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Luiz GUStavo Profile picture
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3 Apr, 12 tweets, 4 min read
Today is my birthday!

As my gift to you, I created this thread with all my NLP posts of this week to give you some technical content for your weekend!

[2 minutes]

1/11🧵
Let's start by What is NLP?



2/11🧵
"What is a Text Embedding?"



3/11🧵
"What is this BERT that people keep talking about?"



4/11🧵
"Ok, BERT looks great, how easy it is to use it?"

Very easy! I wrote more about it here:

5/11🧵
"I want something more practical, where can I start?"

You can start with Sentiment Analysis!



6/11🧵
"I want to do Sentiment Analysis but I don't have ML or coding experience, can you help me?"

YES!
@GoogleCloudTech got you covered:


7/11🧵
Do you want a harder problem, how about Named entity recognition?



8/11🧵
I want to use NER on my product, where can I start fast?

@GoogleCloudTech have some solutions for you:

9/11🧵
"I also heard about text generation and GPT-3, what is this all about?"

Glad you asked:

10/11🧵
Do you have any questions?
Leave them in the comments!!

Do you want to give me a birthday gift?
RT this content with your friends and help me reach more people!

I want to make ML easier for everyone!

11/11🧵
Ok, this thread blew up! I'm very very happy!

Thanks to everyone that helped me share some knowledge with this great community!

All your RTs and likes made my amazing day even better!

I'm really grateful to you all! 😊😊
Thank you!

• • •

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Luiz GUStavo Profile picture
2 Apr
One very interesting task on the NLP fields is text generation.

There are very advanced techniques and a lot of research on it and even business based solely on it!

But how does it work?

[7.47min]
[I guarantee it's a much better read then doom scrolling!!!]

1/11🧵
Let's think: what a model would have to do to generate text?

The rationale is, as humans we form sentences by trying to create a sequence of words that makes sense.

The less random this sequence looks like, the better the output text is and closer to human like.

2/11🧵
Here is where ML can help.

A model should learn how to combine the words the best way possible.
The simplest way to teach this is: given a sentence, hide the last word and let the model try to guess it.

The loss function measures how good the model's guess is.

3/11🧵
Read 11 tweets
Luiz GUStavo Profile picture
2 Apr
One cool use of NLP is for NER (Named-entity recognition)

This enables you to find person names, organizations, locations, quantities, monetary values, percentages, etc. in a piece of text.

If you only want to use it on your data this API cloud.google.com/natural-langua… can help

1/4🧵
Sometimes you need to create your own model for your specific data corpus (eg: legal, science, medical texts)

To create your own model, AutoML Natural Language can help you:

2/4🧵
If you want to build everything from scratch, then you'll need:
• a language embedding (like BERT, ELMO, USE) and #TFHub have all you need
• a dataset and this github.com/juand-r/entity… can help you find one

3/4🧵
Read 4 tweets
Luiz GUStavo Profile picture
31 Mar
Encoding text in numbers is a very important part of NLP as the better this can be done, the better are the possible results!

Word embedding works but they don't have the full context of the sentence.

This is where BERT comes in

But what is BERT?

1/9🧵
When we do word embedding, both sentences
• They are running a test
• They are running a company

Will have very similar embeddings but the meaning of both sentences are very different. Without this context, the model using this encoding will be blind to the context

2/9🧵
This is where Bidirectional Encoder Representations from Transformers (BERT) comes in play!

It is a Transformer-based network created in 2018 and
takes into account the context of the word's occurrence. For the previous example, it gives very different embeddings.

3/9🧵 Image
Read 9 tweets
Luiz GUStavo Profile picture
27 Mar
If you are looking for something to learn during the weekend,

How about on-device Machine Learning?

You'll only need some understanding of ML and some of Mobile development.

Let me give you all the pointers in FAQ style:

[reading: 5.84 min]

1/12🧵
"Which tools will I need to start?"



2/12🧵
"Ok, how can a ML model run on a phone?"



3/12🧵
Read 13 tweets
Luiz GUStavo Profile picture
25 Mar
To make Apps with Magical User Experiences, you need to get all the performance possible from the hardware.

From the on-device ML perspective, you can achieve that using the TFLite Delegates.

They enable you to access the power of HW acceleration.

1/6🧵
Your phone's CPU is usually very fast but as a multi-purpose processor it's not optimized for the heavy math that ML needs

Like on their big brothers (servers 🤓) phones have also more specialized chips more suitable for ML, the most popular being the GPUs

2/6🧵
Another popular accelerator is the Qualcomm Hexagon DSP that has shown 75% reduction in power consumption.

On the Apple side, you can use the Core ML delegate to access the Neural Engine processor on newer i[Phones|Pads] and that can give huge boosts in performance!

3/6🧵
Read 6 tweets
Luiz GUStavo Profile picture
24 Mar
When we want to deploy a ML models on-device you may need to optimize them.

A model with bigger accuracy might also be bigger in size and also use more memory and slower

Do you need real time inference?

Let's take a look on how to optimize your model

[3.14 minutes]

1/6🧵
TFLite have the model optimization toolkit: tensorflow.org/model_optimiza…
To help you with this very important task.

Among the techniques are: Quantization and Pruning

2/6🧵
Quantization works by reducing the precision of the numbers used to represent a model's parameters, which by default are float32.

This results in a smaller model and faster computation.

More info here: blog.tensorflow.org/2020/04/quanti…

3/6🧵
Read 6 tweets

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