人間には『善の心』ともう１つの『悪の心』が存在しているんです！
“Every human being has a “good heart” and another “evil heart”!
Optimization of both is required to excel, it is a non-trivial quest.”

Topics Covered

1. Optimization problem, what is it?
2. Guide to Linear Optimization — Solver glop and Simplex algorithm
3. Guide to Linear Optimization — Guide to Linear Optimization -
4. Guide to Integer Optimization — MIP Solver
5. Guide to Integer Optimization — Solving a MIP Problem
6. Guide to Integer Optimization — Using Arrays to Define a Model
7. Constraint Optimization — CP-SAT Solver
8. Constraint Optimization — Using a CP-SAT Problem
9. Constraint Optimization — Solving a CP-SAT Problem
10. Constraint Optimization — Cryptarithmetic Puzzles
11. …

Follow the link to the entire series by clicking here: The complete guide to NLP with fastai

See also The Annotated Transformer from Harvard NLP.

Attention and the Transformer

Nvidia AI researcher Chip Huyen wrote a great post Top 8 trends from ICLR 2019 in which one of the trends is that RNN is losing its luster with researchers.

There’s a good reason for this, RNNs can be a pain: parallelization can be tricky and they can be difficult to debug. …

In the complete guide to NLP with fastai

Follow the link to the entire series by clicking here: The complete guide to NLP with fastai

This post will put everything we learned until this point together and then introduce attention in translation with an RNN

This is exciting because the performance over our this journey of learning NLP can be summarized as below:

In the complete guide to NLP with fastai

Follow the link to the entire series by clicking here: The complete guide to NLP with fastai

This will be a long post where we will build Rnn and explain the coded Rnn. Next using our Rnn we will introduce the new concept of Teacher Forcing and why one may use it.

So let’s get started….

Predicting the English word version of numbers using an RNN

We were using RNNs as part of our language model in the previous lesson. Today, we will dive into more details of what RNNs are and how they work. We will do this using the problem of trying to predict the English word version of numbers.

Let’s predict what should come next in this sequence:

eight thousand one , eight thousand two , eight thousand three , eight thousand four , eight thousand five , eight thousand six …

In the complete guide to NLP with fastai

Follow the link to the entire series by clicking here: The complete guide to NLP with fastai

Regex workflow

In [6]:
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import pandas as pd
import re

Jeremy Howard’s lecture is the basis for this review: a three-part review plan

* regex workflow
* svd
* transfer learning.

regex is used every day in NLP work, and that it is essential for machine learning practitioners to develop a working knowledge of regex. Since we've already done deep dives into svd and into transfer learning, we'll focus on the regex part of this review.

A simple regex exercise

To illustrate the power of regex and familiarize us with the…

In the complete guide to NLP with fastai

Follow the link to the entire series by clicking here: The complete guide to NLP with fastai

What is the BLEU metric?

The BLEU metric has been introduced in this article to come with some kind of way to evaluate the performance of translation models. It's based on the precision you hit with n-grams in your prediction compared to your target. Let's see this as an example. Imagine you have the target sentence

the cat is walking in the garden

and your model gives you the following output

the cat is running in the fields

We are going to compute the precision, which is the…

In the complete guide to NLP with fastai

Follow the link to the entire series by clicking here: The complete guide to NLP with fastai

Transfer Learning for Natural Language Modeling

Constructing a Language Model and a Sentiment Classifier for IMDB movie reviews

Transfer learning has been widely used with great success in computer vision for several years, but only in the last year or so has it been successfully applied to NLP (beginning with ULMFit, which we will use here, which was built upon by BERT and GPT-2).

As Sebastian Ruder wrote in The Gradient last summer, NLP’s ImageNet moment has arrived.

We will first build a language model for IMDB movie reviews…

In the complete guide to NLP with fastai

Follow the link to the entire series by clicking here: The complete guide to NLP with fastai

Transfer Learning

We are going to create an IMDb language model starting with the pre-trained weights from the wikitext-103 language model.

Now let’s grab the full IMDb dataset for what follows.

In [4]:
path = untar_data(URLs.IMDB)
path.ls()Out[4]:
[WindowsPath('C:/Users/cross-entropy/.fastai/data/imdb/data_clas.pkl'),
.... WindowsPath('C:/Users/cross-entropy/.fastai/data/imdb/vocab_lm.pkl')]In [12]:(path/'train').ls()Out[12]:
[WindowsPath('C:/Users/cross-entropy/.fastai/data/imdb/train/labeledBow.feat'),
...
 WindowsPath('C:/Users/cross-entropy/.fastai/data/imdb/train/unsupBow.feat')]

The reviews are in a training and test set following an imagenet structure. The only difference is that there is an unsup folder in train that contains the unlabelled data.

We’re not going to train a model that…