tarantula | big hairy fuzzy spider that crawls your site , wreaking havoc | Crawler library

Since it looks like the last element is followed by

This issue has been confirmed to be due to a system bug.

The workaround is to wrap the delete logic in NSManagedObjectContext.perform. For example:

Absolutely! But...

Such models tend to need significantly larger and deeper neural-networks to learn the representations.

Word2vec uses a very-shallow network, and performs a simple prediction of neighboring-words, often from a tightly limited vocabulary, as the training-goal which (as a beneficial side-effect) throws off compact vectors for each word.

Image-centric algorithms instead try to solve labeling/classification tasks, or regenerate original images under compressed-representation (or adversarial-classifier) constraints. They use 'convolutions' or other multi-layer constructs to intepret the much-larger space of possible pixel values, and some of the interim neural-network layers can be interpretable as compact vectors for the input images.

Note that even in textual word2vec, the individual "dense embedding" dimensions, learned in an unsupervised fashion, don't have neat human-interpretability (like "bigness", "cuteness", etc.). Often, certain directions/neighborhood of the high-dimensional space are vaguely-interpretable, but they're not precise nor aligned exactly with major dimension axes.

Similarly, any compact representations from deep-neural-network image-modeling won't inherently have individual dimensions with clear meanings (unless specific extra constraints with those goals were designed-in) – but again, certain directions/neighborhoods of the high-dimensional space tend to be meaningful ("crowds", "a car", "smiles", etc).

A nice overview of some key papers in deep-learning based image-analysis – the kinds of algorithms which throw off compact & meaningful vector summaries of images – that I just found is at:

https://adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html

I rewrote your code a bit to one function and used max_row+2 to cover the base case with the empty cell as well. Hope that this help.

Your tags are wrong in the html, first you are opening a

But the main reason why you could be not getting a thing, is because you have your buttons ouside the

Use itertools.groupby with sorted:

This is what you can try.

Use itertools.groupby to collect together teams with the same score, then iterate through each group:

have you tried the CSV python lib? Extracted from the doc (https://docs.python.org/3/library/csv.html):