This course provides a comprehensive overview of foundational concepts in Machine Learning (ML) and Deep Learning (DL), along with practical applications in Generative AI. Participants will explore various models, techniques, and tools, gaining hands-on experience with Python and popular libraries.
- Fundamental Concepts: Overview of basic ML concepts and foundational problems.
- Neural Networks: Introduction to neural networks, key architectures, and application problems.
- Learning Theory: Theoretical underpinnings of learning and related challenges.
- Practical Component: Hands-on practice with Python, data preprocessing, and classification problems using
scikit-learn.
- Introduction to PyTorch: Overview of the PyTorch library and its applications in ML and DL.
- Classification Tasks: Implementing classification tasks using PyTorch.
- Overview of Generative AI: Understanding the principles and applications of generative models.
- Large Language Models (LLMs): Foundation of LLMs and their multimodal capabilities.
- Hands-on Practice: Exploring various multimodal LLMs and their applications.
- Theoretical Foundations: Understanding the theory and techniques behind text generation.
- Prominent LLMs: Introduction to notable LLMs for text generation (e.g., GPT, Claude, Gemini, Llama, Mistral).
- Prompting Technology: Techniques for effective prompting.
- Challenges: Discussing limitations of text generation AI.
- Hands-on Practice: Experimenting with various LLMs for text generation.
- Theoretical Foundations: Understanding the theory and techniques behind image and video generation.
- Models for Image/Video Generation: Introduction to GANs, VAEs, and Diffusion models.
- Popular Libraries: Overview of libraries such as Dall-E, CLIP, VideoGPT, and Make-A-Video.
- Challenges: Discussing limitations of image/video generation AI.
- Hands-on Practice: Experimenting with GANs and Diffusion models.
- Generative AI in QA/Chatbots: Overview of systems utilizing generative AI for question answering and chat functionalities.
- RAG Technology: Introduction to Retrieval-Augmented Generation (RAG).
- Hands-on Practice: Creating a QA/Chatbot system using RAG technology.
- Image Analysis: Overview of tasks such as image captioning and quality enhancement.
- Digital Content Creation: Exploring Text-to-Image and Text-to-Video generation.
- Hands-on Practice: Implementing examples in image analysis and digital content creation.
This project leverages a pretrained language model (like GPT-3.5) and a text-to-image generation model (like DALL-E or Stable Diffusion) to automatically generate descriptions of objects and create corresponding images.
- Objective: Generate a new object description using a sample text description as inspiration.
- Approach: Use a language model to take a sample input and produce similar descriptions for other objects.
- Tasks:
- Start with a sample description, such as
"a cozy, vintage wooden chair with intricate carvings on the arms." - Experiment with prompts to encourage the model to generate variations or describe similar items.
- Guide generation with prompts like
"Describe a lamp similar to the chair in the previous description."
- Start with a sample description, such as
- Objective: Ensure that the description is detailed enough to accurately translate into an image.
- Approach: Add specifics like color, texture, size, and unique features to refine the description.
- Tasks:
- Enhance the prompt with details, such as
"Include the color, shape, and size of the object in your description." - If describing a lamp, ensure the output includes phrases like
"a tall, slender metallic lamp with a bronze finish and a soft white light shade."
- Enhance the prompt with details, such as
- Objective: Use the refined description to generate an accurate image.
- Approach: Feed the descriptive text into a text-to-image model to create an image that reflects the text.
- Tasks:
- Input the descriptive text into the image generation model.
- Adjust wording if needed, as slight changes in the prompt can influence the visual outcome.
- Objective: Verify the quality and coherence of the generated image to ensure it matches the intended description.
- Approach: Compare the image to the original description, noting any mismatches in color, shape, or other details.
- Tasks:
- Refine the text prompt based on feedback if necessary. For instance, emphasize the correct color if the image shows an incorrect one.
- Iterate until the generated image aligns with the initial description.
- Objective: Use feedback to generate variations or improve the accuracy of descriptions and images.
- Approach: Implement a feedback loop where users or evaluators rate alignment between text and image or suggest changes.
- Tasks:
- Generate multiple variations with slight prompt adjustments, collecting feedback for each.
- Experiment with more complex or abstract objects to assess model versatility in matching text-to-image consistency.
- Basic understanding of programming in Python.
- Familiarity with fundamental concepts in statistics and linear algebra is beneficial but not required.
- Python: Primary programming language for exercises.
- scikit-learn: For machine learning tasks.
- PyTorch: For deep learning applications.
- Generative AI Libraries: Dall-E, CLIP, VideoGPT, etc.
This course aims to equip participants with both theoretical knowledge and practical skills in Machine Learning, Deep Learning, and Generative AI. By the end of the course, participants will be able to apply various models and techniques to real-world problems in AI.