Let’s say we have an N-class semantic segmentation problem. Now on each iteration (for each batch) we can calculate Dice loss in two ways: (1) calculate average loss over classes for each sample in a batch and after that get the average over batch, or (2) calculate average loss per class in a batch and then average over classes presented in a batch. Which one is better and why? Or there is no difference at all? Can it affect on how model learns to segment small or big objects? Any related articles?

We are working on a tool, which is supposed to help developers test, understand and explain their machine learning models. The tool will be open-source and free to use.

We are in the prototyping stage and have a prototype ready to show. If anyone would like to have a look and give us feedback, that would be awesome!

If you have experience with testing models and frameworks like SHAP and LIME, it would be even more valuable.

Hello guys!
I’m training GRU neural network with single GRU layer (among other layers), and I tend to think that hidden to hidden transformation requires severe non-linearity to correctly “merge” memory with current timestep (and thus update hidden).
How do I approach this, what is the best practice?
Should I add more GRU layers or should I, for instance, add extra layers to hidden to hidden transformation with nonlinearity like relu?
If I take second approach I guess I should use tanh instead of relu to avoid exploding gradients, am I correct?

Thanks in advance.

I am currently doing a literature search of sparse training architectures, I am thinking of making a library for sparse training and hoping to make it as universal as possible. For that, I would need to get a good idea for the various use cases.

ML newbie here. I see ALL ML classes have these methods. Can someone explain, in plain English, what do they do?

Also, what is the difference between using fit, then transform on a dataset and fit_transform on the same?

When building a time series model using something like xgboost or lightgbm, do they only take into account the present row / instance?

Say I build a model that has in one row:

• current values at timestamp

• lagged values from t-1 to t-5 of the previous values

If the rows/instances are shuffled for k fold cross validation, will that affect prediction ?

I’m interested in transitioning from web development to machine learning.

My math background is mediocre (basic calc) and my only programming experience is in web dev technologies (3 years). I want to be realistic about my effort level and opportunities. I probably won’t be going back to college, but have 10 months left on a work contract and would like to spend any free time learning the basics.

1. What is the feel of competition in the field? With my time-frame and background, is it reasonable to think I can earn an entry-level position?
2. I’ve heard the field is broken up into data science and data modeling. Is data modeling more programmatic? Will my experience as a dev be more relevant there?

I just wrote an article (quite long) about how we’ve build a semantic similarity index alongside the ElasticSearch and used both to provide smarter search results.

Tools used:

• Tensorflow and Simple Tensorflow Serving
• Pre-trained Universal Sentence Encoder
• Docker
• Python 3
• FAISS by Facebook
• ElasticSearch
• gRPC for the API

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Link to the article: https://blog.onebar.io/building-a-semantic-search-engine-using-open-source-components-e15af5ed7885

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I hope, this would be helpful as a practical “how-to” for anyone, trying to build a semantic search engine for their project.

https://www.eventbrite.com/e/pytorch-summer-hackathon-in-menlo-park-registration-63756668913

The eventbrite website will give you a welcome message, but you have to wait until your application is reviewed by Facebook staff and give you a confirmation message. Space is limited and not all applicants will be admitted into the hackathon.

Btw, to anyone who got in, the official website send via email is officially up now (it was down earlier this week), you can use it to find teammates.