Here is a sample of 64 of the images when trained on r/EarthPorn:

https://i.imgur.com/K9mU7vH.jpg

Here is the code:

https://github.com/manicman1999/StyleGAN-Tensorflow-2.0

And finally, here is the live web demo:

https://matchue.ca/p/earthhd/

Enjoy!

So I’m an IB high school student and the IB curriculum requires us to write an extended essay, which is a 4000-word mini-research paper answering a research question. As I’m interested in machine learning, but CS is not available at school, I chose to write about mathematics. I explored some basic stuffs such as gradient descent and back propagation; but they are too fixed and I can’t seem to formulate a question around them. Can you guys suggest some interesting mathematics in machine learning to investigate on?

Also, an adult friend of mine suggest me to try “beta distribution”, but after an hour of research I can’t find the relationship between it and machine learning. Some insight will be hugely appreciated. Thanks.

I’ve been researching metric learning algorithms for a while now, and in the process I discovered some issues with the field.

You can read about it here: https://medium.com/@tkm45/benchmarking-metric-learning-algorithms-the-right-way-90c073a83968

TL;DR:

1. Many papers don’t do apple-to-apple comparisons. They change the network architecture, embedding size, data augmentation, or just use performance-boosting tricks that aren’t mentioned in their paper.
2. Most papers don’t use a validation set.
3. Two baseline algorithms (triplet and contrastive loss) are actually competitive with the state-of-the-art, but are not presented this way in most papers.
4. I’ve made a flexible benchmarking tool that can standardize the way we evaluate metric learning algorithms. You can see it here: https://github.com/KevinMusgrave/powerful_benchmarker

I have 2 datasets where the covariates used are the same but the scale used are different. For example, dataset 1 could have scale for pressure parameter at 0.1, 0.2, 0.5, 0.8 and dataset 2 scale for the pressure parameter is 0.3, 0.8, 0.2, 0.9, 1.0

so far I have plotted the distribution for the response variable (dependent variable) and dataset1 and dataset2 have different distributions for the response variable but if I understand correctly, covariate shift occurs when the distribution of the covariate differs between dataset1 and dataset2.

How can I properly check this assumption?

If I am planning to train a model on dataset1 to be tested for dataset2 with a different distribution, what are the methods that can be used ( this is a regression task) besides Neural Network?

Thank you.

Here is a sample of 64 of the images when trained on r/EarthPorn:

https://i.imgur.com/K9mU7vH.jpg

​

Here is the code:

https://github.com/manicman1999/StyleGAN-Tensorflow-2.0

​

And finally, here is the live web demo:

https://matchue.ca/p/earthhd/

​

Enjoy!

Hi r/MachineLearning,

I wrote a blog post that I hope could be the gentlest way for you to start playing with BERT for the first time;

https://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/

It uses a lighter version of BERT (the distilled version from HuggingFace, distilBERT) to do sentence embedding, then uses Scikit Learn for Linear Regression classification. As a first exposure to BERT, I’m having people use the general trained model and not worry about fine-tuning for now. After getting people through this initial hump, I’m hoping readers would get more comfortable doing more exploration and poking around with the model and its usecases.

​

I hope you enjoy it. All feedback/corrections are appreciated.

I am creating a project using a javascript machine learning library to run an Image Classifier in the browser. Please help me fill out this google form: https://forms.gle/8TfZMMwp46VgARPM9

I need to collect as much data as possible. Much appreciated!