I&#x27;ve watched all 8 available videos, which is as far as my knowledge goes but it has been background on gradients, calculating derivatives, introduction to word vectors and how they relate to each other, recurrent neural nets and how to push time series through, introduction to tensor flow and finally how to scan backwards and forwards through &quot;time&quot; in a recurrent RNN (each word in a sentence is a time step in NLP).<p>Word vectors are &quot;just&quot; high dimensional entities - 100-300 dimensions, used as input. So the introduction to them was about how you go about building a dataset that is a collection of 50,000 column vectors each of which is 300 rows. And then how to use that to go on and build a neural net to do useful work.<p>The conclusion is that all the work done on syntax, grammar and word classification can effectively be replaced by having a huge corpus (e.g. all of wikipedia is small), 300 dimensions for each word and then a loss function to classify each word.<p>One can imagine how that would be applied to sales data of multiple products or other data.<p>It foes on to suggested how sentiment analysis is performed and how entity recognition would work (entities being places, names of people and companies).<p>The info has been general but described in terms of NLP, the techniques so far are not just for use in NLP.<p>I&#x27;m not an NLP person and tbh I&#x27;ve never even made a neural net (although I could if I had a reason) I&#x27;m just interested in the subject.