A brand new ML book series by Kevin Murphy renewing his previous ML textbook(Machine Learning: A Probabilistic Perspective).
The first of two is available online as a pdf and the second is to come in 2022.
Two books to rule them all. 🙂
* book website
* Probabilistic Machine Learning: An Introduction
* Probabilistic Machine Learning: Advanced Topics
* repo for python 3 code for “Probabilistic Machine Learning”
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It’s been a while since the last time I took a visit to my own blog.
Things have changed exponentially for the last 6 years or so in AI.
And I’m back to post on my journey to AI from time to time.
Finally acquired the statement of accomplishment for Machine Learning on Coursera by Andrew Ng. Actually it took me more than 2 years to make it since the first opening of the class in the year of 2012. I don’t wanna make any excuse for such procrastination but, as others would do, I used to have some difficulty sparing the time for the class solely. Yeah, shame on me but I finished the course with a record of 100%. 🙂
Anyway, I’m personally proud of what I’ve done for the last couple months. And deep appreciation to Andrew Ng for this encouraging class.
Statement of accomplishment: Coursera ml 2014
Just caught a mention by ML reddit on NuPIC – the very open source project I’ve been looking for. Feels like I almost found a holy grail in the quest for intelligence.
i just found a blog on computer vision and machine learning, and got a chance to have a fundamental and clear understanding of the curse of dimensionality.
the posting above provides a very thorough and fundamental explanation about the essence of the curse of dimensionality. the other postings on that blog also seem to be definitely must-read articles for ML newbies, just like me. 🙂
it would be really worth reading the whole materials on that blog, and that’s what i’m gonna do for the next week.
Analyze and visualize data, together.
One sorta fresh & brand-new social network community, or something alike, service for data scientists or people with interest in data science: plotly. Just got signed up out of pure curiosity and now I think it’s definitely worth a go for data geeks. Planning to make my own life-logging record with a few gadgets like arduino, specifically, it’s really an appealing feature to provide users with the arduino API to stream data from hardware devices, as written in the front page of their site. Better pay visits more often.
I happened to find these undergraduate ML course video clips opened on youtube by UBC. I think I really have to thank to UBC & the professor Nando de Freitas for this valuable sharing. Anyone with strong interest or something in ML should definitely be better to take a look through the whole sessions. That’s what I’m gonna do for months now.
Here’s a community-driven and educational guidance with current focus on machine learning and probabilistic AI – Metacademy. It will make a great assistance for those with strong interest and enthusiasm in machine learning, just like me. 🙂 It draws an intuitive map of sequential steps towards the concept you queried about. You’ll see once you give it a try.