n8n Tutorial for Beginners 2026: How to Build AI Agents
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In this video, I show you how to build your own AI agent in n8n from scratch, self-host it on Hostinger for unlimited executions, and automate tasks that normally waste hours every week. You’ll learn how to set up a VPS with one-click n8n installation, connect OpenAI models and memory, add real-world tools like Google Search, and turn the output into clean newsletters automatically sent to your inbox.

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Learn the fundamentals of generative artificial intelligence in this beginner-friendly course (2025 edition). We’ll cover what generative AI is, how it differs from traditional AI, key architectures (GANs, VAEs, Transformers), hands-on examples, prompt engineering, and real-world applications you can build today. No prior AI or deep-learning experience required.

✅ What you’ll learn:
What is generative AI & why it matters in 2026
Core models: GAN (Generative Adversarial Network), VAE (Variational Autoencoder)
Transformer architecture and large language models (LLMs)
Text-, image-, and audio-generation use cases
Basics of prompt-engineering and building your own simple GenAI app
Ethical, bias, and safety considerations for generative systems

00:00:00 Introduction
00:02:00 What is Generative AI?
00:15:29 Generative AI Examples
00:34:09 Generative AI Tools
00:55:33 What Is Artificial Intelligence?
01:06:15 Types Of Artificial Intelligence
01:16:30 What is Deep Learning
01:27:43 TensorFlow Explained
01:52:29 Convolutional Neural Network
02:13:05 Artificial Neural Network
02:44:56 Recurrent Neural Networks
03:13:50 Keras
03:38:59 What is LLM?
03:58:43 What Are GANs?
04:11:04 Transformers in Gen AI
04:18:43 Prompt Engineering Explained
04:32:28 Prompt Engineering for Code Generation
04:41:47 How to Become a Prompt Engineer?
04:46:51 Building a Chatbot with Prompt Engineering
05:03:01 GitHub Copilot
05:20:26 What is LangChain?
05:37:42 What is RAG?
06:00:34 Generate Images Using DC-GAN
06:25:08 Midjourney
06:43:30 OpenAI API using Python
06:51:44 Generative AI in Marketing
07:02:14 The Future of Generative AI and Job Opportunities
07:07:58 Nvidia's Latest Breakthrough in Generative AI
07:14:36 DeepSeek vs OpenAI: Who Wins the AI Race?
07:27:09 Alibaba’s Qwen 2.5-Max Just Beat GPT-4 & DeepSeek?
07:31:32 DeepSeek Training Cost
07:37:01 Exploring the Ethics of Generative AI
07:44:31 What is Agentic AI?
07:54:30 Agentic AI vs Generative AI
08:12:31 Dangers of AI
08:35:17 Artificial Intelligence Project Ideas
08:48:54 Top 10 Benefits Of Artificial Intelligence
09:00:42 GenAI Roadmap
09:09:27 Top 5 Generative AI Career Opportunities
09:25:40 Generative AI Interview Questions

📌 By the end of this course you’ll be equipped to explore your own generative-AI experiments and skill up for deeper tools.

🔔 If you enjoyed this video, Subscribe & hit the bell icon so you won’t miss further AI-tutorials in 2026. Leave a comment if you have questions or want to see a project walkthrough.

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Please write back to us at [email protected] or call us at IND: 9606058406 / US: +18885487823 (toll-free) for more information.

In this Python tutorial, We'll see how to create an AI Text Generation Solution with GPT-Neo from Eleuther AI.
We'll learn
1. About GPT-Neo
2. How to install the latest Hugging Face Transformers Package
3. Load Text Generation pipeline and Download Pre-trained GPT-Neo Models
4. Text Generation

Eleuther AI - https://www.eleuther.ai/
GPT-Neo on Hugging Face Model Hub - https://huggingface.co/EleutherAI/gpt-neo-1.3B
GPT-Neo - https://github.com/EleutherAI/gpt-neo
GPU Focused GPT-NeoX - https://github.com/EleutherAI/gpt-neox/
Colab Code - https://colab.research.google.....com/drive/1UByjdT5l_

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An introduction to support vector machines (SVMs) that requires very little math (no calculus or linear algebra), only a visual mind.
This is the third of a series of three videos.
- Linear Regression: https://www.youtube.com/watch?v=wYPUhge9w5c
- Logistic Regression: https://www.youtube.com/watch?v=jbluHIgBmBo

0:00 Introduction
1:42 Classification goal: split data
3:14 Perceptron algorithm
6:00 Split data - separate lines
7:05 How to separate lines?
12:01 Expanding rate
18:19 Perceptron Error
19:26 SVM Classification Error
20:34 Margin Error
25:13 Challenge - Gradient Descent
27:25 Which line is better?
28:24 The C parameter
30:16 Series of 3 videos
30:30 Thank you!

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Simplilearn's Google Cloud Platform (GCP) Architect certification program will empower you with the skills needed to advance your career in cloud architecture and become a certified Google Professional Cloud Architect. The course covers IAM, Networking, cloud storage, and much more. Simplilearn’s online Google Cloud Platform (GCP) Architect certification course introduces you to the flexible infrastructure and platform services provided by Google Cloud Platform. In this course, you will learn how to analyze and deploy infrastructure components such as networks, storage systems, and application services.

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_At Fully Connected, the MLOps conference held by Weights & Biases, Peter Welinder discusses how OpenAI (developers of GPT-4 and DALL-E 2) uses Weights & Biases for model training, with an emphasis on sharing training runs, the ability to create reports, which different teams use heavily to document their hypotheses, experiments, and conclusions resulting in mini scientific papers that provide insights into the work being done at OpenAI._

*Transcript*

Lukas Biewald - Okay, and final question, since this is our user conference and you are such a high profile user of OpenAI, I'm curious if you could say a little bit about how OpenAI uses Weights & Biases and if you have a favorite feature or part of Weights & Biases, we'd love to know about that.

Peter Welinder - Yeah, I mean, we use it for pretty much all of our model training. So just tracking them, I think there's a lot of just sharing of the fact that you can easily share training runs and stuff like that. It's a super-used feature.

But I think one thing that these days I do way, way less of that sort of work. So one of the features I really like is the ability to have reports and so on where people... So we use that quite heavily.

It depends a little bit on the team, but a number of teams are using that quite heavily to kind of really have a clear hypothesis.

Here's the hypothesis.
Here are the experiments that were run to kind of validate or invalidate that hypothesis.
Here's the conclusion.

You have all these mini scientific papers, essentially, on all of the stuff that's happening at OpenAI, which is incredibly interesting to kind of follow along with.

Lukas Biewald - Fantastic. That sounds very interesting.

OpenAI has finally released the most anticipated ChatGPT model today, called "gpt-3.5-turbo". Unlike the GPT 3.0 models that work like a search engine, ChatGPT model tracks the conversation to create more useful outputs. In this tutorial, I will go through every single detail on how to get started with the newest OpenAI ChatGPT API in Python.

📑 Python Script Used In The Video: https://wp.me/payCAw-1lw

💻 Build Your Own ChatGPT Desktop App: https://youtu.be/snkys9zXyD0

✉ Signup for an OpenAI account: https://openai.com/

📑 OpenAI GPT Model Pricing: https://openai.com/pricing

📑 openai.ChatCompletion reference: https://platform.openai.com/docs/guides/chat

📑 OpenAI ChatGPT Model (gpt3.5 turbo) Info : https://platform.openai.com/docs/models/gpt-3-5

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00:00 - Intro to OpenAI gpt 3.5 turbo model
00:34 - OpenAI GPT-3.5 Pricing difference vs GPT-3.0
01:51 - OpenAI GPT 3.5 models summary
03:03 - Sign up for an OpenAI account
03:29 - Create an OpenAI API Key
03:47 - Create Python script
04:15 - Install OpenAI Python package
04:27 - open.ChatCompletion library usage
11:00 - Track ChatGPT conversation
15:03 - Make the Python script runs like ChatGPT

#chatgpt #chatgptapi #openai #ai #python #gpt

Поддержать проект можно по ссылкам:
Если вы в России: https://boosty.to/vertdider
Если вы не в России: https://www.patreon.com/VertDider

А вы знали, что лазерный луч можно взвесить? С некоторыми оговорками, но можно, ведь вес — это не масса, а сила, и у потока фотонов она есть, правда очень небольшая и измерить её совсем не просто. Чтобы узнать, как именно ученым удалось это сделать, Дерек с канала Veritasium отправился в Национальный институт стандартов и технологий США, где ему заодно показали самый маленький эталон массы и уникальные электростатические весы.

Видео по теме:
Килограмм умер, да здравствует килограмм! [Veritasium]: https://youtu.be/Fj0txRnIMjw
США используют килограммы! [Veritasium]: https://youtu.be/vYSMoboq7oA
Самый круглый на свете объект [Veritasium]: https://youtu.be/iLXskyUgkAY

Перевод: Елена Смотрова
Редактура: Алексей Малов
Научная редактура: Леонид Вокин
Озвучка: Дмитрий Чепусов, Алексей Никитин
Монтаж звука: Андрей Фокин
Монтаж видео: Джон Исмаилов

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Разрешение на публикацию: https://bit.ly/2SFhrHZ
© https://www.youtube.com/c/veritasium/
Источник: https://youtu.be/pXoZQsZP2PY

1. Деякі пігменти вибірково поглинають УФ, тому вони можуть здаватися світлими у видимому світлі, але темними в УФ. Зазвичай пігменти розсіюють УФ-енергію як тепло, але також може відбуватися розрив внутрішньомолекулярних зв’язків.
2. Флуоресцентні молекули поглинають УФ випромінювання і повторно випромінюють цю енергію як видиме світло. Це робить їх темними в УФ діапазоні, і змушує світитися видимим світлом під дією УФ випромінювання.
3. Ультрафіолетове світло розсіюється сильніше, ніж видиме світло, оскільки довжина хвилі менша, а релеївське розсіювання обернено пропорційне довжині хвилі в четвертому степені.

✅Текст читали: Ігор Анісімов (AdrianZp) та Мар’яна Янкевич (MariAm Blog).
✅Переклад, редагування, субтитрування, монтаж: Віталій Шевчук | Цікава наука.

❕Підтримайте "Цікаву науку" на https://www.patreon.com/join/CikavaNauka або
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References:
Overview of main UV effects:
Visualizing Rayleigh Scattering through UV Photography
https://watermark.silverchair.....com/bams-d-14-00260_
Arctic animals are photographed in the UV to increase visibility and get an accurate count:
Lavigne, D. (1976). Counting Harp Seals with ultra-violet photography. Polar Record, 18(114), 269-277. doi:10.1017/S0032247400000310
Absorption spectrum of melanin: https://www.cl.cam.ac.uk/~jgd1000/melanin.html
"The spectroscopy of human melanin pigmentation," by N. Kollias. In: Melanin: Its Role in Human Photoprotection, pp. 31 - 38. Valdenmar Publishing Co. (1995).
"Optical properties of human sclera, and their consequences for transscleral laser applications," by A. Vogel, C. Dlugos, and R. Nuffer, Lasers in Surgery and Medicine 11(4), pp. 331 - 340 (1991).
"The incidence and time-course of latanoprost-induced iridial pigmentation as a function of eye color," by P. Wistrand, J. Stjernschantz, and K. Olsson, Survey of Ophthalmology 41(S2), pp. S129 - S138 (1997).

🔥Edureka Deep Learning Training - TensorFlow Certification:- https://www.edureka.co/ai-deep....-learning-with-tenso
This Edureka video on the "GPT-3 Explained" will help you understand why and what is GPT 3 Language Model, followed by its Use cases and limitations. Following Pointers are covered in this GPT-3 explained video:
00:00:00 Introduction
00:00:57 Introduction to GPT
00:02:50 Practical Applications of GPT Deep Learning Model
00:04:54 Datasets and Parameters used in GPT 3
00:06:44 Limitations of GPT Algorithm
00:08:14 How you can use GPT

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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.

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Who should go for this course?

The following professionals can go for this course:
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