Nvidia stock price growth

+$270B in a day!

Markets News, Feb. 23, 2024: Stocks Close Mixed to Cap Winning Week; Nvidia Briefly Tops $2T

Nvidia (NVDA) briefly jumped above a $2 trillion market capitalization in early trading, making it only the fourth U.S. company to reach the milestone. The company added $270 billion to its market cap yesterday in the largest ever single-day jump in market value.

nvidia stock price - Google Search

E167: Nvidia smashes earnings (again), Google's Woke AI disaster, Groq's LPU breakthrough & more -
All-in-podcast @YouTube

revenue

ML training data: Bikeshare rides

 System Data | Capital Bikeshare

...  invite developers, engineers, statisticians, artists, academics and other interested members of the public to use the data we provide for analysis, development, visualization

Index of bucket "capitalbikeshare-data"

https://aka.ms/bike-rentals

Exercise Part 3: Explore Data | Coursera

Exercise Part 4: Train a Machine Learning Model | Coursera

AI: Semantic Kernel vs LangChain

Semantic Kernel 101

Currently, the Copilot approach rules the artificial intelligence (AI) world, and in that world, Microsoft created Semantic Kernel as a framework for building its own Copilots. Now, you can use Semantic Kernel too. Semantic Kernel (SK) is an open-source AI framework, created by Microsoft for .NET, Python, and Java developers working with Large Language Models (LLMs). Its purpose is threefold: To abstract away the underlying LLMs, APIs and tooling; to handle more complex implementations in a generic way; and to make it easy to integrate your own content. Semantic Kernel is very similar to LangChain, a popular open source LLM framework for Python and JavaScript

Introduction | 🦜️🔗 Langchain

CODE Consulting - Services - Artificial Intelligence (AI)

microsoft/semantic-kernel: Integrate cutting-edge LLM technology quickly and easily into your apps @GitHub (MIT)

C#, Python, Jupyter

LangChain.com

LangChain - Wikipedia

LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.

langchain-ai/langchain: 🦜🔗 Build context-aware reasoning applications  @GitHub (MIT)

Python, Jupyter

Project Jupyter - Wikipedia