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Let's meet every other week or so to work on ML problems and get really good fast! Machine Learning involves many disciplines (Calculus, Probability, Linear Algebra, Coding, Mining, Deployment, Data Vis) and some of them lend themselves well for group study. It's not about discussing AI papers.
Although I love the beauty of mathematical proofs and their intricacies... I want to focus on cementing insights with code.
That means working with data and getting results (predicitve models). The goal here is to become solid at doing and eventually deploying. (pair-coding and peer-testing might be a good option let's see)
If you're game you should have at least completed 1+ ML course, implemented some algorithms or have experience with one of the disciplines above so you can contribute to the group. (~~ beginner/intermediate skill level)
We will use Python.
Hope to see you soon,
Markus
Good Resources – all you need is Jeremy Howard (and Love):
Fast.ai (ML for coders) is by orders of magnitude!! better and more up to date than anything else. Jeremy Howard is the most practical and legit guy in the field. He was Chief Scientist at Kaggle, founded a few other startups and is a great teacher.
This one just came out in September 2018: http://www.fast.ai/2018/09/26/ml-launch/ and it is amazing. Or do the Deep Learning 2018 course for images and so on.
Refresher / Lookup on python / pandas
https://github.com/jakevdp/PythonDataScienc...
Only use as reference, not as homework.
Meh/Solala Resources
for Mobile or Ubahn boredom: Brilliant.org (math, lin alg and ML quizzes) – most paid quizzes are sh%t, but 1/3 are well done.
Bad Resources. DON'T WASTE YOUR TIME THERE:
BAD: datacamp.com ('Interactive copy paste with 100% irrelevance to any real world problem taught by people after at least one stroke')
BAD: Andrew Ng's Coursera course. Dated, pedagogically inferior and not pragmatic. You'll finish it and won't be able to do anything after...
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AI Deep Dive (Virtual) - Building Self-Improving Agents
·OnlineOnlineImportant: Register on the event website to receive the joining link. (rsvp on meetup will NOT receive anything).
This is virtual event for our AI global community, please double-check your local time. Can't make it live? Register anyway to receive the webinar recording.
Join Snowflake to learn how to build self-improving agents
Tech Talk: Your Agent Should Fix Itself: Building Self-Improving Agents
Speaker: Elliot Botwick, Principal AI/ML Architect, Snowflake
Abstract: In this talk, we will share how coding agents help developers build high quality agents faster.
A key insight from building agents in production is that high quality agents operate with their goals, plans and actions aligned. We introduce the Agent Goal-Plan-Action (Agent GPA) framework to capture this insight, which achieved state of the art benchmarks on TRAIL/GAIA with 95% error coverage and 86% error localization.
The Agent GPA framework assesses the full agent's process:- Was the goal achieved efficiently?
- Did the plan make sense?
- Were the right tools used?
- Did the agent follow through?
Without visibility into these steps, teams risk deploying agents that look reliable but create hidden costs in production. Inaccuracies can waste compute, inflate latency and lead to the wrong business decisions, all of which erode trust at scale.
We will show how to use coding agents to automate the process of measuring and improving your agent's GPA by using optimization skills that take advantage of the GPA evaluation framework. By the end, you’ll be able to use coding agents and the GPA framework to identify common agent failures, improve their agent and make it ready for production.Venue:
Virtual, join from anywhereMore virtual sessions:
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