Generative AI nano degree program

Introduction and Overview

Proficiency in Generative AI skills such as text and image generation, deep learning fundamentals, NLP, and computer vision is highly valuable in today's technological landscape. These skills enable individuals to develop innovative solutions across various domains such as natural language processing, image synthesis, and personalized recommendation systems. With the growing demand for AI-driven applications in industries ranging from healthcare to finance, mastering Generative AI equips professionals with the ability to create intelligent systems that enhance efficiency, creativity, and decision-making processes. Additionally, understanding the ethical implications of AI ensures responsible development and deployment of these technologies, fostering trust and sustainability in the field.

This training program covers foundational principles and advanced practices in Generative AI. It includes topics on text and image generation using Large Language Models (LLMs), deep learning fundamentals, NLP, computer vision, and ethical considerations. Through practical projects, learners acquire skills in building custom chatbots, AI photo editing tools, and personalized real estate agents.

Program structure

Generative AI Fundamentals:

• Introduction to Generative AI Fundamentals: Covers foundational knowledge about generative AI, popular algorithms, and architectures for text and image generation.

• Deep Learning Fundamentals: Essentials of deep learning for generative AI practitioners including an introduction to PyTorch and Hugging Face libraries.

• Foundation Models: Exploration of foundation models in AI, their application to various tasks, and ethical implications.

• Adapting Foundation Models: Techniques for adapting foundation models including prompt tuning and parameter-efficient fine-tuning (PEFT).

  → Project: Lightweight Fine-Tuning to a Foundation Model using PEFT

Large Language Models (LLMs) & Text Generation:

• Introduction to LLMs: Types of LLMs, understanding their limitations and capabilities, and strategies for prompt engineering.

• NLP Fundamentals: Basics of Natural Language Processing, text encoding, and generation.

• Transformers and Attention Mechanism: Exploration of transformer architectures, attention mechanisms, and modern transformer models.

• Retrieval Augmented Generation: Creation of a custom Q&A bot and leveraging OpenAI's language processing capabilities.

• Build Custom Datasets for LLMs: Construction of relevant datasets for fine-tuning large language models.

  → Project: Building a Custom Chatbot

Computer Vision and Generative AI:

• Introduction to Image Generation: Defining image generation and its relevance in AI and machine learning.

• Computer Vision Fundamentals: Understanding how computers process and analyze image data.

• Image Generation and GANs: Exploration of Generative Adversarial Networks (GANs) for image generation.

• Transformer-Based Computer Vision Models: Understanding Vision Transformers and their applications.

• Diffusion Models: Fundamentals of diffusion algorithms and hands-on work with Huggingface Diffusers for image generation.

  → Project: AI Photo Editing with Inpainting

Building Generative AI Solutions:

• Introduction to Building Generative Apps: Design and implementation of Generative AI using Large Language Models.

• Building Generative AI Solutions with Vector Databases: Core concepts of vector databases and their application in AI.

• Developing Generative AI Solutions with LangChain: Exploration of LangChain framework for working with large language models.

  → Project: Personalized Real Estate Agent

Skills

• Generative AI Fundamentals: Generative AI Fluency • Image classification • Transfer learning • Training neural networks • Hugging Face • Parameter-Efficient Fine-Tuning • Prompt Engineering • Deep learning • PyTorch • Foundation Models • Ethical AI

• Large Language Models (LLMs) & Text Generation: Together AI API • Search implementation in Python • NLP transformers • Selenium • Large Language Models • Data cleaning • Natural language processing • OpenAI API • Transformer neural networks • Prompt Engineering • Tokenization • Cosine Similarity • API requests • Recurrent neural networks • Attention mechanisms • Text generation • Data quality assessment • Word embeddings • Data scraping

• Computer Vision and Generative AI: Image pre-processing • Transfer learning • Word embeddings • Ethical AI • Diffusion Models • YOLO algorithm • Model evaluation • Text generation • Computer vision fluency • Image classification • Large Language Models • Pandas • Image generation • Training neural networks • Convolutional neural networks • Parameter-Efficient Fine-Tuning • Image segmentation • Computer Vision Transformers • Tokenization • Data quality assessment • Generative adversarial networks

• Building Generative AI Solutions: Vectors • Retrieval-Augmented Generation • OpenAI API • LangChain