------------------------------------------

►모든 권리는 해당 소유자에게 있습니다.
✔ 본 영상은

Check out the latest (and most visual) video on this topic! The Celestial Mechanics of Attention Mechanisms: https://www.youtube.com/watch?v=RFdb2rKAqFw

This is the second of a series of 3 videos where we demystify Transformer models and explain them with visuals and friendly examples.

Video 1: The attention mechanism in high level https://youtu.be/OxCpWwDCDFQ
Video 2: The attention mechanism with math (this one)
Video 3: Transformer models https://www.youtube.com/watch?v=qaWMOYf4ri8

If you like this material, check out LLM University from Cohere!
https://llm.university

00:00 Introduction
01:18 Recap: Embeddings and Context
04:46 Similarity
11:09 Attention
20:46 The Keys and Queries Matrices
25:02 The Values Matrix
28:41 Self and Multi-head attention
33:54: Conclusion

In this video, we will build a Vision Language Model (VLM) from scratch, showing how a multimodal model combines computer vision and natural language processing for vision QA. GitHub below ↓

Want to support the channel? Hit that like button and subscribe!

GitHub Link of the Code
https://github.com/uygarkurt/VLM-PyTorch

Dataset
https://huggingface.co/dataset....s/uygarkurt/simple-i

Implement and Train ViT From Scratch for Image Recognition - PyTorch
https://youtu.be/Vonyoz6Yt9c

Implement Llama 3 From Scratch - PyTorch
https://youtu.be/lrWY4O5kUTY

Fine-Tune Visual Language Models (VLMs) - HuggingFace, PyTorch, LoRA, Quantization, TRL
https://youtu.be/3ypHZayanBI

ViT (Vision Transformer) - An Image Is Worth 16x16 Words (Paper Explained)
https://youtu.be/8phM16htKbU

What should I implement next? Let me know in the comments!

00:00 VLM Explanation
04:42 Downloading Dataset
06:39 Imports
10:09 Hyperparameters
12:37 Data Processing
16:03 ViT Definition
19:05 VLM Implementation
41:40 Sample Inference
45:20 Data Loaders
52:46 Training Loop
57:50 Inference

References
https://github.com/AviSoori1x/seemore

Buy me a coffee! ☕️
https://ko-fi.com/uygarkurt