It’s been one year since the spammageddon of Hacktoberfest requests and issues with contributing to only accepted opted-in repositories – but this year there are some good changes:
You can now use Gitlab for PRs.
Monetary donations are also encouraged for open source projects.
You can either get a shirt or plant a tree, for completing 4 PRs with improvements to participating open source projects.
Also, of course if you have a project yourself you maintain, you can add “Hacktoberfest” tag to get participants to find your repo. 🙂 What are some of the projects you might consider helping?
Continue reading “Hacktoberfest 2021 is here!”
In Brian Christian’s The Most Human Human, he first explains some of the early experiments in human-like chatbots – Eliza the therapist and the more recent attempts to “understand” language through the vast equivalent documents the UN keeps record of.
He also goes in to the question that plagued early industrialism, will robots be getting our jobs with AI? Well, if something is a repetitive and arduous task, as he puts it, you are the robot. In the art world an actor may do a play for a weekend or two… and no more – is that a waste? I his words:
Continue reading “Book review – The Most Human Human”
If you have been doing OpenCV or Python machine learning, you have most likely come across PyImageSearch. If you come across a sale for this service I would recommend trying it out if you are learning some machine learning. This past week they have run a free promo 7 day trial, which gave me an opportunity to browse these.
Continue reading “PyImageSearch Review”
In The Master Algorithm by Pedro Domingos, the author has some interesting thoughts on the “master algorithm”. Just like physicists want to find a universal formula for everything, a similar quest is what some in the machine learning world are looking for, and could simplify or bring new insights to how the world works through collected data. Just as many useful algorithms can be used in different ways – for example a super simple neural net can find pi, or a neural net can tell the difference between a dog and a cat, why can’t there be one that could run all machine learning problems?
Continue reading “The Master Algorithm Book Review”
Human pose estimation is something useful for robotics/programming as you can see what position a person is in a picture. For last weekend’s Hackrithmitic I did an experiment for fun using computer vision pose estimation. To start with I found several possibilities with available libraries:
Continue reading “Human pose estimation with Python and Gluoncv”
- Tensorflow js has been used to say, don’t touch your face, but it takes a massive amount of cpu.
- Openpose is a popular one, only licensed for noncommercial research use, and there is a Opencv example for it that doesn’t quite show how to use it.
- AlphaPose is supposedly faster and has a more clear license and possibility for commercial use – if you want that as a possibility. I checked out the install instructions and worked but for “python3” instead of “python”. It also misses obvious step of installing cuda for your Nvidia system before running.
- GluonCV is another, which seems more user friendly. This one I was able to get running in a few minutes with their example:
Thousands of years ago, probably around the invention of the wheel, and before the time of Solomon, humans must have been measuring various objects… calculating some distances, and wondering, is there a better way to measure how far around the outside of a wheel is compared to its diameter? Continue reading “Neural Networks Part 2: Learning Pi”
Keras is a library that makes machine learning easy to run and train without knowing too much of the math behind it. It has many tutorials including an excellent howto by Egghead.io, Pyimagesearch and of course the official documentation and books…
…but what if you want to look into the details of how it works? Neural networks are a series of functions that are adjusted over time, and we can “see” what happens in a simple example. Continue reading “Into Neural Networks! Part 1”