Anyways, one problem that neural networks have struggled with is sequential data and temporal data.
Traditional neural networks need to have a fixed input and output size.
When you update your model using backpropagation and calculate the gradients of the loss (how wrong your model is), the gradients get smaller and smaller the further back you get in the network.
This basically means the more layers that are in the network, the less efficient training becomes.
We’ll start by importing all of the necessary libraries and modules. Training all of this took around three hours on my laptop, you can also train the model on Google Colab with a GPU runtime.
Theoretically, the generated texts should start sounding coherent after about 30 epochs so let’s take a look at some of the outputs of the model.“patience are first developed — our sense, that the world of the process as in the same form to the reverence, and the master of the synthesion and the same tempo of the sensues itand and as the all tame and the except of the free of the contradition of existe not that the morality and want of the place to all man of the tempope” of all moralexclive the soul to the good has always to his stands has of the chrison to danger of the super”“could regard eventhe emotions of hatred, or do the conduct, and an instances of the has one wele be enterer of the state of the religion in the soul, the represens and according and can be the feelings of the above all religions and a struggle of the senses well entiblent stands and can all suffett of the conduct of the soutes of his subject, and dependers for the religion that is not as a feelings of whom suphriated the sense of the st”Well… But isn’t it cool that all of this was generated by a neural network?Then it might be able to look at the frames in between until it gets to the last frame of the video and it sees a guy that has fallen down.But once it gets there, it’s forgotten whether or not the guy was standing up in the first place.Let’s use Keras, a high-level API for developing deep learning models.This follows the example code in the Keras Github repo for text generation with LSTMs.The exploding gradient is basically the opposite, if a gradient is very large, it backpropagates like an avalanche, and since RNNs go through many sequences and iterations, the problem of vanishing/exploding gradients is present.One solution to this problem is the use of LSTMs, Long Short Term Memory cells.No doubt with a better architecture and more extensive data, you could get results that might be indistinguishable from what a human would write.Even though I might not be able to get a neural network to write out my English essays for me just yet, I think that with a bit more experimenting and testing, I can cheat my way out of at least of my assignments.The main topics covered are: Note: except from a few common topics only briefly addressed in G22.2565-001, the material covered by these two courses have no overlap.It is strongly recommended to those who can to also attend the There will be roughly 4 assignments and a project.