Concise Lecture Notes - Lesson 4 | Fastai v3 (2019)

These notes were typed out by me while watching the lecture, for a quick revision later on. To be able to fully understand them, they should be used alongside the jupyter notebooks that are available here:



Does the language model work in informal language models where they use near illegible shortforms?


Language Model Creation

Creating a classifier

Why the learning rate for RNN is 2.6^4?


What are the 10% of cases where using Neural Nets for Tabular data is not a good option?

How to combine NLP (tokenized data), matadata(tabular data) with fastai?

Collaberative Filtering

Will the NLP model work when there are emojis or when hindi is written in Roman script?

Digging Deeper

Why do this step?

What does the neural net contain?

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