Summary of "DAY - 1 | Introduction to Generative AI Community Course LIVE ! #genai #ineuron"
Summary of "DAY - 1 | Introduction to Generative AI Community Course LIVE ! #genai #iNeuron"
This session is the introductory class of a two-week community course on Generative AI, focusing on foundational concepts, theoretical understanding, and practical applications. The instructor, Sunny, along with Buppy, will guide participants through the course, which includes lectures, quizzes, assignments, and hands-on coding sessions.
Main Ideas, Concepts, and Lessons Conveyed
1. Course Introduction and Structure
- The course runs for two weeks, sessions from 3:00 PM to 5:00 PM.
- It covers basics to advanced topics in Generative AI.
- Includes theoretical lessons, practical coding, application development, quizzes, and assignments.
- Participants must enroll on a free dashboard where all course materials will be uploaded.
- Recorded sessions will be available on the iNeuron YouTube channel and dashboard.
2. Course Instructors
- Sunny: Data science expert with 3 years at iNeuron, experienced in ML, DL, NLP, MLOps.
- Buppy: Co-instructor (details not extensively covered).
3. Prerequisites
- Basic knowledge of Python (control flow, data structures, exception handling).
- Basic understanding of machine learning and deep learning is helpful but not mandatory.
- No need for deep knowledge of classical ML or deep learning architectures.
4. Course Curriculum Overview
- Generative AI: Definition, applications, and why use it.
- Large Language Models (LLMs): History, types, and detailed architecture.
- OpenAI and LangChain: APIs, dashboards, Python integration, differences between OpenAI and LangChain.
- Application Development: Building projects using LLMs and LangChain.
- Advanced Topics: Vector databases, embeddings, open-source LLMs (Llama 2, Falcon, Bloom).
- Deployment: Using MLOps concepts to deploy AI applications.
5. Generative AI Fundamentals
- Generative AI generates new data (text, images, audio, video) based on training data.
- It is a subset of deep learning.
- Two main segments:
- Generative Image Models (e.g., GANs)
- Generative Language Models (LLMs)
- GAN architecture explained: Generator vs. Discriminator neural networks.
- Transition from GANs to Transformer-based LLMs for language and image generation.
6. Deep Learning and Neural Networks Refresher
- Three major neural network types:
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN) for grid/image data
- Recurrent Neural Networks (RNN) for sequence data
- Advanced RNN variants: LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit)
- Sequence-to-sequence learning, encoder-decoder architecture, and its limitations.
- Introduction to Attention Mechanism and its role in overcoming sequence length limitations.
7. Transformer Architecture
- Introduced by the paper "Attention is All You Need" (2017).
- Faster than RNN/LSTM due to parallel processing of input tokens.
- Key components: Input embedding, positional encoding, multi-headed attention, feed-forward neural networks.
- Transformer consists of encoder and decoder blocks.
- Transformer is the foundational architecture for modern LLMs like GPT, BERT, T5.
8. Generative vs Discriminative Models
- Discriminative models perform supervised learning to classify or predict labels.
- Generative models learn to generate new data, often trained with unsupervised learning followed by supervised fine-tuning and reinforcement learning (as in ChatGPT).
- Generative models generate outputs based on learned data patterns.
9. Large Language Models (LLMs)
- LLMs are large-scale neural networks trained on massive datasets.
- Capable of performing multiple NLP tasks: text generation, summarization, translation, question answering, code generation, etc.
- Examples: GPT family (GPT-1, 2, 3, 3.5, 4), BERT, RoBERTa, T5, Megatron, XLM.
- Models categorized by Transformer usage:
- Encoder-only (e.g., BERT)
- Decoder-only (e.g., GPT)
- Encoder-decoder (e.g., T5, BART)
- OpenAI models and open-source alternatives (Llama 2, Falcon, Bloom, StableLM).
- Training involves unsupervised pre-training, supervised fine-tuning, and reinforcement learning.
10. Applications of LLMs
- Text classification, chatbot creation, summarization, speech recognition, sentiment analysis, code generation.
- LLMs identify patterns in data and generate coherent, context-aware outputs.
11. Prompt Engineering
- Input given to LLMs is called "input prompt."
Category
Educational
