https://github.com/annomator/annomator_1.0

Annomator automatically generates text, box and mask data for direct use or training. Use the automatic image annotation or start from scratch. Simply paint each mask with the right color code with no external text file needed. It is compatible with all the MSCOCO models in the Tensorflow Model Zoo.

I have just trained my first CNN network by using the MNIST dataset, it is the most famous handwriting dataset. However, instead of using their testing images, I want to utilize my own 28×28 testing images.

The rationale behind this, is that I want to make a handwriting recognition program, so obviously I need a way to convert traditional image format to the one-dimensional MNIST format, so that the CNN can read it.

What is the best way to accomplish this task?

Just finished writing this library for javascript implementing the neat algorithm.

Take a look if you are interested

https://github.com/ExtensionShoe/NEAT-JS

a question: Would you prefer Google coral to Raspberry Pi 4, or Jetson Nano? will they replace the other? which one is better for machine learning?

welcome to share your opinion with me!

Francis Bach just started a blog, and this is his first blog post if anyone is interested:

The “η-trick” or the effectiveness of reweighed least-squares

Optimizing a quadratic function is often considered “easy” as it is equivalent to solving a linear system, for which many algorithms exist. Thus, reformulating a non-quadratic optimization problem into a sequence of quadratic problems is a natural idea.

Greetings all,

I’ve recently been recommended to read Hidden Technical Debt in Machine Learning Systems, and have found its insights valuable. Knowing that this will benefit at least one upcoming project I am working, what other essential reading is out there for taking machine learning projects to production in a principled manner? Whose other mistakes can I learn from before making my own?

Hi all, I’m sorry if this is not the appropriate place to post such a question, but I’d be really happy if you guys could shed some light on my situation.

Without giving too much detail, I graduated with a BSc a few months ago from a small university in [STEM field; not CS]. I joined a research lab during this time in my field that involves some computational work, and ended up doing a little project for fun applying machine learning to this field, which never evolved to yield novel results. Now, I am applying to MSc programs in CS, but am also considering just joining industry doing something in programming.

I don’t think that little project is noteworthy at all; in fact, it is nearly identical to something done almost 5 years ago, with a slightly different algorithm. However, my professor is now pushing me to write a paper about the project, which will make it easier for the lab to get grants and pursue more interesting machine learning work in the future (strangely, I will not be there anymore though, and no one else in the lab seems to have a big interest in ML). Moreover, the professor is expecting me to educate all of the other lab members on how to use python, making sure GPUs are in order, training ML algorithms, etc. since I was the one that started the project.

Here are the questions that I’d be happy if you could give me some input: (1) do you think it is better to publish a paper that is not interesting than to not publish at all, and (2) do you think that it is reasonable to ask a student after graduation to help out in the lab? If so, for how long?

Thank you very much in advance for your comments.