Read the full list of tips: http://builder.io/blog/claude-code

MIT 14.01 Principles of Microeconomics, Fall 2023
Instructor: Prof. Jonathan Gruber
View the complete course: https://ocw.mit.edu/14-01F23
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

Prof. Gruber introduces the class by explaining microeconomics as the study of individuals and firms who make themselves as well off as possible in a world full of scarcity. He then explains the core concepts of supply and demand. Keywords: microeconomics, scarcity, constrained optimization, supply and demand

License: Creative Commons BY-NC-SA
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For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

November 11, 2025
This lecture covers agents, prompts, and RAG.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

Please follow along with the course schedule and syllabus: https://cs230.stanford.edu/syllabus/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

NOTE: There was no class on November 4, 2025 (Lecture 7). The previous lecture is Lecture 6.

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐒𝐚𝐥𝐞𝐬𝐟𝐨𝐫𝐜𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) : https://www.edureka.co/salesfo....rce-administrator-an
This video by Edureka will help you understand who is a Salesforce Administrator and what are their roles and responsibilities. It will also talk about how to become a Salesforce Administrator.

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🔥Professional Certificate Program in Generative AI and Machine Learning - IITG (India Only) - https://www.simplilearn.com/iitg-generative-ai-machine-learning-program?utm_campaign=y4wh1vbtYp8&utm_medium=DescriptionFirstFold&utm_source=Youtube
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In this, How To Integrate ChatGPT with WhatsAppvideo, we will learn how to connect ChatGPT, the powerful language model developed by OpenAI, with WhatsApp on a Windows operating system. This ChatGPT On WhatsApp tutorial will guide you through the step-by-step process of setting up the integration, including installing the necessary software and configuring the settings. By the end of this ChatGPT integration video, you can use ChatGPT to generate responses to WhatsApp messages in real-time. This will allow you to automate your messaging and save time, making it easier to handle large numbers of messages at once

In this ChatGPT Whatsapp Integration Video, We will cover the following topics:

00:00 Introduction to Integrate ChatGPT in WhatsApp
00:39 Demonstration of our Project
04:23 Simplilearn Course Promotion
05:05 Quiz Question
05:44 Download the required files from Git
06:13 Create a folder for our project
07:20 Install Python for the project
08:52 Install Golang for the project
11:54 Install and Import Libraries
14:33 Download and Install gcc compiler
16:53 Hands-on Demonstration

🔥Link to source code files: https://github.com/abhisarahuj....a/Integrate_ChatGPT_

🔥Link to download gcc compiler: https://jmeubank.github.io/tdm-gcc/

While announcing the chatbot, OpenAI wrote on its announcement page, “We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.”

#ChatGPT #IntegrateChatGPTWithWhatsApp #ChatGPTWhatsAppTutorial #WhatsAppIntegration #ChatGPTTutorial #ChatGPTPython #PythonTutorial #Python #PythonProjects #GoLang #GoLangProjects #PythonForBeginners #AI #Simplilearn

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⏩ Check out more Python videos: https://youtube.com/playlist?l....ist=PLEiEAq2VkUUJO27

➡️ About Post Graduate Program In AI And Machine Learning

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.

✅ Key Features

- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more
- Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools

✅ Skills Covered

- ChatGPT
- Generative AI
- Explainable AI
- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
- And Many More…

👉 Learn More At: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=20FebHowToIntegrateChatGPTWithWhatsapp&utm_medium=Description&utm_source=youtube

Fine-tuning GPT-3.
In this video we demonstrate how to use APIs and audio to create prompts and completions that can be used to fine-tune Transformers such as GPT-3. We show how to use a News API with Python to extract news articles and create a dataset for training and fine-tuning models.

We explain what prompt and completion datasets are, and the role they play in natural language processing in fine-tuning transformers. We then discuss using video and audio streams to build data pipelines for fine-tuning and transcribing speech to text using OpenAI's Whisper.

With over 500 hours of YouTube videos being uploaded every minute video is a rich source of detailed information with which to train AI.

The video first includes step-by-step demonstrations of using the News API to create a prompt and completion dataset and then shows how to build pipelines transcribing speech to text using Whisper.

Foundation models like GPT-3 can be 'Fine-Tuned'. This Fine_tuning allows them to learn the vocabulary and semantics of particular disciplines. If you want your AI solution to be familiar with options pricing algorithms, then you can show it lots of options pricing papers. If you'd like your AI to be able to converse and write about monetary policy then you can expose it to policy papers, books on economic theory, central bank meeting minutes and news conferences.

You train transformer AI models in a particular way. You don't simply feed in a file containing the text of the book or article that you are using to train the AI. You break the text up into a series of "Prompts" and "Completions". Prompts are the beginning of a passage of text and completions are what follow a prompt.

In this post we leverage the breadth and depth of content on YouTube. We propose a strategy to use selected videos to train our AI language models. These videos are chosen because they are rich in expertise in an area we want to train our language model in. The example used in this video is to train our AI in monetary policy by studying content generated by the Federal Reserve. We devise a strategy to use speech to text transcription of YouTube videos. We use OpenAI's 'Whisper' to perform this transcription. We then break this text up into 'prompts' and completions to train our model.

Following this approach we can easily train our NLP models to have expertise in any field we choose.



#NLP #Python #FineTuning #AI #MachineLearning #NewsAPI #Whisper #Lucidate #chatgpt #gpt3 #gpt-3 #openai #whisper #openai-whisper

Links:
Attention Mechanism in Transformers: https://youtu.be/sznZ78HquPc Transformer playlist: https://youtube.com/playlist?l....ist=PLaJCKi8Nk1hwaMU
Federal reserve Description: https://youtu.be/lJTmCv2kKKs
FOMC Feb 2023 Press Conference: https://www.youtube.com/watch?v=3Iv4aCN0OOo&t=2s
Python code for Transcriber class: https://github.com/mrspiggot/p....rompts_and_completio

=========================================================================
Link to introductory series on Neural networks:
Lucidate website: https://www.lucidate.co.uk/blog/categ...

YouTube: https://www.youtube.com/playlist?list...

Link to intro video on 'Backpropagation':
Lucidate website: https://www.lucidate.co.uk/post/intro...

YouTube: https://youtu.be/8UZgTNxuKzY

'Attention is all you need' paper - https://arxiv.org/pdf/1706.03762.pdf

=========================================================================
Transformers are a type of artificial intelligence (AI) used for natural language processing (NLP) tasks, such as translation and summarisation. They were introduced in 2017 by Google researchers, who sought to address the limitations of recurrent neural networks (RNNs), which had traditionally been used for NLP tasks. RNNs had difficulty parallelizing, and tended to suffer from the vanishing/exploding gradient problem, making it difficult to train them with long input sequences.

Transformers address these limitations by using self-attention, a mechanism which allows the model to selectively choose which parts of the input to pay attention to. This makes the model much easier to parallelize and eliminates the vanishing/exploding gradient problem.

Self-attention works by weighting the importance of different parts of the input, allowing the AI to focus on the most relevant information and better handle input sequences of varying lengths. This is accomplished through three matrices: Query (Q), Key (K) and Value (V). The Query matrix can be interpreted as the word for which attention is being calculated, while the Key matrix can be interpreted as the word to which attention is paid. The eigenvalues and eigenvectors of these matrices tend to be similar, and the product of these two matrices gives the attention score.

=========================================================================

#ai #deeplearning #chatgpt #gpt3 #neuralnetworks #attention #attentionisallyouneed

This video is a crash course on understanding how finetuning on LLM models can be performed uing QLORA,LORA, Quantization using LLama2, Gradient and Google Gemma model. This crash course includes both theoretical intuition and practical intuition on making you understand how we can perform finetuning.

Timestamp:

00:00:00 Introduction
00:01:20 Quantization Intuition
00:33:44 Lora And QLORA Indepth Intuition
00:56:07 Finetuning With LLama2
01:20:16 1 bit LLM Indepth Intuition
01:37:14 Finetuning with Google Gemma Models
01:59:26 Building LLm Pipelines With No code
02:20:14 Fine tuning With Own Cutom Data

Code Github: https://github.com/krishnaik06/Finetuning-LLM
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Support me by joining membership so that I can upload these kind of videos
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►Generative AI On AWS:https://www.youtube.com/watch?v=2WOa4_3Bgtw&list=PLZoTAELRMXVP5zpBfH7pab4aB1LbmCM1z
►Fresh Langchain Playlist: https://www.youtube.com/watch?v=KmQOlg5YfU0&list=PLZoTAELRMXVOQPRG7VAuHL--y97opD5GQ
►LLM Fine Tuning Playlist: https://www.youtube.com/watch?v=Vg3dS-NLUT4&list=PLZoTAELRMXVN9VbAx5I2VvloTtYmlApe3
►AWS Bedrock Playlist: https://www.youtube.com/watch?v=2maPaQutcWs&list=PLZoTAELRMXVP8-wzKPtrRST3jNCprvMZj
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►Google Gemini Playlist: https://www.youtube.com/watch?v=it0l6lx3qI0&list=PLZoTAELRMXVNbDmGZlcgCA3a8mRQp5axb&pp=gAQBiAQB
►Langchain Playlist: https://www.youtube.com/watch?v=4O1rs7mrNDo&list=PLZoTAELRMXVORE4VF7WQ_fAl0L1Gljtar&pp=gAQBiAQB
►Data Science Projects:
https://www.youtube.com/watch?v=S_F_c9e2bz4&list=PLZoTAELRMXVPS-dOaVbAux22vzqdgoGhG&pp=iAQB

►Learn In One Tutorials

Statistics in 6 hours: https://www.youtube.com/watch?v=LZzq1zSL1bs&t=9522s&pp=ygUVa3Jpc2ggbmFpayBzdGF0aXN0aWNz

End To End RAG LLM APP Using LlamaIndex And OpenAI- Indexing And Querying Multiple Pdf's

Machine Learning In 6 Hours: https://www.youtube.com/watch?v=JxgmHe2NyeY&t=4733s&pp=ygUba3Jpc2ggbmFpayBtYWNoaW5lIGxlYXJuaW5n

Deep Learning 5 hours : https://www.youtube.com/watch?v=d2kxUVwWWwU&t=1210s&pp=ygUYa3Jpc2ggbmFpayBkZWVwIGxlYXJuaW5n

►Learn In a Week Playlist

Statistics:https://www.youtube.com/watch?v=11unm2hmvOQ&list=PLZoTAELRMXVMgtxAboeAx-D9qbnY94Yay

Machine Learning : https://www.youtube.com/watch?v=z8sxaUw_f-M&list=PLZoTAELRMXVPjaAzURB77Kz0YXxj65tYz

Deep Learning:https://www.youtube.com/watch?v=8arGWdq_KL0&list=PLZoTAELRMXVPiyueAqA_eQnsycC_DSBns

NLP : https://www.youtube.com/watch?v=w3coRFpyddQ&list=PLZoTAELRMXVNNrHSKv36Lr3_156yCo6Nn
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AI Agents Explained: A Comprehensive Guide for Beginners by Alfie Marsh Co-Founder & CEO of https://www.getstation.ai/

(0:00) Introduction to AI Agents
(0:23) What is an AI Agent?
(0:49) How AI Agents Differ from Traditional Software
(1:36) AI Agents vs Large Language Models (LLMs)
(2:50) How AI Agents Work
(3:16) Component 1: Planning
(3:47) Component 2: Interacting with Tools
(4:10) Component 3: Memory and External Knowledge
(5:07) Component 4: Executing Actions
(5:39) Risks and Future of AI Agents
(6:30) Conclusion

In this video, Alfie Marsh, Co-Founder & CEO of Station AI, unpacks the world of AI agents and explains how they are evolving to become an integral part of our lives. Discover what AI agents are, how they differ from traditional automations and other large language models (LLMs) like GPT, Claude, and Gemini, and explore real-world examples of AI agents in action.

Learn about the key components that make up AI agents, including their ability to plan, interact with tools, store memory, access external knowledge, and execute actions autonomously. Alfie also discusses the potential risks and the future of AI agents as they become more sophisticated with advancements in language models like GPT-4 and beyond.

Whether you're interested in building AI agents, understanding how they work, or exploring no-code solutions and tutorials, this video provides a comprehensive overview of AI agents and their growing importance in our lives and careers.

Agents are everywhere these days, but with so much information available, it’s easy to feel overwhelmed. Most tutorials and videos focus on specific frameworks without covering the fundamental principles behind these systems.

This course takes a different approach. Over four modules, you'll learn to implement the four core agentic patterns from scratch, using just Python and Groq LLMs:

* Reflection Pattern
* Tool Use Pattern
* Planning Pattern
* Multi-Agent Pattern

Explore the written lessons on my Substack blog:

https://theneuralmaze.substack.com/

Check out the code here:

https://github.com/neural-maze/agentic_patterns

––––––––––––––––––––––––––––––

If you like this content, you can also follow me here:

📩 Substack - https://theneuralmaze.substack.com/
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💻 GitHub - https://github.com/MichaelisTrofficus
🐦 Twitter - https://x.com/moteropedrido

––––––––––––––––––––––––––––––

––––––––––––––––––––––––––––––

0:00 Introduction
2:48 Module 1 - Reflection Pattern
20:40 Module 2 - Tool Pattern
44:22 Module 3 - Planning Pattern
1:13:16 Module 4 - MultiAgent Pattern
1:41:00 Conclusion

––––––––––––––––––––––––––––––
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For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

February 6, 2026
This lecture covers:
• A randomized controlled trial examining the impact of AI-mediated feedback on students' disciplinary writing performance and learning
• An introduction to and evaluation of FeedbackWriter
• How knowledge engineering can enhance cognitive fidelity and enable reliable feedback generation

To follow along with the seminar schedule, visit: https://hci.stanford.edu/

Xu Wang is an Assistant Professor in Computer Science and Engineering and the School of Information (By courtesy) at the University of Michigan.

https://github.com/aamini/introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning - aamini/introtodeeplearning

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Transformers Rise Of The Dark Spark Full Movie Cinematic (2022) 4K ULTRA HD Action

New Clips 2022!

Subscribe To Gameclips To Catch Up All The Best Clips.

Gameclips always brings you the best full game movies with a focus on the story cinematic aspect of the video games. On this channel you will find the best cinematic moments and clips from the best video games with added educational narrative storylines to the videos. You will also find full edited let's play walkthroughs and guides to the latest and best new games coming out.

** RPA Training: https://www.edureka.co/robotic....-process-automation- **
This session on UiPath Studio will give you an overview of UiPath Studio and its components.
Below are the topics covered in the video:
1:00 What is RPA?
3:05 RPA Tools
3:35 Introduction To UiPath Studio
4:09 UiPath Studio Installation
6:35 Projects in UiPath
10:47 UiPath Studio Components
11:32 Ribbon Components
21:03 Activity Components
25:13 Demo

RPA Playlist: https://bit.ly/2B53HLe
RPA Blog Series: https://bit.ly/2Ay1bzG

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

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How does it work?

1. This is a 4 Week Instructor-led Online Course, 25 hours of assignment and 20 hours of project work
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 work on a project, based on which we will provide you a Grade and a Verifiable Certificate!

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

About the Robotic Process Automation Course

Edureka’s RPA training helps you to understand the concepts around Robotic Process Automation using the leading RPA tool named ‘UiPath’. Robotic Process Automation is the automation of repetitive and rules-based human tasks working with the software applications at the presentation/UI level i.e. no software integrations are needed at middleware, server or database levels. In this course, you will learn about the RPA concepts and will gain in-depth knowledge on UiPath tool using which you will be able to automate real-world processes at the enterprise level such as Insurance Claims Processing, Accounts Payable / Purchase Orders Processing, Invoice Processing, Complaints Management, Customer Feedback Analysis, Employee Onboarding, Compliance Reporting, and many more.

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What are the Objectives of our Robotic Process AutomationTraining?

After completing this course, you will be able to:

1. Know about Robotics Process Automation and their working
2. Assess the key considerations while designing an RPA solution
3. Work proficiently with the leading RPA tool ‘UiPath
4. Have practical knowledge on designing RPA solutions using UiPath
5. Perform Image and Text automation
6. Learn Data Manipulation using variables and arguments
7. Create automation with applications running in Virtual Environments
8. Debug and handle exceptions in workflow automation

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Why learn Robotic Process Automation?

Robotic Process Automation (RPA) is an automation technology for making smart software by applying intelligence to do high volume and repeatable tasks that are time-consuming. RPA is automating the tasks of a wide variety of industries, hence reducing the time and increasing the output.
Some of the facts about RPA include:

1. A 2016 report by McKinsey and Co. predicts that the robotic process automation market could be worth $6.7 trillion by 2025
2. A major global wine business, after implementing RPA, increased the order accuracy from 98% to 99.7% while costs reduced to Rs. 5.2 Crore
3. A global dairy company used RPA to automate the processing and validation of delivery claims, reduced goodwill write-offs by Rs. 464 Million

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What are the pre-requisites for this course?

To master the concept of RPA, you need to have a basic understanding of :

1.Basic programming knowledge of using if/else and switch conditions
2.Logical thinking ability

Got a question on the topic?
Please share it in the comment section below and our experts will answer it for you.

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 interactive session on "Top Networking Commands," we will look at some of the preferred commands applied in the window's command prompt application to access information and configure network settings for our respective devices. In this demo session, you will understand the execution and the functioning of multiple network commands for better handling of our systems.

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Dive into the future of AI with our Generative AI & Machine Learning course, in collaboration with E&ICT Academy, IIT Guwahati. Learn tools like ChatGPT, OpenAI, Hugging Face, Python, and more. Join masterclasses led by IITG faculty, engage in hands-on projects, and earn Executive Alumni Status.

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In this video, I dive into the world of agentic AI, a concept that's set to be a major buzzword in 2025. We explore how agentic AI combines multiple agents—some of which are language models (LLMs) and others that are tools—to create more sophisticated and effective AI workflows. We walk through a simple example of how an AI agent can draft a marketing plan, another can critique it, and a third can refine it based on the feedback. We also discuss how some agents can be tools, like a Google search to gather data, and how an orchestrator agent can manage the entire process, adjusting steps as needed.

Leave me a comment if you have new topics I should be discussing.

Check out my LinkedIn: https://www.linkedin.com/in/donwoodlock/

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YouTube Timestamps
0:00 – 0:20 Welcome
0:21 – 0:50 Introduction to the concept of Agentic AI
0:51 – 3:50 Explanation of how Agentic AI works
3:50 – 4:08 Advertisement plug-in
4:08 – 5:20 Example of using compound LLM’s
5:21 – 5:55 Why you should use a compound LLM approach
5:57 – 6:45 Best way to train and use LLM’s for optimal outcome
6:45 – 7:40 How to think of LLM as agents
7:41 – 10:05 Not every agent needs to be an LLM
10:06 - 10:50 Possibility of having an orchestrator agent
10:51 – 12:50 How to use these agents
12:51 – 13:26 Closing remarks

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Think you're getting the most out of Claude Code? You're probably just scratching the surface. In this video, we break down the 7 levels of mastery, guiding you from basic prompting all the way to building fully autonomous systems. Stop the trial-and-error and start unlocking Claude Code's true potential today.

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21:21 - Level 4: Connecting Claude Code to Your Apps (MCPs)
26:18 - Level 5: Move from Executor to Supervisor
29:34 - Level 6: Agent Teams
35:57 - Level 7: Fully Autonomous Systems


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