I start wathing when i was single....now i have wife and 2 kids and still watching

wow, dropping a 30 hours course, just above and beyond..

This is amazing, 30 hours of free knowledge right at our fingertips. Thank you so much everyone, you are truly making an impact in people’s lives.

00:25 *🎓 Introduction to Generative AI Community Session* 02:14 *📚 Dashboard Walkthrough and Enrollment Process* 04:19 *📋 Curriculum Overview and Instructor Introduction* 07:42 *📊 Detailed Curriculum Breakdown* 16:49 *🧠 Preparing for Generative AI and LLM Introduction* 23:26 *🧠 Basics of Deep Learning* 27:52 *🤖 Neural Network Architectures Overview* 32:07 *📊 Introduction to Generative AI* 33:28 *📝 Generative AI Models and Applications* 46:12 *🧠 Overview of Text and Image Generation* 56:31 *🔄 Addressing Sequence Mapping Issues* 01:11:52 *🧠 Understanding the Transformer Architecture* 01:17:46 *🔄 Discriminative vs Generative Models* 01:25:00 *🚀 Milestones in Large Language Models (LLMs)* 01:44:02 *📚 Accessing Resources and Engaging with the Community* 01:53:42 *🚀 Overview of Course Enrollment and Dashboard Setup* 01:56:15 *📝 Agenda for Today's Session* 02:06:02 *🌐 Introduction to OpenAI: History, Goals, and Milestones* 02:17:19 *📊 Introduction to OpenAI's API* 02:28:29 *💼 Job Opportunities and Interview Insights* 02:38:19 *📊 Checking Environment and Installing Jupyter Notebook* 02:57:50 *💻 Using the OpenAI Playground and setting up API keys* 03:04:15 *🎮 Exploring the OpenAI Playground and its functionalities* 03:10:47 *🤖 Understanding chat completion API and function calling* 03:29:08 *💡 Understanding tokenization and pricing in OpenAI API.* 03:41:05 *🧩 Prompt Templating with Hugging Face* 03:44:03 *💻 Project Development Setup and Flask Integration* 04:01:10 *🖥 Calling Chat Completion API* 04:22:04 *🖥 Extracting Textual Descriptions Using API Calls* 04:42:10 *📝 Extracting Origin and Destination from Arguments* 04:43:59 *🛫 Retrieving Flight Details* 05:03:21 *💻 Introduction to Function Calling and API Integration* 05:10:05 *🤖 Utilizing Len Chain for AI Prompting* 05:34:50 *🧩 Features and Capabilities of Len Chain* 05:42:40 *🎓 Practical Implementation and Use Cases* 05:49:34 *📝 Creating Prompt Templates* 05:57:15 *🤖 Utilizing Agents for Real-time Information Extraction* 06:09:52 *🛠 API Access and Documentation* 06:13:07 *🖥 Implementing API Calls in Code* 06:15:24 *📊 Real-Time Information Retrieval with Sur API* 06:17:18 *📚 Accessing Wikipedia Information* 06:24:10 *📄 Exploring Langchain Documentation* 06:47:00 *🔗 Sequential Chaining in LLM* 06:52:10 *🧩 Utilizing Sequential Chains in AI Strategy* 07:24:41 *🛠 Len chain project implementation agenda* 08:21:35 *🤖 Setting up Hugging Face Hub and accessing models* 08:30:38 *📦 Installation and library setup for Hugging Face* 08:42:01 *📚 Understanding open-source model usage* 08:46:18 *📝 Steps to download and utilize models locally* 08:54:42 *🤖 Introduction to using LennChain for generating text* 08:58:12 *💡 Exploring different prompts and models for text generation* 09:06:32 *🚀 Announcement of upcoming sessions and resources for learning* 09:25:11 *🚀 Publishing Code to GitHub* 09:37:01 *💻 Setting up Virtual Environment and .gitignore* 09:42:00 *📋 Listing Project Requirements in requirements.txt* 09:48:43 *📦 Structuring Local Package with init.py* 09:58:41 *💻 Installing Python packages and managing dependencies* 10:06:02 *📂 Exploring package metadata and project structure* 10:10:07 *🛠 Configuring API keys and environment variables* 10:24:27 *🧩 Setting Up the Environment for LLM Integration* 10:30:24 *🔗 Creating a Prompt Template for Quiz Evaluation* 10:34:46 *🤖 Connecting Components Using LLM Chain and Sequential Chain* 10:40:25 *🛠 Loading Data and Reading Text from a File* 10:46:41 *🛠 Explaining the usage of OpenAI callback for token tracking in LlamaChain* 11:06:46 *📁 Project setup and agenda overview* 11:11:09 *🛠 Adding files to project structure* 11:28:38 *🔒 Setting Up Logging in Python* 11:41:24 *📝 Using GitHub and Neuro Lab for Project Development* 11:51:33 *🛠 Setting up Virtual Environment and Installing Requirements* 11:58:36 *💻 Setting up Project Files and Importing Dependencies* 12:24:59 *🛠 Finalizing Application Structure* 12:43:31 *🧩 Generating MCQs and Displaying Results* 13:02:00 *📊 Demo and Troubleshooting Streamlit Application* 13:04:26 *🖥 Explanation of application UI and code structure* 13:06:31 *🚀 Tomorrow's agenda: Deployment and introduction to Vector Databases* 13:12:12 *📊 Generating MCQs from text data* 13:16:22 *💻 Preparation for AWS EC2 instance deployment* 13:23:22 *🚀 Starting with Generative AI* 13:24:06 *🛠 Setting up AWS EC2 Instance* 13:37:04 *📂 Cloning and Configuring Repository* 13:40:55 *🗝 Managing Environment Variables* 13:44:44 *🚀 Running the Application* 13:49:09 *📄 Saving generated quizzes in various formats* 13:53:48 *🚀 Deployment process overview and instructions* 14:09:47 *🧮 Understanding Vectors in 2D and 3D Spaces* 14:15:08 *📊 Encoding Techniques for Data Representation* 14:19:06 *📝 Introduction to Embedding Concept* 14:32:02 *🧠 Understanding Vocabulary and One-Hot Encoding* 14:47:39 *📊 Introduction to Vector Databases and Practical Implementation* 14:53:41 *📚 Enrollment Process and Dashboard Overview* 15:00:41 *💻 Accessing Additional Resources and Video Recordings* 15:20:15 *📉 Challenges with Traditional Databases* 15:38:08 *🗃 Use Cases of Vector Databases* 15:57:27 *🖥 Setting up code sharing and library installation* 16:00:56 *📁 Creating and uploading folders/files, troubleshooting file size* 16:06:52 *📑 Loading PDF data, text chunking, and preprocessing* 16:19:34 *📐 Extracting Embeddings with OpenAI* 16:29:37 *💻 Exploring Pinecone Features and Pricing* 16:42:28 *🧬 Implementing Similarity Search with Vector Databases* 17:11:45 *📝 Course Curriculum Overview* 17:19:32 *💻 Setting Up and Exploring Chroma DB in Jupyter Notebook* 17:24:31 *🔍 Overview of Chroma DB on GitHub* 17:26:35 *📚 Exploring Chroma DB Documentation* 17:47:23 *📊 Generating Embeddings and Using OpenAI Model* 17:51:27 *🗝 Setting Up OpenAI API Key* 17:55:06 *📚 Importing Libraries and Loading Data* 18:09:37 *📚 Chunking Data for Large Text Input* 18:22:12 *💽 Creating Embeddings and Storing Locally* 18:28:38 *🔄 Retriever Creation and Document Retrieval* 18:51:27 *🛠 Overview of project flow and data handling:* 19:08:27 *📊 Understanding Model Costs* 19:16:20 *🛠 Requirements for Open Source Models* 19:27:11 *🧠 Understanding Llama 2* 19:38:00 *🦙 Using the Llama 2 Model for Chat* 20:21:19 *📝 Generating responses using LLM model* 20:27:14 *🛠 Setting up LLM model with Hugging Face pipeline* 20:46:21 *🛠 Using Prompt Templates and LLM Chain* 21:12:12 *🛠 Architecture Overview* 21:18:24 *🧩 Technology Stack* 21:55:15 *📚 Setting Up Pinecone for Vector Database* 21:59:30 *📚 Loading PDF Data and Creating Text Chunks* 22:20:19 *📚 Storing Text Embeddings in Pinecone Vector Database* 22:39:15 *📂 Setting Up Project Structure* 23:16:21 *🗂 Setting Up Environment Variables and Code Structure* 23:19:13 *📝 Converting Notebook Code to Modular Components* 23:22:02 *📦 Integrating Third-Party Libraries and Dependencies* 23:24:26 *🛠 Storing Data in a Vector Database* 23:42:54 *🌐 Developing Frontend Components with Flask* 23:44:20 *📦 Setting up environment and dependencies* 23:50:36 *🌐 Creating web interface and user interaction* 24:13:51 *🎓 Information about Generative AI Course* 24:36:59 *🤖 Comparison Between Google B and Gemini* 24:13:51 *🎓 Information about Generative AI Course* 24:22:13 *📑 Agenda for Google Gemini LLM Session* 24:29:41 *🔍 Understanding Google Gemini LLM Model* 24:36:59 *🤖 Comparison Between Google B and Gemini* 25:03:58 *🧾 Automating Invoice Data Extraction with LLM* 25:07:01 *🛠 Setting up Environment and Installing Dependencies* 25:08:52 *📝 Planning and Architecture for Invoice Extraction Application* 25:25:12 *🚀 Initializing Streamlit App and User Interaction* 25:29:36 *🖥 Discussing Input Information for Response Generation* 25:39:39 *🎓 Conclusion and Course Promotion* 26:02:22 *🎓 Exploring Course Documentation* 26:03:09 *🤝 Appreciation and Course Selection* 26:05:52 *📚 Guidance for Learning Paths* 26:11:37 *💼 Career Path Discussion* 26:13:52 *🌍 Appreciation and Enrollment Queries* 26:42:55 *📊 Executing SQLite insertions and displaying records* 26:46:28 *🧠 Configuring and loading generative AI model for query generation* 26:50:09 *🗃 Creating functions for retrieving queries from the database* 26:53:10 *🚀 Setting up Streamlit app and defining prompts* 26:59:13 *💻 Setting Up Streamlit App for SQL Query Processing* 27:24:17 *🛠 Debugging SQL Query Execution* 27:36:18 *🗃 Advanced SQL queries and nested conditions* 27:46:30 *💬 Q&A Session and Course Information* 27:59:21 *🏢 Discussion on industry experience and project-based learning* 28:21:32 *📚 Overview of Future Capabilities* 28:43:00 *🧠 Understanding Generative AI* 28:49:06 *🎨 Generative Models vs. Discriminative Models* 28:57:46 *💡 Training LLM Models* 29:12:23 *⚡ GPU Infrastructure Costs and Carbon Emissions* 29:15:35 *📊 Training Metrics and Considerations* Made with HARPA AI

Have never seen such a MEGA tutorial session. We're forever blessed with such kind hearts. Glorious blessings to you guys.

Respect to whoever finishes this 30 hours… I’m right behind y’all I swear!

This channel is a pure gold bro

This is gold! Thank for all the instructors❤

Woah. Man just needs time. 30 hrs. Well done guys.

Krish Naik is the MAN , thanks for posting!

Beyond awesome, thanks a lot, 30 hour course makes a difference!

Sunny is a great instructor. Well done and thank you

perfect, 00:25 Introduction to Generative AI Community Session 02:14 Dashboard Walkthrough and Enrollment Process 04:19 Curriculum Overview and Instructor Introduction 07:42 Detailed Curriculum Breakdown 16:49 Preparing for Generative AI and LLM Introduction 23:26 Basics of Deep Learning 27:52 Neural Network Architectures Overview 32:07 Introduction to Generative AI 33:28 Generative AI Models and Applications 46:12 Overview of Text and Image Generation 56:31 Addressing Sequence Mapping Issues

Me parece perfecto! mientras mas dure es mejor... Disfruto los videos largos y educativos... Espero finalizar este curso de manera correcta. Gracias...

This is very generous of these two brothers to share such world knowledge of the now in later

The best course so far on Generative AI for free. I have just started with this one the day it was uploaded. Kudos to the whole team!!

2 week course in one video! Awesome!

This is epic! I need to take 5 days' leave and follow this undisturbed, non-stop!

I am watching for 1.5 hour and I have to say this is an amazing resource. Thank you

Krish Naik & iNeuron, I’ve been learning from you guys the past 4 years. ❤

😮 30 hours. Thanks for the timestamps

Thank you Sunny for your passion in sharing the knowledge. You have taken great effort to create this video. I was pretty new to GenAI, your course helped me understand necessary concepts and it wasn't boring at all.

this site is gem, posting valuable things for no cost. My best wishes to all the team members

Thank you! I am looking for course like this! Loving it!

Massive respect for this guy and his team

I was just about to start my education into this adventure and saw this video. The marketing is probably subliminal, but I'm excited either way!

What a gift! Thank you ❤

Wow , thank you so much for sharing the knowledge

Watching from Zambia with determination. Thank you for this great course

Awersome, if the world had more people like you , it would be a better place. Thanks.

Casually drops the whole generative ai course, W

Just completed. I appreciate your work guys. Very impressive.

He always waits for the students to reply. Such a good man.

Brilliant course! thank you for this compilation

That's absolutely amazing 😮30 hrs 🎉course for GENAI

Completed a 31-hour course - turns out, I can binge-watch educational content too! Who needs Netflix when you've got knowledge to gain?

This is the perfect video that I'm looking for❤❤. Thank you for making this long video😍😍

Omg I was looking for this. Thank you❤️‍🔥!!!!

they've started this course 1 or 2 months ago and completed long back on their channel, code camp collaborated with them and uploaded their complete course 2 hours ago. I really appreciate the great content. Thanks all

3days 10hrs Worksop learning. Thank you.

I am from india,here we make fun of people with bad english,whereas in comments all the native english people has nothing to complaint about his bad english,we indians must learn something from them. And gr8 effort team behind this extensive 30 hrs course

just imagine this figure 1:06:18:01 it`s insane length of video. thanks guys, you are incredible

wow really appriciate your work guys, so informative!

Oho men this is a masterpiece and it is for free how lucky we are !! hats off to freecode camp 🎓🎓🎓

I started following the tutorial today and plan to complete the series by the end of the month!! Wish me luck and happy learning!

Finish 30hrs course, and get so many knowledge about AI. Thanks for sharing😊

30hrs??? Just woow... Your efforts are highly appreciated.

Thank god , just 3 days back I started with generative AI course on google could. I was searching for project to make. Then I opened KZfaq. Free code camp always know what we want❤. I am going to start this course from tomorrow. Wish me luck guys.

I affirm myself, I am going to complete this course 😎

Guys, you all gained some good karma by sharing this huge learning material to the world. 😍😍

Wow. Just wow. Thank you!

Finally done with the course. LLM are just transformers trained on large amount of data using good amount of GPU resources. Transformers can be build by any data scientist but GPU resources needed to build LLMs can be afforded by big companies only. Fine tuning can be done but will again require good amount of GPUs. Currently good models exists such as Chat gpt-3.5, lama, gemini pro. Chat gpt 4 is better at coding/computing than gpt-3.5. Gemeni pro has vision LLM which can read pdfs and images. Good prompt engineering is required to get tasks done by LLMs. Hugging face has all the models with all possible task a ML/DL model can accomplish but different models will do different task which require retraining and fine tuning. LLMs can help solve a lot of ML/DL task from one platform/model, given we do proper prompt engineering. I think we are moving exponentially in the direction were we will be using LLMs to do different variety of AI tasks using prompt engineering.

Just completed 1 hour !! it required lots of concentration , will come back to update my progress 🙂🙂

This is awesome and much appreciated.

World best channel with best mentor

Having watched the 30hr video, it was worth it

I completed 13 hours in 5 days, will be completing whole course in next 6 days. thank you for this learned a lot.

Absolutely insane good content! Keep it up!

you guys an absolute legend

I hope you all are enjoying the sessions...Take a challenge to complete this course. Good Luck

Committing to complete this course. Please as a reminder keep me in check by liking the comment or replying to the comment. Will help me stay focused.

Thank you so much for this knowledge ❤

Learning how to Code AI is a game changer

What we will learn in this course how to use AI For Developing a website or software??or its just a brief introduction of different AI models??i dont get what we will learn in it and how we can implement it in our Programing.

I really appreciate the effort put into really quality content. Thank you.

Thank you so much for your service ❤

I started watching it when i was single.i got bf and he left me.. now he has wife and 4 kids ..& i m single again & still watching 😢

The content is a treasure and the intent is genuine and should be given credit but the English and tone are a clear destroyer . Is there a way to use ai to create an an agent to watch this and repackage it with proper English, tone, and video creation so this knowledge could reach millions more?

Thanks for this course.

30hours 🙏🏻 thanks so much for creating this.

Currently I am at 9:20:24.

Imagine AI changing the accent 😂

Yo, good to see krish naik joining up

This video is definitely on my playlist ❤

Only 30% parts are useful . Remaining 70% are chat interaction and other time wasting stuff...

Its so Frustrating to watch 10:44:33 that you are implementing project according to openAi api key but not giving any guidance for those students who are using hugging_face. PS : "You are providing this knowledge on free platform. "

Simply the Amazing ❤️

Simply mind blowing ❤

i can't understand his accent, makes it unpleasant to watch 30hrs of this.

Omg thank you a lot!! I’ll update this message once I finish this course:)😅

Thank you for this amazing content ❤

Best video for a good night sleep ❤

I can't thank this channel enough

amazing as always

Thanks for providing knowledgeable content. Hope will providingsuch more content in future.

Beyond I introduction will be going through all of this this week😮😮😮

I love Ineuron .. I am from Pakistan :) love you guys really you are great

Thanks you for this amazing lessons.. Instructors are good.

I hope by following you I can learn a new skill and change my world and others too.

very excited!!!!!!!!

Very nice generative Ai 30 hours course👍👍

Am excited.

Guys this is course very good, just a advice watch at 1.5x speed and don't try to finish in a day or 2. Watch only 2-3 hours of timeline every day.

Woooo , more knowledge than a degree

Thanks for your efforts

This course is helpful ❤

I am starting this!

I enjoy your teaching.

Thank you so much ❤

Very nice, thank you very much.