Machine Learning Homework

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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.

It’s like if you had a video of a guy falling down the stairs and you wanted to train a neural network to classify what happens in the video.

Machine Learning Homework

A regular neural network might be able to tell that the guy was standing up in the first frame of the video.One way we can accomplish this is by utilizing a one to many RNN.We can train a neural network on a bunch of different essays and perform sampling on the model to generate a new essay we’ve never seen before!The neural network does a forward pass and then on the second forward pass, it takes in some of the information from the first iteration to get more context to make the second prediction. We use this new type of network to operate over sequences of data in a much better way than what we could do with traditional networks.But one problem with RNNs is the vanishing/exploding gradient problem.This course introduces the fundamental concepts and methods of machine learning, including the description and analysis of several modern algorithms, their theoretical basis, and the illustration of their applications.Many of the algorithms described have been successfully used in text and speech processing, bioinformatics, and other areas in real-world products and services.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?It contains a bunch of mathematical formulas that helps RNNs solve the vanishing gradient problem and also makes predictions more accurate.When working with an LSTM, think of there being four pieces of information: long term memory, short term memory, an event and an output.


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