
Explore GPT-4's ability to generate and evaluate system messages, enhancing AI-assisted coding and prompt engineering for more effective language model interactions.

Learn to build an AI-powered video search app using YouTube data, Pinecone, and Streamlit. Covers data collection, enhancement, indexing, and querying, with practical code examples and implementation steps.

Learn to build a Streamlit interface for unified chat, enabling automated conversations between LLMs and user interactions with various models. Explore code implementation, setup, and key features of this versatile chat application.

Discover how to create efficient chatbots using OpenAI and Streamlit for data visualization and simulation, enhancing internal processes in insurance and asset management.

Master ChatGPT's Python API capabilities through hands-on demonstrations of text completion, chat, image generation, and speech features, culminating in building a Streamlit demo application.

Learn to automatically select vector indexes and prompts for document QA using Langchain router chains, with code demos for Streamlit and terminal applications.

Learn to build a Python app for audio content moderation using AssemblyAI API and Streamlit. Transcribe audio, analyze content, and create an interactive tool for efficient moderation.

Learn to enhance YouTube search using OpenAI Whisper, transformers, and vector search. Build a system for precise content retrieval, transcription, and question-answering from YouTube videos.

Build a ChatGPT-like chatbot using Langchain, GPT-3, and OpenAI embeddings. Includes Streamlit UI, file uploader, and Wikipedia API integration for answering questions based on uploaded documents and relevant context.

Comprehensive guide to deploying ML models: from prototyping to production, covering architectures, optimization techniques, scaling strategies, and edge deployment considerations.

Learn to create interactive AI chat applications using Streamlit and LangChain, combining powerful language models with specialized tools for enhanced functionality and user experience.

Learn to build a lip-reading application using Python, TensorFlow, and Streamlit. Gain hands-on experience in machine learning and computer vision while creating a practical, cutting-edge project.

Learn to build a GIF search engine using semantic search, exploring data preparation, vector databases, and querying techniques for more accurate and context-aware results.

Learn to create an OpenAI and LangChain-powered web app using Streamlit. This tutorial covers setup, app building, deployment, and showcases a live text globalization application.

Build a GenAI support bot using Docker, Neo4j, LangChain, and Ollama. Learn to create vector embeddings, integrate LLMs with knowledge graphs, and develop natural language Q&A systems.

Explore OpenAI Assistants API for RAG systems: create AI agents, upload files, and support 10,000 documents. Learn Python implementation and code review for advanced AI assistance.

Learn to create an AI coding assistant using Vision GPT, exploring its capabilities in understanding text and visuals for enhanced code development. Includes live demo of building a Streamlit app.

Aprende a desplegar aplicaciones en GCP: clona repositorios, instala Docker, crea imágenes y sirve aplicaciones con StreamLit.

Deploy ML models for forensic anthropology using Docker and Streamlit. Learn to create a web application for skeletal sex prediction, combining archeology, physics, and technology for impactful research in bioarchaeology.

Explore building a customizable ML experiment tracking system using DVC for versioning and Streamlit for data exploration, focusing on practical implementation with Python APIs.

Learn to create interactive GUIs and web apps using streamlit in Python. Hands-on exercises cover basics, data visualization, custom layouts, and integration with popular libraries for effective data science projects.