🎯 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
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
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.
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
@iNeuronIntelligence I paid money for full stack data science with generative AI V2 and you have cancelled the course, all this happened in the month of June, despite multiple emails none of you are responding and no payment is done to me, this is clearly unprofessional and your team who sold me the course is also not responding! Please refund the amount and have some responsibility to answer the emails or calls!
Such a high quality video. How did you record your screen and yourself? Which software did you use? I would be thankful if you could let me know. Best wishes and regards
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
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..
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 ?
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
For course details please visit: ineuron.ai/course/generative-ai-community-edition
🎯 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
Amazing Content. Lessgoo!!!
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
best cource for generative AI.
lectures starts at 26:10 .......ur welcome
Thanks.. lots of aah .. so.. basically..
Superb
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.
Thanks .
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
From B10x me too..did not learn a word. Glad i found this. My company has been pressing for me to learn this
Can I get this whole session in Hindi language
Beautifully explained
@iNeuronIntelligence I paid money for full stack data science with generative AI V2 and you have cancelled the course, all this happened in the month of June, despite multiple emails none of you are responding and no payment is done to me, this is clearly unprofessional and your team who sold me the course is also not responding! Please refund the amount and have some responsibility to answer the emails or calls!
I am very happy for your videos
Good one. Getting your point.. YES
Thank you so much "Sunny" and "Bappy"
Thank You Sir.
Best Playlist for GenAi
Deep Respect 💪🎉
Highly appreciate
very happy ...thanks ineuron
Very good 👍
Well explained ❤
Informative video, looking for more such content 😃
Well done 👍👏
Thanks sir
And i am exiceted with free and advance course
Thanks a lot really helpful initiative ❤❤
Please upload the ppt in the resources section INEURON
Excellent man, thanks so much
very informative thanks a lot for this looking forward to more such sessions
great session thanks
excellent explanation ,
Thanku so much sir 🙏🏻
Hii ruhi
🙌🙌
Thank you so much it is so interesting tutorial
thanks for the starting GenAI series🙏
YES
Yes i like
Very good
When is the next sessions to begin please? I am keen to join your next batch...
Video and audio fine
Yes
Yes brother
Great sir 👍
Can we get ppt of this training
👏🏻👏🏻
Lecture start at 26:10
I request you to include single agent and multi agent frame works like autogen and all.
if possible, can we get all Notes
What is prerequisite to learn generative AI
Great work Sunny! ❤
sir jo 5 baje hindi wali class hone wale thi wo nahi hogi kya
It happened please check with iNeuron tech Hindi
Where is PPT
I am not able to sign up on the ineuron platform.
Can u share the PPT of entire course
Such a high quality video. How did you record your screen and yourself? Which software did you use? I would be thankful if you could let me know. Best wishes and regards
💎 Cognizant Technology Solutions (CTSH) 💎
SIr, can you pls share the link of this PPT?
How many time you use word different
gotta pay for gpt 3.5 turbo???because none of my code runs it just shows ratelimit error
Plz upload the ppt and research papers
Can u share the PPT of this entire course
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
where is the slides /notes
Is there any prerequisite to take this course?
No
Basics of Python
Sir is this course enough for placement criteria
Why i feels its sudhanshu kumar voice??
From 59 mins could not Hear anyting 3/4 mins
Bro said ...uh... 2 billion times
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..
They will check the assignment you have submitted and give marks then the marks count in honor 60%
Can we learn this without knowledge of Deep Learning ?
🙏🙏
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 ?
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
Probably a language barrier
Sir .I am from Arts background . And have interest in learning AI . Will I be getting good jobs after learning this
Audible
Hi sir kaise hain aap sir main bhi sikhana chahta hun kaise Sikh lunga mujhe itni English nahin aati
Yes I regestration
Free course is it and totally complete
22 minutes lost and still nowhere near start of the topic, can u pls edit the video else you will lose way more audience than you can imagine
gan ❌GAYn✅
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
If all audience is from Pakistan and INDIA then why not in URDU or Hindi ?
conclusion: everything is nothing
👍
Does a person require software background?? Without that can he able to survive? Please let us know
Can u share the PPT of this entire course @iNeuroniNtelligence
DALL.E ❌ Delhi✅
yes
yes
yes
Yes
yes