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

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.

Connect with us on : [phone]

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[nehalshams]

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

[himanshushukla]

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

[GauravSharma-lxrb]

Informative video, looking for more such content 😃

[Aman_]

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

[shubhamsharma--india]

Thanks a lot really helpful initiative ❤❤

[kd]

🎯 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

[Chapi-pxbf]

Thank you so much it is so interesting tutorial

[sawfhsawfh]

Thank you so much "Sunny" and "Bappy"

[rakeshkumarrout]

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

[gayamsunilkumar]

Please upload the ppt in the resources section INEURON

[Startupindia]

And i am exiceted with free and advance course

[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.

[shivamagarwal]

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