Fine-tuning ChatGPT with Synthetic GPT-4 Datasets - A Python Implementation
Master fine-tuning ChatGPT using Python and synthetic GPT-4 datasets through hands-on implementation of multi-GPT system interactions and advanced prompt engineering techniques.
AI Agents: Understanding LLM Systems and Task-Specific Tools - An Introduction
Discover the fundamentals of AI agents, their interaction with LLMs, and practical implementation methods in this beginner-friendly exploration of modern artificial intelligence.
ChatGPT Prompt Engineering with In-Context Learning - 7 Examples
Master prompt engineering techniques through 7 practical examples, from zero-shot prompting to reverse Turing tests, while exploring innovation theory and strategic AI applications.
ChatGPT Prompt Engineering with In-Context Learning Using OpenAI API
Master prompt engineering techniques for ChatGPT with hands-on practice using In-Context Learning and OpenAI API integration in Python through a practical COLAB environment.
Generative AI and LLM Technologies - A Comprehensive Tutorial
Explore essential concepts and technologies behind generative AI systems, from pre-training and fine-tuning to prompt engineering and advanced data structures for LLMs.
Uncertainty, Prompting, and Chain-of-Thoughts in Large Language Models - Part 2
Dive into advanced AI concepts including temperature scaling, Bayesian calibration, prompt-based finetuning, and chain-of-thought techniques for more reliable and explainable language models.
Prompting for Evaluation of Free-Text Explanations - Part 2
Dive into advanced techniques for evaluating free-text explanations in AI prompting, focusing on methodologies and practical applications for data science analysis.
Getting Started with DSPy - Building LLM Programs Tutorial
Discover how to build robust LLM programs using DSPy framework, from basic concepts to creating retrieve-then-read RAG systems with optimized instructions and structured data handling.
LLM Prompt Personalization: A Step-by-Step Guide to Creating Custom AI Prompts
Master the art of creating personalized LLM prompts through a straightforward, code-free approach that optimizes your interactions with AI language models for better results.
Fine-tuning ChatGPT with In-Context Learning - Chain of Thought, AMA, and ReAct Reasoning
Master advanced in-context learning techniques like Chain-of-Thought and ReAct to optimize ChatGPT's performance without expensive fine-tuning, based on latest research insights and practical applications.
Retrieval Augmented Generation (RAG) - Advances in Generative AI
Dive into retrieval augmented generation (RAG) to enhance AI systems with external knowledge retrieval, improving accuracy and context awareness in generative AI applications.
Prompt Engineering: Implementing Tree of Thoughts with GPT-4 - AI Education Case Study
Explore the implementation of Tree of Thoughts (ToT) framework in GPT-4, examining its application for AI education planning and demonstrating advanced prompt engineering techniques for complex problem-solving.
Chain of Thought and Instruction Fine-Tuning for Enhanced Language Model Performance
Explore how Chain of Thought and instruction fine-tuning techniques enhance language models' performance, from basic reasoning tasks to simulating human behavior and decision-making processes.
Connecting Multiple ChatGPT Sessions with Dynamic Instruction Prompt Templates
Master advanced prompt design techniques to connect and optimize multiple ChatGPT sessions using Dynamic Instruction Prompt Templates (DIPT), enabling continuous learning and improved effectiveness across conversations.
Instruction Fine-Tuning and In-Context Learning of Large Language Models with Symbols
Delve into advanced concepts of LLM instruction fine-tuning and in-context learning, exploring symbol tuning, FLAN-PaLM models, and cutting-edge research developments.
LangChain - Integrating External Documents and SQL with GPT-4
Dive into LangChain's capabilities for integrating external documents and SQL with GPT-4, learning to chain multiple data sources and leverage advanced language models effectively.
Phi-2: Small Language Model Performance Tutorial - Google Colab with Python
Explore Microsoft's Phi-2, a compact yet powerful language model outperforming larger counterparts. Learn its training methods and test capabilities in coding, math, reasoning, and data extraction using Google Colab.
SDXL Turbo - Instant High-Quality Text-to-Image Generation with One-Step AI Model
Explore SDXL Turbo, a fast text-to-image AI model. Learn setup, key parameters, and generate high-quality images across various themes. Discover its versatility and speed in creating stunning visuals.
Imagined Dragons: Building an Imagination-Powered Music Recommendation Platform
Explore building an AI-powered music recommendation platform using LLMs. Learn prompt engineering, chatbot memory management, and open-source tool creation for innovative mixtape generation.
Langchain with Golang
Explore LangChain's implementation in Go, featuring a live demo showcasing its capabilities for building powerful language model applications.
Advanced NLP: Prompting and Complex Reasoning - Lecture 7
Explore advanced NLP techniques including prompting methods, sequence-to-sequence pre-training, and complex reasoning strategies for enhanced language model performance.