Anton Osika sur Twitter : "Quite a nice visualisation: it shows the No free lunch theorem. In #machinelearning. Thx to pythons @scikit_learn. Radim Řehůřek : Word2vec Tutorial. I never got round to writing a tutorial on how to use word2vec in gensim. It’s simple enough and the API docs are straightforward, but I know some people prefer more verbose formats. Let this post be a tutorial and a reference example. Preparing the Input Starting from the beginning, gensim’s word2vec expects a sequence of sentences as its input.
Each sentence a list of words (utf8 strings): Keeping the input as a Python built-in list is convenient, but can use up a lot of RAM when the input is large. Gensim only requires that the input must provide sentences sequentially, when iterated over. For example, if our input is strewn across several files on disk, with one sentence per line, then instead of loading everything into an in-memory list, we can process the input file by file, line by line: Note to advanced users: calling Word2Vec(sentences) will run two passes over the sentences iterator. The second pass trains the neural model. Training Memory Evaluating Storing and loading models Outro. API Documentation. Word2vec - Google research tool for computing vector representations of words. : MachineLearning.
Deep learning for NLP : MachineLearning. Deep Learning 101. Deep learning has become something of a buzzword in recent years with the explosion of 'big data', 'data science', and their derivatives mentioned in the media. Justifiably, deep learning approaches have recently blown other state-of-the-art machine learning methods out of the water for standardized problems such as the MNIST handwritten digits dataset. My goal is to give you a layman understanding of what deep learning actually is so you can follow some of my thesis research this year as well as mentally filter out news articles that sensationalize these buzzwords. (source) Imagine you are trying to recognize someone's handwriting - whether they drew a '7' or a '9'.
From years of seeing handwritten digits, you automatically notice the vertical line with a horizontal top section. If you see a closed loop in the top section of the digit, you think it is a '9'. If it is more like a horizontal line, you think of it as a '7'. So how do you build a deep representation of input data? (source) Main Page - Ufldl.