So in this section we’re going to take everything we’ve learned and we’re going to build our first neural network to train over the datasets that we just created. Now what I’d like for you to do for this project is to start with your neural net form the last chapter. I guess the last module that you did where you built a basic neural network for predicting on a structured data dataset. Then I would like to take this three layer neural network and remove the non-linearity in the hidden layer. I’ll show you why later. Then I would like for you to use the functions that we created above to generate the trained data on the fly. So a review and a label goes in. It’s converted into the two vectors that we need for the input and output data. And then a forward pass and a back prop pass happen, so that the data is being trained on the fly. Next thing I would like for you to do is create a function for pre-processing the data. So that all of these kind of vocabulary variables, and word2index variables are variables of this class, so everything is self-contained in that class. And then modify the train variable to actually train over the entire corpus, instead of just on one inputs and targets list. So, that’s kind of what I would like for you to do. You can start with either with this shell, that was presented at the beginning of your last week’s chapter or with the complete neural net that you started with last time. Now if you do need help, obviously the first thing to do is to go re-watch the previous week’s Udacity lectures, make you’re familiar with that propagation and it’s gradient ascent, and the error measure that we’re using and also how to modify back prop to get rid of non-linearity. And if you still need more help, go ahead and check out chapters three through five of Grokking Deep Learning. I’ve included the 40% off code here. It does a comprehensive review of fore prop, back prop and error gradients and stochastic gradient descent. In a moment I’ll show you how I put this network together, and then we’ll kind of talk about the different changes that we made. All right, I’ll see you then.