eep learning networks are mechanical systems which “learn” by modeling high-level abstractions in datasets, and cycling through trial-and-error guesses with feedback, to establish an optimally-weighted system that can make accurate predictions about new data. LSTM RNNs (long short term memory Recurrent Neural Nets) is a deep learning method that overcomes some of the limitations in classical time series forecasting (non-linearity, fixed-time windows, time lag specification, and multivariate forecasting). Hence, deep learning is emerging as an important forecasting technique for coping with exponentially growing data, and the ability to expediently process and direct these data. In the future, deep learning smart networks might provide an advanced computational infrastructure for tackling real-time predictive data science problems such global health monitoring, energy storage and transmission, and financial risk assessment. Slides: https://www.slideshare.net/lab....logga/deep-learning-

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This is a complete course on learning Generative ai and agentic with Langchain and Langgraph. We have included all the topics from RAG, vectorless rag, Deep agents, Guardrails, LLM Evaluation and LLM Gateways Techniques
Github Links
Langchain : https://github.com/krishnaik06..../Langchain-V1-Crash-
Langgraph: https://github.com/krishnaik06..../Agentic-LanggraphCr
RAG: https://github.com/krishnaik06/RAG-Tutorials
Vectorless RAG: https://github.com/krishnaik06/RAG-Tutorials/blob/main/PageIndex_Vectorless_RAG_CrashCourse%20(1).ipynb
Deep Agents: https://drive.google.com/file/....d/1SVjvgqvKfF-FPAIqp
Guardrails : https://github.com/krishnaik06..../Langchain-V1-Crash-/blob/main/updatedlangchain/langchain_guardrails_crash_course.ipynb
LLM Evals : https://github.com/krishnaik06/RAG-Tutorials/blob/main/1-rag_evaluation.ipynb
LLM Gateways: https://github.com/krishnaik06..../Langchain-V1-Crash-/blob/main/llm_gateway_tutorial.ipynb

Timestamp
00:00:00 Introduction
00:02:31 Langchain Course
02:35:12 Langraph Course
05:02:29 RAG Course
07:10:43 Vectorless RAG
08:02:11 Deep Agents
08:45:43 Guardrails
09:22:55 LLM Evaluation
10:30:25 LLM Gateways
-------------------------------------------------------------------
Learn from us visit https://krishnaik.in/liveclasses

Generative AI vs AI Agents vs Agentic AI Explained | Complete Beginner's Guide (2026)

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The content has been transformed, repurposed, and presented with new perspective and value.

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🌟 Build Your Command Center in Notion - https://www.pressplay.cc/link/s/F8236D6B

Create your first AI agent in three simple steps using n8n! This tutorial shows you how to create an automated subscription tracker that processes natural language input and updates Google Sheets automatically.

You'll learn to configure the agent's brain (chat model and memory), connect tools like Google Sheets, and write system prompts that control agent behavior. Perfect for non-technical users ready to transition from using AI tools to actually building with them.

*TIMESTAMPS*
00:00 Build Your First AI Agent
00:29 Refresher on AI Agents
01:20 Getting Started on n8n
02:54 Why Bother?
03:32 Step 1: Building the Brain
08:21 Step 2: Connecting the Tools
10:38 Step 3: Activating the Brainstem
12:21 Testing our AI Agent
14:56 Beyond the Basics

*RESOURCES MENTIONED*
OpenAI API Setup: https://platform.openai.com/
ChatGPT System Prompt Template: https://www.jeffsu.org/build-y....our-first-ai-agent-i
Notion Command Center System: https://www.pressplay.cc/link/s/F8236D6B

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#aiagents #ai #automation

In this video we will discuss about the differences between generative ai vs agentic ai vs AI agents.
Agentic AI is an AI system that acts autonomously, adapts in real time, and solves multi-step problems based on context and objectives.
-----------------------------------------------------------------------------------------------------
Join our 2.0 Live Agentic AI With MCP Batch starting from May 10th 2025
Course Enrollment Link: https://www.krishnaik.in/livec....lass/2.0%20Live%20Ag
Reach out to Krish Naik's counselling team on 📞 +919111533440 or +91 87885 03778 in case of any queries we are there to help you out.

Build your own functional AI coding agent from the ground up using Python and the free Gemini Flash API. This project-based tutorial provides a deep understanding of how powerful AI tools work by guiding you through the creation of an agentic loop powered by tool calling. You will implement the core abilities for your agent to interact with and modify a codebase, including reading files, writing to files, and executing code to get feedback.

Lane Wagner created this course.

Check out the interactive version of the course on boot.dev: https://www.boot.dev/courses/build-ai-agent-python

❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

⭐️ Contents ⭐️
- 00:00:00 Introduction
- 00:01:14 Why Build an AI Agent?
- 00:01:49 Course Overview & What We're Building
- 00:02:25 How to Follow Along
- 00:03:47 What is an AI Agent? (Agentic Loops & Tool Calling)
- 00:06:03 The Agent's Four Tools
- 00:07:58 Prerequisites & Project Goals
- 00:10:08 Demo: Agentic vs. One-Shot Responses
- 00:13:07 Python Project Setup with UV
- 00:15:44 Getting Started with the Gemini API
- 00:19:21 Making Your First API Call
- 00:24:44 Accepting Command-Line Arguments
- 00:27:46 Managing Conversation History
- 00:30:39 Adding a Verbose Flag for Debugging
- 00:33:35 Setting Up the Project for Our Agent (Calculator App)
- 00:36:23 Building Tool #1: Get Files Info
- 00:49:39 Building Tool #2: Get File Content
- 00:58:24 Building Tool #3: Write File
- 01:05:26 Security Note: Dangers of Running AI-Generated Code
- 01:07:30 Building Tool #4: Run Python File
- 01:18:00 Understanding the System Prompt
- 01:33:10 How Tool Calling Works: Declaring Functions for the LLM
- 01:41:38 Adding All Function Declarations
- 01:49:19 Implementing the Function Calling Logic
- 01:57:30 Creating the Agentic Loop
- 02:07:11 Final Demo: Agent Fixes a Bug Autonomously
- 02:13:44 Conclusion & Next Steps

🎉 Thanks to our Champion and Sponsor supporters:
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Warp is free to try but for a limited time, you can try Warp Pro free for 7 days with 2,500 AI credits - no card required. Use my link https://go.warp.dev/forrestytcow

Everyone seems to be using AI to code now, and in this video, I'm showing you how to actually code with AI - not vibe code. Because there's a huge difference between software developers who strategically guide AI, and vibe coders who just let it take the wheel.

0:00 Everyone’s Coding with AI
0:32 Stop Repeating Yourself
1:45 Prompting is Important
4:21 The Software Development Workflow
6:42 Pair Programming
7:57 Implement Multiple Agents
9:25 git worktree
9:57 Extra Stuff AI Can Help With
11:21 Pull the Plug
11:58 Always Remember This

This video is sponsored by Warp.

If you're a developer, sign up to my free newsletter Dev Notes 👉 https://www.devnotesdaily.com/

If you're a student, checkout my Notion template Studious: https://notionstudent.com

Don't know why you'd want to follow me on other socials. I don't even post. But here you go.
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Deploy your site using here.now completely for free, copy the prompt for your agent here! https://here.now/r/twt

In this complete guide, I walk you through the best local agentic coding workflow using LM Studio and VS Code - no API keys, no cloud, no cost per token.You'll learn how to install and configure LM Studio, choose and run the right local models for coding, and connect everything to VS Code so you can use AI-powered agents directly in your editor. This is the exact setup I use for local AI coding that actually works.

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

🚀 Tools I Use
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Openclaw setup: https://www.hostinger.com/techwithtim
VPS setup: https://www.hostinger.com/techwithtim10
Wispr Flow (Best AI Dictation): https://ref.wisprflow.ai/TechWithTim-jun26

🎞 Video Resources 🎞
Presentation in This Video: https://clever-mirage-d82v.here.now/
LM Studio Download: https://lmstudio.ai/
VSCode Download: https://code.visualstudio.com/

⏳ Timestamps ⏳
00:00 | Overview
00:46 | What are Local Models
02:30 | VRAM & Computer Hardware
05:18 | Memory & Model Speed
06:39 | How to Pick a Model
11:10 | Trying It Live
12:23 | LM Studio Setup & Model Download
22:15 | Using Local Models in VS Code
28:43 | Autocomplete Model Setup

Hashtags
#LMStudio #AICoding #AIDevTools

UAE Media License Number: 3635141

01 Song - Vellinilaa Thullikalo 00:00
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Dive with us into the enigmatic world of Artificial General Intelligence (AGI)! 🌐 From Microsoft's game-changing Gorilla AI 🦍, exhibiting human-like cognition, to the art of crafting an AGI's digital brain 🧠. Ever wondered how close we are to welcoming AGI into our daily lives? The answer might surprise you! Explore AGI's vast potential applications, from healthcare 🩺 to space 🚀, and the challenges that lie ahead. Ensure you're equipped for the tech revolution; hit like 👍, share, and subscribe 🔔 for a front-row seat to the future. Let's decode tomorrow, today! 🚀

🔔 Subscribe for more insights into the ever-evolving world of AI and digital content. Drop a comment below with your thoughts on YouTube's AI transformation!
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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐓𝐚𝐛𝐥𝐞𝐚𝐮 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) : https://www.edureka.co/tableau....-certification-train
In this Tableau YouTube live session, we will be using a SuperStore data to figure out the future strategies for sales and better customer targeting by analyzing the customer buying trends & different products in the superstore.

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

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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.

🔥Edureka Tensorflow Training - https://www.edureka.co/ai-deep....-learning-with-tenso
This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.

Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE

00:00 Introduction
01:00 Why Neural Networks
02:24 Motivation Behind Neural Networks
04:43 What are Neural Networks
06:39 How it works
13:42 Activation Functions
17:12 Multi Layer Perceptron
20:51 Training a Neural Network
24:36 Hands On

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

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

🔵DevOps Engineer Masters Program: https://bit.ly/2B9tZCp
🟣Cloud Architect Masters Program: https://bit.ly/3i9z0eJ
🔵Data Scientist Masters Program: https://bit.ly/2YHaolS
🟣Big Data Architect Masters Program: https://bit.ly/31qrOVv
🔵Machine Learning Engineer Masters Program: https://bit.ly/388NXJi
🟣Business Intelligence Masters Program: https://bit.ly/2BPLtn2
🔵Python Developer Masters Program: https://bit.ly/2Vn7tgb
🟣RPA Developer Masters Program: https://bit.ly/3eHwPNf

---------------------------------------Edureka Post Graduate Courses-------------------------------------------

🔵 Artificial and Machine Learning PGD: https://bit.ly/3AylL0q

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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!

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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.

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Who should go for this course?

The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers
3. Business Analysts
4. Information Architects
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies

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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).

Learn Next.js by building a custom API with MongoDB using the latest app router. You will build an authentication system that can register the user, login, create a secure cookie with JWT Token, use email service, verify the user's email, forget password, middleware route protection, and much more. Also, you will learn about integrating Appwrite for the back end. Finally, you will learn about deploying everything to Vercel.

✏️ Course developed by @HiteshChoudharydotcom

💻 Code
https://github.com/hiteshchoud....hary/nextjs-fullstac
https://github.com/hiteshchoud....hary/nextjs-appwrite

⭐️ Contents ⭐️
(0:00:00) Start, basics and Prerequisites
(0:04:56) Project structure, diagrams and tech stack
(1:09:07) Signup and Login
(2:10:44) Middleware in Nextjs
(2:52:22) User verification with email
(3:48:26) Nextjs deployment with database connectivity
(4:01:50) Nextjs meets Appwrite
(4:07:31) How to integrate Appwrite to nextjs
(4:44:02) Nextjs component to talk to Appwrite
(5:14:24) Appwrite nextjs and context api
(5:53:52) What’s next after this

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

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