DAY - 1 | Introduction to Generative AI Community Course LIVE !

Day - 1 | Introduction to Generative AI
The moment we've all been eagerly waiting for is finally here! Today marks the commencement of our much-anticipated course on GENERATIVE AI. 🎉
We'll be diving into fascinating topics like LLM, LlamaIndex, Vector DB, and much more. It's your chance to embark on a comprehensive exploration of cutting-edge concepts.
For course details please visit : ineuron.ai/course/generative-...
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[@iNeuroniNtelligence]

For course details please visit: ineuron.ai/course/generative-ai-community-edition

[@sumitpatil4356]

i have doubt in programming of function calling how to connect sunny

[@himanshushukla3060]

Really? I cant believe i paid for a webinar recently for generative Ai and i can't believe that all of this you guys are going to be teaching for free !! Kudos to you guys thanks a ton

[@KishanPatel22573]

From B10x me too..did not learn a word. Glad i found this. My company has been pressing for me to learn this

[@nehalshams7945]

30:04 topics of deep learning ANN, CNN, RNN, RL, GAN 45:55 Generative Model 47:29 where generative AI exist 55:33 timeline of LLM 59:31 different types of mapping techniques 1:16:04 Attention research paper 1:23:14 discriminative vs generative model 1:27:56 LLM

[@NihalNibhanupudi]

Beautifully explained

[@Aman_7458]

very informative thanks a lot for this looking forward to more such sessions

[@GauravSharma-lx9rb]

Informative video, looking for more such content 😃

[@KumR]

Good one. Getting your point.. YES

[@MrAmarSindol]

very happy ...thanks ineuron

[@jatinnandwani6678]

Excellent man, thanks so much

[@kd6613]

🎯 Key Takeaways for quick navigation: 02:24 *📢 Introduction to Generative AI Community Session* - Introduction and confirmation of audio/video quality. - Waiting for additional participants before starting. 06:05 *🚀 Overview of Generative AI Course Content* - Introduction to the course and its structure over two weeks. - Discussion on the basic to advanced concepts in Generative AI and application development. 07:55 *💡 Course Dashboard and Enrollment Details* - Explanation of the course dashboard, enrollment process, and availability of resources. - Highlighting the accessibility of lectures, quizzes, and assignments for free. 09:07 *👨‍🏫 Instructor Introduction and Course Curriculum* - Introduction of the instructor's background and expertise in data science. - Detailed explanation of the course syllabus, including topics on generative AI, LLMs, OpenAI, and practical applications. 11:42 *📚 Curriculum Deep Dive and Interactive Learning Approach* - Focus on recent trends and practical applications in generative AI. - Emphasis on interactive learning, hands-on projects, and the importance of quizzes and assignments for reinforcement. 13:31 *🧠 Detailed Course Topics and Technologies* - Explanation of generative AI basics, large language models (LLMs), OpenAI's API, and Lennon. - Discussion on creating AI applications, vector databases, and exploring various open-source models. 19:27 *🛠️ Prerequisites and Course Readiness* - Detailing the prerequisite knowledge for the course, including basic Python, ML, and DL. - Assurance of comprehensive teaching methods for all skill levels. 22:28 *🌟 Introduction to Generative AI and Large Language Models* - Begin introduction to generative AI and LLMs, setting the stage for detailed exploration in subsequent sessions. - Engaging participants with questions on their familiarity with generative AI to tailor the session's content. 29:06 *🧠 Deep Learning Fundamentals and Neural Networks* - Introduction to deep learning and its three major segments: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). - Discussion on specialized neural networks like Reinforcement Learning (RL) and Generative Adversarial Networks (GANs), emphasizing their role in generative AI. 37:01 *🎨 Generative AI Overview and Image Models* - Explanation of Generative AI as a creator of new data, including images, texts, and videos, based on training samples. - Division of generative AI into generative image models and generative language models, with an emphasis on GANs and their architecture involving generators and discriminators. 43:43 *🤖 Introduction toLarge Language Models (LLMs) and their Evolution* - Introduction to Large Language Models (LLMs) highlighting their emergence from Transformers and their capacity for various generative tasks. - Discussion on the evolution of image and text generation models, moving from GANs to more advanced LLMs capable of generating images from text prompts. 48:21 *📚 Generative AI within the Deep Learning Spectrum* - Positioning of Generative AI within the deep learning domain, elaborating on its capability to generate images, texts, and undertake complex transformations like text-to-image generation. - Exploration of the discriminative AI versus generative AI, underscoring the generative AI’s unique ability to create new, unseen data. 52:16 *🔄 Evolution of Generative Tasks and LLM Capabilities* - Discussion on the evolution from GANs to LLMs for tasks like image-to-image generation, text-to-text generation, image-to-text, and text-to-image generation. - Emphasis on the versatility of LLMs in handling both homogeneous and heterogeneous data types. 55:16 *🏛️ Generative AI within the AI Ecosystem* - Placement of Generative AI within the broader AI, machine learning, and deep learning contexts, affirming its status as a subset of deep learning. - Explanation of the hierarchy from AI to deep learning and how generative AI fits within this structure. 01:01:47 *🔊 Technical Difficulties and Resumption* - A brief interruption due to microphone issues, followed by a check with the audience for audio quality and resumption of the session. 01:04:03 *📖 Sequence-to-Sequence Models and Encoder-Decoder Architecture* - Introduction to sequence-to-sequence models, their significance, and limitations in handling fixed-length input and output. - Exploration of encoder-decoder architecture and the introduction of attention mechanisms to overcome the limitations of traditional seq2seq models. 01:15:11 *💡 The Transformer Model and Its Impact on NLP* - Discussion on the Transformer model, introduced in the "Attention is All You Need" paper, and its revolutionary impact on natural language processing (NLP). - The Transformer model's architecture, featuring input embedding, positional encoding, multi-head attention, and how it differs fundamentally from RNNs, LSTMs, and GRUs. 01:18:11 *🔄 Transformer Architecture and Its Efficiency* - Overview of the Transformer architecture, emphasizing its speed and parallel processing capabilities. - Explanation of key components like input embedding, positional encoding, and multi-headed attention. 01:28:01 *🗂️ Introduction to Large Language Models (LLMs)* - Definition and significance of Large Language Models (LLMs), trained on extensive datasets. - Discussion on why LLMs are termed "large" due to their size, complexity, and the vast amounts of data they are trained on. 01:35:06 *🔍 Open Source and OpenAI Based LLMs* - Distinction between OpenAI's proprietary models like GPT variants and open-source models like Bloom and Llama. - Explanation of the various applicationsand capabilities of LLMs in generating and understanding complex data patterns. 01:38:05 *📚 Session Conclusion and Recap* - Recap of the session's key points on generative AI and LLMs. - Encouragement for audience interaction and feedback on the session's content, with a forward look towards practical demonstrations in future sessions. 01:39:08 *🛠️ Accessing OpenAI and Hugging Face Models* - Instructions on how to access OpenAI API and explore various models on the Hugging Face Hub. - Emphasis on the need to create an account and generate an API key for OpenAI and explore open-source models for various tasks on Hugging Face. 01:42:20 *🔄 Alternative Platforms and LLM Applications* - Introduction to AI 21 Labs as an alternative to OpenAI's GPT models, offering a different model for free usage. - Discussion on the broad capabilities of LLMs in handling tasks like text generation, sentiment analysis, and chatbots. 01:44:35 *🖼️ Generative AI and LLMs in Computer Vision* - Clarification that LLMs are primarily for language-related tasks, not directly applicable to computer vision projects. - Mention of different models and transfer learning techniques for computer vision tasks. 01:48:05 *📘 Understanding Transfer Learning in NLP with ULMFit* - Explanation of transfer learning's role in NLP, as showcased by the ULMFit paper. - Discussion on how LLMs have evolved from traditional language models by being trained on vast datasets, making them versatile for various NLP tasks. Made with HARPA AI

[@kiku_108]

lectures starts at 26:10 .......ur welcome

[@safdarabbas6581]

Thanks.. lots of aah .. so.. basically..

[@dharmaraosalana641]

Superb

[@thomasantony3381]

Deep Respect 💪🎉

[@shubhamsharmaofficial04]

Thanks a lot really helpful initiative ❤❤

[@76-madhupshrivas15]

Great sir 👍

[@kNetMask255]

thanks for the starting GenAI series🙏

[@AnkitKumar-eq3vw]

Well explained ❤

[@anshkumarpathak2880]

Well done 👍👏

[@naveen_savita]

Very good 👍

[@firmankrisnadi7248]

Highly appreciate

[@sshekharmishra2496]

Great work Sunny! ❤

[@user-gh1ql7sl1c]

great session thanks

[@imrahamed]

0:00-0:05 : 📢 Introduction to the generative AI community session. 0:05-0:15 : 📚 Overview of the curriculum and topics to be covered. 0:15-0:25 : 🎥 Explanation of the dashboard and enrollment process. 0:25-0:30 : 💻 Introduction of the instructor and their expertise. 0:30-0:45 : 📺 Detailed discussion on generative AI and large language models. 0:45-1:00 : 🌐 Explanation of OpenAI and its different models. 1:00-1:15 : 📝 Importance of vector databases in generative AI applications. 1:15-1:30 : 🗃 Introduction to open-source models like LAMA and Falcon. 1:30-1:45 : 🚀 Deployment of end-to-end projects using generative AI and MLOps concept. Key Insights 📢 The generative AI community session will cover topics like generative AI, large language models, open-source models, and deployment using MLOps. It aims to provide a comprehensive understanding of generative AI applications. 📚 The curriculum focuses on recent trends in generative AI and emphasizes practical implementation by creating real-world projects. 💻 The dashboard serves as a central platform for accessing videos, assignments, quizzes, and resources related to the community session. 🎥 The session will be conducted by an experienced instructor with expertise in data science, machine learning, and deep learning. 📺 The theoretical part of the course will cover generative AI, large language models, and their applications, while the practical part will involve using Python to work with OpenAI and LAMAs. 🌐 OpenAI offers various models like GPTs, and the session will provide a detailed walkthrough of these models and how to utilize them using Python API. 📝 Vector databases play a crucial role in generative AI applications, storing and retrieving embeddings for efficient processing and retrieval of data. 🗃 Open-source models like LAMA and Falcon offer powerful features that can be used to solve various tasks and will be explored in this community session. 🚀 The session will conclude with deploying end-to-end projects using generative AI and MLOps concepts, showcasing the practical application of the knowledge gained.

[@Chapi-px8bf]

Thank you so much it is so interesting tutorial

[@sawfhsawfh2024]

Thank you so much "Sunny" and "Bappy"

[@ruhiraj9551]

Thanku so much sir 🙏🏻

[@ajaypandit.]

Hii ruhi

[@kanchantomar1591]

🙌🙌

[@Startupindia3]

Yes i like

[@Startupindia3]

Yes

[@prakashquest850]

Yes brother

[@Startupindia3]

Video and audio fine

[@Startupindia3]

And i am exiceted with free and advance course

[@yashkrgupta2122012]

yes

[@gayamsunilkumar3451]

Please upload the ppt in the resources section INEURON

[@ashutoshsenofficial8262]

👏🏻👏🏻

[@rakeshkumarrout2629]

I request you to include single agent and multi agent frame works like autogen and all.

[@subirmyspace]

🙏🙏

[@shivamagarwal2188]

I am not able to sign up on the ineuron platform.

[@varmavanam-pd2xb]

What is prerequisite to learn generative AI

[@jisubehera7670]

Can we get ppt of this training

[@user-lw9wr9cr7g]

Hi Sir, Can I get learn and get experience only on GEN AI and apply for jobs, without having previous experience on Machine Learning or Artificial Intelligence ?

[@ankitsanap5908]

Plz upload the ppt and research papers

[@huzaifaharis285]

How many time you use word different

[@roseannerosie6110]

gotta pay for gpt 3.5 turbo???because none of my code runs it just shows ratelimit error

[@shashankshashank161]

Can we learn this without knowledge of Deep Learning ?

[@realtruevideo]

Can u share the PPT of this entire course

[@aditya5ghare]

Why i feels its sudhanshu kumar voice??

[@tomal1979]

Is there any prerequisite to take this course?

[@sunnysavita9071]

No

[@sachinpurohit2512]

sir jo 5 baje hindi wali class hone wale thi wo nahi hogi kya

[@sunnysavita9071]

It happened please check with iNeuron tech Hindi

[@krishnasundeepbasavaraju7024]

Hi sir, with a heavy heart I am writing this comment. I am currently working as lecturer from past 10 years having no growth in my career. I just want a career guidance to switch my career. sir, I am also pursuing PHD in signal processing with ML. having fundamentals of Machine learning too. kindly suggest me the way and steps to learn Gen AI . this will be of great help in my life sir. Thank you

[@iqtech6065]

I have submitted all quizzes and assignments, for quizes it's showing me completed but for assignments it's not marked yet status is submitted when I am downloading certificate it's saying that submit 60% assignments.... Kindly help..

[@032surajmali6]

They will check the assignment you have submitted and give marks then the marks count in honor 60%

[@andrewereynolds5840]

💎 Cognizant Technology Solutions (CTSH) 💎

[@Startupindia3]

Yes I regestration

[@Startupindia3]

Free course is it and totally complete

[@madhulikasworld1783]

From 59 mins could not Hear anyting 3/4 mins

[@vickYT_69]

gan ❌GAYn✅

[@ComputerByVirender]

Why he is using so many fillers while speaking? After Every 2-3 second he is like Aaa ammm aaa amm aaa aaa aaa aaaa aaaaaa aaaaaa

[@nishanthgandhidoss6848]

There is a need to make your content little more better. For example: Large Language model are called "large" because of the large number of weights theses models have not because of the data alone

[@lettechtutorials]

If all audience is from Pakistan and INDIA then why not in URDU or Hindi ?

[@Redemption369]

Does a person require software background?? Without that can he able to survive? Please let us know

[@user-tk1tq6nz1d]

Can u share the PPT of this entire course @iNeuroniNtelligence

[@vickYT_69]

DALL.E ❌ Delhi✅

[@yashkrgupta2122012]

yes

[@tarun87able]

Yes