In 2013, Mikolov et. al published ‘Distributed Representations of Words and Phrases and their Compositionality‘, a paper about a new approach to represent words by dense vectors. This was an improvement over the alternative, representing words as one-hot vectors, as these dense vector embeddings encode some meaning of the words they represent. In other terms, words with similar meaning are be close to each other in the vector space of the embedding. For example, “blue” would be close to “red” but far from “cat”. A commonly used name for their approach is word2vec.
If you want to calculate a set containing all subsets of set (also called power set) you could either choose an recursive approach or try this iterative approach which is faster than the recursive one.
def get_subsets(fullset): listrep = list(fullset) subsets =  for i in range(2**len(listrep)): subset =  for k in range(len(listrep)): if i & 1<<k: subset.append(listrep[k]) subsets.append(subset) return subsets subsets = get_subsets(set([1,2,3,4])) print(subsets) print(len(subsets))
You can also find a shorter version at the end of the article, but to understand the principle the algorithm above is more suitable.
There are many tasks in image processing that can be solved with Convolutional Neural Networks (CNNs). One of these tasks is called image style transfer. The goal of image style transfer is to apply the style of one image to the content of another image. This way you can create an drawing showing you in the style of Van Gogh, for example.
I am going to explain how style can be extracted from one image and transferred to the content of another image in this article.
I also wrote an overview paper on Image Style Transfer using Convolutional Neural Networks for a computer vision seminar at my university.