Generative AI Master course
Training Duration: (50 hours)
What You Will Learn:
• Introduction to Generative AI:
o Understand the fundamentals of Generative AI and its applications.
o Explore the differences between traditional AI models and generative models.
• Getting Started with Langchain:
o Learn the basics of Langchain and its role in AI development.
o Set up your development environment and tools.
• Huggingface Integration:
o Integrate Huggingface’s state-of-the-art models into your Langchain projects.
o Customize and fine-tune Huggingface models for specific applications.
• Building Generative AI Applications:
o Step-by-step tutorials on creating advanced generative AI applications.
o Real-world projects such as chatbots, content generators, and data augmentation
tools.
• Deployment Strategies:
o Learn various deployment strategies for AI models.
o Deploy your models to cloud platforms and on-premise servers for scalability and
reliability.
• RAG Pipelines:
o Develop Retrieval-Augmented Generation (RAG) pipelines to enhance AI
performance.
o Combine generative models with retrieval systems for improved information access.
• Optimizing AI Models:
o Techniques for monitoring and optimizing deployed AI models.
o Best practices for maintaining and updating AI systems.
• End-to-End Projects:
o Hands-on projects that provide real-world experience.
o Build, deploy, and optimize AI applications from scratch.
Syllabus
1. Introduction to Generative AI
• Overview of Generative AI
o What is Generative AI?
o Applications in various industries (text, images, music, code generation, etc.)
• Basic AI Concepts
o Machine Learning vs. Deep Learning vs. Generative AI
o Key components: Models, data, and learning
• Tools and Libraries
o Python setup: Jupyter Notebook, Colab
o Introduction to TensorFlow, PyTorch, Hugging Face, and OpenAI APIs
2. Fundamentals of Neural Networks
• Deep Learning Basics
o Artificial Neural Networks (ANN)
o Feedforward and Backpropagation
o Loss functions and optimization
• Hands-On: Build a simple Neural Network
o Frameworks: TensorFlow/Keras or PyTorch
o Training and testing a basic network
3. Probabilistic Generative Models
• Introduction to Probabilistic Models
o Gaussian Mixture Models (GMM)
o Hidden Markov Models (HMM)
• Variational Inference
o Basics of Bayesian inference
o Variational Autoencoders (VAE) theory and implementation
• Hands-On: Implementing VAEs
o Generate synthetic data using VAEs
4. Generative Adversarial Networks (GANs)
• Theory of GANs
o Generator and Discriminator
o Minimax Game and Loss Function
o Challenges: Mode collapse, convergence issues
• Types of GANs
o DCGANs, WGANs, and Conditional GANs
• Hands-On: Build and Train GANs
o Generate synthetic images (e.g., handwritten digits or simple objects)
o Explore variations with Conditional GANs
5. Transformers and Attention Mechanisms
• Understanding Attention Mechanisms
o Importance of self-attention
o From RNNs to Transformers
• Introduction to Transformers
o Architecture of Transformers
o Key components: Positional Encoding, Multi-head Attention, Feedforward layers
• Hands-On: Implementing Attention
o Build a simple attention mechanism
o Explore a Transformer model using Hugging Face
6. Large Language Models (LLMs) and NLP
• Introduction to LLMs
o What are LLMs? (e.g., GPT, BERT, T5)
o Tokenization and embeddings
o Fine-tuning pre-trained models
• Hands-On: Text Generation
o Fine-tune a GPT model on custom text data
o Generate creative writing or chat-based text
7. Diffusion Models and Image Generation
• Introduction to Diffusion Models
o Theory: Noise addition and removal process
o Popular diffusion models (e.g., DALL·E, Stable Diffusion)
• Hands-On: Image Generation
o Use pre-trained models for image generation
o Experiment with latent diffusion
8. LangChain and OpenAI API
• LangChain Basics
o Overview of LangChain and its ecosystem
o Connecting LLMs with external tools and APIs
o Building chains: Sequential and parallel pipelines
• OpenAI API
o Introduction to the OpenAI API (e.g., GPT models)
o Using OpenAI API for text generation, summarization, and translation
• Hands-On: Building Workflows
o Build a LangChain-powered pipeline for a text-based use case
o Experiment with OpenAI for prompt engineering and fine-tuning
9. Building Chatbot Applications
• Introduction to Chatbots
o Types of chatbots: Rule-based vs. AI-powered
o Key components: Intent recognition, response generation
• Creating AI-Powered Chatbots
o Using LangChain and OpenAI to build conversational agents
o Integrating memory and context in chatbot interactions
• Hands-On: Deploying Chatbots
o Build a chatbot app using Python
o Deploy using Streamlit or Flask
o Test chatbot on real-world scenarios
10. Capstone Project
• Project Options
o Text-to-Image Generation (e.g., captions into images)
o Custom Text Generation (fine-tuning a GPT model)
o Music Generation using RNNs or Transformers
o AI-powered chatbots with LLMs
o Artistic Style Transfer using neural networks
• Presentation and Evaluation
o Project presentation
o Peer review and feedback
Who Should Take This Course:
• AI and Machine Learning Enthusiasts
• Data Scientists and Machine Learning Engineers
• Software Developers and Engineers
• NLP Practitioners
• Students and Academics
• Technical Entrepreneurs and Innovators
• AI Hobbyists
By the end of this course, you will have the knowledge and skills to build, deploy, and optimize generative AI applications, leveraging the power of Langchain and Huggingface. Join us on this exciting journey and become a master in Generative AI!