Google Vertex AI Python SDK and Gemini for Data Science and Machine Learning
Explore Google Vertex AI, SDK, and Gemini through practical demonstrations and hands-on coding examples for machine learning and data science applications.
Google App Engine: Deploy Machine Learning Applications in Google Cloud
Deploy machine learning applications on Google Cloud using App Engine. Learn to leverage cloud infrastructure for scalable ML solutions.
The Science of LLM Benchmarks: Methods, Metrics, and Meanings
Explore LLM benchmarks, evaluation metrics, and critical assessment techniques to understand model strengths and limitations in this comprehensive talk on AI performance evaluation.
LangGraph AI Agent Upgrade: Integrating Groq, Gemini, and Chainlit Front End
Upgrade a LangGraph AI agent with Groq, Gemini, and Claude integrations, fix bugs, and create a stylish Chainlit front-end for web searching capabilities similar to Perplexity.
Gemini: A Background to LLMs and Google's Newest Multimodal LLM
Explore the history of large language models and discover Gemini, Google's advanced multimodal AI. Learn about its capabilities, applications, and potential impact on various industries.
Comparing AI Data Integration Methods: Long Context Windows, RAG, and Model Tuning
Explore the key differences between long context windows, RAG, and model tuning for AI applications, understanding when and how to implement each approach effectively.
Google Gemini vs GPT-4: Understanding the LMSYS Arena Leaderboard and Gemini-exp-1121
Discover how Google's Gemini-exp-1121 model competes with GPT-4 on the LMsys Arena leaderboard, including ELO calculation methods and basic performance testing insights.
Improving Gemini Responses with Google Search Access Integration
Discover how to enhance Gemini's responses by integrating Google search capabilities through AI Studio's platform, featuring practical demonstrations and implementation techniques.