Generative AI with Large Language Models (LLMs)

This intensive course delves into the world of generative AI, focusing on the power and potential of Large Language Models (LLMs).  By the end, you'll gain a strong foundation in LLM architecture, explore real-world applications, and get hands-on experience with text generation and language understanding tasks.

Target Audience:

• Data scientists seeking to leverage LLMs for advanced data analysis and content creation.

• Machine learning engineers interested in building and deploying LLM-powered applications.

• Software developers looking to integrate LLMs into their existing NLP workflows.

• AI enthusiasts eager to understand the cutting edge of generative AI research.

Prerequisites:

• Basic understanding of Python programming and its use in data science or machine learning (libraries like NumPy, Pandas).

• Familiarity with machine learning concepts like neural networks is helpful but not mandatory.

Course Objectives:

• Gain a comprehensive understanding of generative AI, its core principles, and potential applications across various domains.

• Explore the fundamental concepts and architecture of Large Language Models, including their strengths and limitations.

• Master the inner workings of the Transformer architecture, the backbone of many powerful LLMs.

• Apply transfer learning techniques with pre-trained LLMs (e.g., GPT-3, BERT) for various Natural Language Processing (NLP) tasks.

• Develop skills in text generation using LLMs, exploring techniques like controlled text generation and different sampling methods (Greedy Sampling, Beam Search) to influence the output style and content.

• Master techniques for language understanding tasks with LLMs, including question answering, summarization, sentiment analysis, and topic modeling.

• Identify ethical considerations surrounding generative AI, such as potential biases in LLM training data and responsible use of generated content.

• Explore the exciting future directions of generative AI research and its potential impact on various industries.

Course Length: 5 Days

Course Outline:

Module 1: Introduction to Generative AI and LLMs

• Demystifying Generative AI: Explore the core concepts of generative models, their training methodologies (Generative Adversarial Networks, Variational Autoencoders), and potential applications in various fields (e.g., image generation, music composition). (2 hours)

• Unveiling Large Language Models (LLMs): Dive deep into the world of LLMs, their unique capabilities in processing and generating human-quality text, and their role in revolutionizing NLP tasks. (2 hours)

• LLM Applications in Action: Explore real-world use cases of LLMs across different industries, such as chatbots, machine translation, content creation, and code generation. (2 hours)

Module 2: Transformer Architecture and Pretrained Models

• Decoding the Transformer: Examine the core architecture of LLMs, focusing on the Transformer model's encoder-decoder structure, attention mechanism, and its ability to learn long-range dependencies within text data. (3 hours)

• Unveiling the Power of Pretrained Models (e.g., GPT-3, BERT): Explore the concept of pre-trained LLMs, how they are trained on massive datasets, and their ability to be fine-tuned for specific NLP tasks. (2 hours)

• Leveraging Transfer Learning: Master the art of fine-tuning pre-trained LLMs by adapting them to specific NLP tasks through techniques like adding task-specific output layers and retraining on smaller, domain-relevant datasets. (3 hours)

Module 3: Text Generation with LLMs

• Unleashing the Power of Text Generation: Explore various techniques for crafting creative text formats with LLMs, including generating different writing styles, poems, code, scripts, musical pieces, and email. (3 hours)

• Exploring Conditional Text Generation: Learn how to guide the LLM to produce specific outputs by providing prompts, keywords, or specific contexts to influence the generated content. (2 hours)

• Sampling Techniques in Action: Understand different sampling methods used in text generation, such as Greedy Sampling (selecting the most likely word at each step) and Beam Search (exploring a broader range of possibilities) to achieve desired outcomes. (2 hours)

Module 4: Language Understanding with LLMs

• Beyond Text Generation: Explore the world of Language Understanding tasks with LLMs, focusing on how they can be used for tasks like question answering, where the model needs to comprehend a question and provide a relevant answer based on its knowledge. (2 hours)

• Fine-Tuning LLMs for Understanding: Learn how to optimize LLM performance for specific NLP tasks through fine-tuning techniques like adding task-specific objective functions and training on labeled datasets relevant to the desired understanding task (e.g., question answering, summarization). (3 hours)

• Optional Module: Advanced Language Understanding Tasks with LLMs (time permitting): Explore additional NLP tasks that can be tackled with LLMs, such as sentiment analysis (identifying