This week we’re looking into transformers. Transformers were introduced a couple of years ago with the paper Attention is All You Need by Google Researchers. Since its introduction transformers has been widely adopted in the industry.

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Models like BERT, GPT-3 made groundbreaking improvements in the world of NLP using transformers. Since then model libraries like hugging face made it possible for everyone to use transformer based models in their projects. But what are transformers and how do they work? How are they different from other deep learning models like RNNs, LSTMs? Why are they better?

In this video, we learn about it all!

Some of my favorite resources on Transformers:
The original paper - https://arxiv.org/pdf/1706.03762.pdf
If you’re interested in following the original paper with the code - http://nlp.seas.harvard.edu/20....18/04/03/attention.h
The Illustrated Transformer – https://jalammar.github.io/ill....ustrated-transformer
Blog about positional encodings - https://kazemnejad.com/blog/tr....ansformer_architectu
About attention - Visualizing A Neural Machine Translation Model - https://jalammar.github.io/vis....ualizing-neural-mach
Layer normalization - https://arxiv.org/abs/1607.06450


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00:26:25 - Blueprism vs UiPath vs Automation Anywhere
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In this design thinking tutorial, we will be looking at what is design thinking, why design thing is important, steps of design thinking, empathy mapping, five phases and four pillars of design thinking and agile methodology with design thinking. It is extremely helpful in solving problems that are ill-defined or unknown. Design Thinking is an iterative method that helps resolve user issues or redefines problems in alternative strategies and solutions.

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Below are the topics we will be discussing in the video:
00:00:00 What is design thinking
00:07:42 Dt process
00:16:58 DT steps
00:27:40 Empathy mapping
00:33:00 DT and agile

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ChatGPT, an extraordinary creation of artificial intelligence. Born from the brilliance of OpenAI's GPT-3.5 architecture, ChatGPT is designed to engage, educate, and entertain. With a vast expanse of knowledge and the ability to adapt to individual preferences, ChatGPT is here to provide valuable insights, answer questions, and spark captivating conversations. From exploring various subjects to assisting with creative endeavors, ChatGPT is ready to embark on a journey of discovery with you. Whether you are a marketing expert, cybersecurity expert, a developer or a blogger, ChatGPT is all you need!
This full course on chatgpt, will help you to excel in it in digital marketing domain, cybersecurity, ui ux, content creation, seo. With this we will also put a light on how to use ChatGPT to make money!

00:00 Introduction to ChatGPT
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Automate WhatsApp using ChatGPT
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If you're an absolute beginner in web development, you can learn the basics of HTML here. This video covers setting up an HTML file, and the basic HTML tags and what they mean.

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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
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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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Welcome back to US Military Power Channel, today's episode we will uncover "The US Army's New Super Robo-Dog With Sniper Rifle".

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Ламинарное течение, безусловно, красивый феномен. Обязательно поищите ролики на YouTube, если вы его ещё не видели. Один из больших поклонников этого явления — Дестин Сэндлин с канала Smarter Every Day, но Дерек Маллер с канала Veritasium не разделяет его восторженных взглядов и готов доказать, что турбулентное течение круче.

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Перевод: Алексей Лоскутов
Редактура: Алексей Малов
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© https://www.youtube.com/user/1veritasium
Источник: https://youtu.be/5zI9sG3pjVU

Вікно Еймса – це зображення, наприклад, на пласкому шматку картону, яке здається прямокутним вікном, але насправді є трапецією. Обидві сторони шматка картону мають однакове зображення. Картон підвішений вертикально на дроті, щоб він міг безперервно обертатися, або прикріплений до вертикальної осі, навколо якої неперервно обертається.
Під час обертання вікна здається, що обертання на мить зупиняється та змінює напрямок на протилежний. Тому воно не сприймається як безперервне обертання в одному напрямку, а натомість сприймається як коливання.
Кімната Еймса – приміщення неправильної форми, що використовується для створення тривимірної оптичної ілюзії. Була спроєктована американським офтальмологом Альбертом Еймсом в 1934 році й побудована в 1935 році.
Кімната Еймса побудована так, що спереду вона виглядає як звичайна кімната кубічної форми із задньою стінкою та двома боковими стінами, паралельними одна одній і перпендикулярними до горизонтальних площин підлоги і стелі. Проте дійсна форма кімнати трапецієподібна. [Wikipedia]

✅Текст читав: Андрій Горбунов (Lacuna)
✅Переклад, редагування, субтитрування: проєкт самоосвіти pre post, Віталій Шевчук (Цікава наука)

❕Підтримайте "Цікаву науку" на https://www.patreon.com/join/CikavaNauka або
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© Оригінальне відео: https://www.youtube.com/watch?v=dBap_Lp-0oc
© Канал Veritasium: https://www.youtube.com/c/veritasium (з люб’язного дозволу автора)
© Researched and written by Petr Lebedev and Derek Muller
Filmed by Derek Muller and Raquel Nuno
Animations, VFX, and Music by Jonny Hyman
Ames Room VFX and additional Ames Window animation by Nicolas Pratt
© Derek Muller
#SciTube #ЦікаваНаука #WatchUA

References:
Ames, A., Jr. (1951). Visual perception and the rotating trapezoidal window. Psychological Monographs: General and Applied, 65(7), i–32. https://doi.org/10.1037/h0093600
Marcel de Heer & Thomas V. Papathomas (2017) The Ames Window Illusion and Its Variations
DOI:10.1093/acprof:oso/9780199794607.003.0014
Oross, Stephen, Francis, Ellie, Mauk, Deborah & Fox, Robert. (1987). The Ames Window Illusion: Perception of Illusory Motion by Human Infants. Journal of Experimental Psychology: Human Perception & Performance, 13(4), 609-613.
Behrens, R. (1987). The Life and Unusual Ideas of Adelbert Ames, Jr. Leonardo, 20(3), 273-279. doi:10.2307/1578173
Burnham, C., & Ono, H. (1969). Variables Altering Perception of the Rotating Trapezoidal Illusion. The American Journal of Psychology, 82(1), 86-95. doi:10.2307/1420609
Allport, G. W., & Pettigrew, T. F. (1957). Cultural influence on the perception of movement: The trapezoidal illusion among Zulus. The Journal of Abnormal and Social Psychology, 55(1), 104–113. https://doi.org/10.1037/h0049372
Zenhausern R. Effect of Perspective on Two Trapezoid Illusions. Perceptual and Motor Skills. 1969;28(3):1003-1009. doi:10.2466/pms.1969.28.3.1003

Simple and easy explanation of LLM or Large Language Model in less than 5 minutes. In this short video, you will build an intuition of how a large language model works using animation and simple story telling. This is the explanation that even a high school student can understand easily.

Simple Explanation of Neural Network: https://www.youtube.com/watch?v=ER2It2mIagI

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.

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This is the 6th video in a series on using large language models (LLMs) in practice. Here, I review key aspects of developing a foundation LLM based on the development of models such as GPT-3, Llama, Falcon, and beyond.

More Resources:
▶️ Series Playlist: https://www.youtube.com/playli....st?list=PLz-ep5RbHos Read more: https://towardsdatascience.com..../how-to-build-an-llm

[1] BloombergGPT: https://arxiv.org/pdf/2303.17564.pdf
[2] Llama 2: https://ai.meta.com/research/p....ublications/llama-2-
[3] LLM Energy Costs: https://www.statista.com/stati....stics/1384401/energy
[4] arXiv:2005.14165 [cs.CL]
[5] Falcon 180b Blog: https://huggingface.co/blog/falcon-180b
[6] arXiv:2101.00027 [cs.CL]
[7] Alpaca Repo: https://github.com/gururise/AlpacaDataCleaned
[8] arXiv:2303.18223 [cs.CL]
[9] arXiv:2112.11446 [cs.CL]
[10] arXiv:1508.07909 [cs.CL]
[11] SentencePience: https://github.com/google/sent....encepiece/tree/maste
[12] Tokenizers Doc: https://huggingface.co/docs/tokenizers/quicktour
[13] arXiv:1706.03762 [cs.CL]
[14] Andrej Karpathy Lecture: https://www.youtube.com/watch?v=kCc8FmEb1nY&t=5307s
[15] Hugging Face NLP Course: https://huggingface.co/learn/n....lp-course/chapter1/7
[16] arXiv:1810.04805 [cs.CL]
[17] arXiv:1910.13461 [cs.CL]
[18] arXiv:1603.05027 [cs.CV]
[19] arXiv:1607.06450 [stat.ML]
[20] arXiv:1803.02155 [cs.CL]
[21] arXiv:2203.15556 [cs.CL]
[22] Trained with Mixed Precision Nvidia: https://docs.nvidia.com/deeple....arning/performance/m
[23] DeepSpeed Doc: https://www.deepspeed.ai/training/
[24] https://paperswithcode.com/method/weight-decay
[25] https://towardsdatascience.com..../what-is-gradient-cl
[26] arXiv:2001.08361 [cs.LG]
[27] arXiv:1803.05457 [cs.AI]
[28] arXiv:1905.07830 [cs.CL]
[29] arXiv:2009.03300 [cs.CY]
[30] arXiv:2109.07958 [cs.CL]
[31] https://huggingface.co/blog/ev....aluating-mmlu-leader
[32] https://www.cs.toronto.edu/~hi....nton/absps/JMLRdropo

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Intro - 0:00
How much does it cost? - 1:30
4 Key Steps - 3:55
Step 1: Data Curation - 4:19
1.1: Data Sources - 5:31
1.2: Data Diversity - 7:45
1.3: Data Preparation - 9:06
Step 2: Model Architecture (Transformers) - 13:17
2.1: 3 Types of Transformers - 15:13
2.2: Other Design Choices - 18:27
2.3: How big do I make it? - 22:45
Step 3: Training at Scale - 24:20
3.1: Training Stability - 26:52
3.2: Hyperparameters - 28:06
Step 4: Evaluation - 29:14
4.1: Multiple-choice Tasks - 30:22
4.2: Open-ended Tasks - 32:59
What's next? - 34:31

RE-live your 90s with this beautiful compilation of the most romantic Bollywood songs. Don't forget to tell us your favorite one.

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Song Details:
Meri Mehbooba - 00:00
Humko Sirf Tumse - 05:31
Pyaar Nahin Karna Jahan - 09:47
Teri Chunnariya - 13:54
Dekha Tujhe Toh - 18:17
Teri Mohabbat Ne Dil - 24:52
Kitna Pyaara Tujhe Rabne Banaya - 28:18
Jaati Hoon Main - 33:51
Kahin Mujhe Pyar Hua Toh Nahin - 37:05
Soldier Soldier Meethi Baaten - 43:19
Jadoo Hai Tera - 49:33
Jo Haal Dil Ka - 56:07
Aankhon Se Tune Kya Keh Diya - 01:00:16

#90severgreen #hindisongs #lovesongs #romantic #tipsofficial

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Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
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A friendly introduction to Bayes Theorem and Hidden Markov Models, with simple examples. No background knowledge needed, except basic probability.
Accompanying notebook:
https://github.com/luisguiserrano/hmm

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

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

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

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

📚 RESOURCES MENTIONED
https://www.youtube.com/watch?v=SzPE_AE0eEo
https://www.youtube.com/watch?v=6BRyynZkvf0
https://www.youtube.com/watch?v=vgncj7MJbVU
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#AIAgents #n8n #NoCode #Automation #AITutorial #Gemini #Perplexity #DigitalWorkers

What is the difference between generative ai and ai agents and agentic AI system? Let's understand it in a very simple, intuitive language.

Langgraph tutorial: https://youtu.be/CnXdddeZ4tQ?si=rkrOziDj4y_dQo4-
AI bootcamp with HR assistant agentic system: https://codebasics.io/bootcamp....s/ai-data-science-bo

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