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light on math machine learning: intuitive guide to understanding word2vec. here comes the third blog post in the series of light on math machine learning a-z. this article is going to be about word2vec algorithms. word2vec algorithms output word vectors. word vectors, underpin many of the natural language processing (nlp) systems, that have taken the jupyter notebook for this tutorial: here recently, i had to take a dive into the seq2seq library of tensorflow. and i wanted to a quick intro to the library for the purpose of implementing a neural machine translator (nmt). i simply wanted to know “what do i essentially. make cnns for nlp great again! clas*ifying sentences with cnns in tensorflow. tensorflow version: 1.2 original paper: convolution neural networks for sentence clas*ification full code: here rnn can be miracle workers, but… so, you’re all exhausted from trying to implement a recurrent neural network with tensorflow to clas*ify sentences? you somehow wrote some tensorflow code that looks like a rnn but unable... glove: global vectors for word representation + implementation. hi, this post will be about a new word2vec technique that has come after skip-gram and cbow, introduced in this paper. why the authors claim that glove is better than context-window based methods is that, it tries to combine both global and local statistics in order to. a paper in thousand words: dueling network architectures for de*p reinforcement learning. paper is found here introduction and the contribution this paper’s main contribution is that instead of using standard neural network architectures such as convolution neural networks (cnns) and long-short term memory netowrks (lstms). for example, de*p q-networks uses a standard cnn. the authors of this paper introduce an alterations to... thicken your wallet with ml: predict stock price movements with lstms. this is a tutorial on how to use lstms for stock price movement prediction. i have seen quite a few tutorials on using lstms for stock