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Generative AI training focuses on teaching participants how to build, use, and understand AI models capable of generating new content, such as text, images, audio, or code. Generative AI models don’t just classify or recognize data—they learn patterns and then generate outputs based on those patterns, mimicking human creativity. It automates creative processes, from marketing content to product design.
Curriculum Designed by Experts
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
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
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
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
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
• 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
• 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
• 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
• 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
• 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
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