The higher the batch size, the more memory space you'll need. Namely this answer: > In the neural network terminology: > one epoch = one forward pass and one backward pass of all the training examples > batch size = the number of training examples in one forward/backward pass. To understand epochs and how they differ from iterations (from above), check out () StackOverflow post. Now we'll build our vocabulary and train our model. 0 means update all weights in the output layer of the neural network. negative is the negative sampling rate.Words representing more than this will be eligible for downsampling. iter is the number of iterations, the number of times the training set passes through the algorithm.Unlike most numerical datasets that have 2 dimensions, text data can have hundreds or even more. window is how many words in front and behind the input word should be considered when determining context.min_count specifies a term frequency that must be met for a word to be considered by the model.workers is the number of threads used to train the model.min_alpha is exactly what it sounds like, the minimum alpha can be, which we reduce after every epoch.Essentially, the learning rate is, as stated in the link, 'how quickly a network abandons old beliefs for new ones.' A very intuitive explanation for learning rate can be found here. `negative` is the negative sampling rate.

`iter` is the number of iterations, the number of times the training set passes through the algorithm.

`window` is how many words in front and behind the input word should be considered when determining context.

`min_count` specifies a term frequency that must be met for a word to be considered by the model.

`workers` is the number of threads used to train the model.`min_alpha` is exactly what it sounds like, the minimum `alpha` can be, which we reduce after every epoch.A very intuitive explanation for learning rate can be found ().

Kid cudi tech n9ne songs