Discover and read the best of Twitter Threads about #TFHub
Most recents (9)
I've been trying the new TensorFlow Decision Forest (TF-DF) library today and it's very good!
Not only the ease of use but also all the available metadata, documentation and integrations you get!
Let me show you some of the cool things I've learned so far...
[5 min]
1/11🧵
Not only the ease of use but also all the available metadata, documentation and integrations you get!
Let me show you some of the cool things I've learned so far...
[5 min]
1/11🧵
TensorFlow Decision Forests have implemented 3 algorithms:
• CART
• Random Forest
• Gradient Boosted Trees
You can get this list with tfdf.keras.get_all_models()
All of them enable Classification, Regression and Ranking Tasks
2/11🧵
• CART
• Random Forest
• Gradient Boosted Trees
You can get this list with tfdf.keras.get_all_models()
All of them enable Classification, Regression and Ranking Tasks
2/11🧵
CART or Classification and Regression Trees is a simple decision tree. 🌳
The process divides the dataset in two parts
The first is used to grow the tree while the second is used to prune the tree
This is a good basic algorithm to learn and understand Decision Trees
3/11🧵
The process divides the dataset in two parts
The first is used to grow the tree while the second is used to prune the tree
This is a good basic algorithm to learn and understand Decision Trees
3/11🧵
#GoogleIO2021 was last week and it's a lot of content to read/watch!
I'll post summaries of all the ML talks and help you be up to date
Let's start with my talk with @kempy about how #TFHub and how people are using it on the real world
[5 min]
1/7🧵
I'll post summaries of all the ML talks and help you be up to date
Let's start with my talk with @kempy about how #TFHub and how people are using it on the real world
[5 min]
1/7🧵
Having access to high quality models enables developers to solve their problems!
This makes ML more accessible to everyone and helps the society with it, like:
• Defend forests from illegal hunting
• Studying & protecting whales
• Blocking spam
• Helping farmers
2/7🧵
This makes ML more accessible to everyone and helps the society with it, like:
• Defend forests from illegal hunting
• Studying & protecting whales
• Blocking spam
• Helping farmers
2/7🧵
TensorFlow Hub has models for all the ML domains such as Image, Text, Audio and Video
For image, this tutorial can get you started with Transfer Learning. It does some cool tricks with the data and the final model is ready for on-device deployment
tensorflow.org/hub/tutorials/…
3/7🧵
For image, this tutorial can get you started with Transfer Learning. It does some cool tricks with the data and the final model is ready for on-device deployment
tensorflow.org/hub/tutorials/…
3/7🧵
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🧵
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🧵
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🧵
• 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🧵
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🧵
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🧵
• 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🧵
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🧵
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🧵
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🧵
2/12🧵
"Ok, how can a ML model run on a phone?"
3/12🧵
3/12🧵
When you have your TensorFlow Model and want to use it on a mobile device, you'll need to convert it to the TFLite format.
This process can be done in two ways:
- Using the Python API
- Using a command line tool
Let's look into some more details….
1/6🧵
This process can be done in two ways:
- Using the Python API
- Using a command line tool
Let's look into some more details….
1/6🧵
Why do we need to convert?
The TFLite is an optimized format (Flatbuffer) for faster loading
To keep the framework lite and fast, all the Operations are optimized for mobile execution but not all TF operations are available
The available ops: tensorflow.org/lite/guide/ops…
2/6🧵
The TFLite is an optimized format (Flatbuffer) for faster loading
To keep the framework lite and fast, all the Operations are optimized for mobile execution but not all TF operations are available
The available ops: tensorflow.org/lite/guide/ops…
2/6🧵
How to convert the model?
The Python API to convert a model is straightforward and it's the recommended method of conversion
You can, during the conversion, apply some optimizations, like post training quantization to reduce your model size and latency.
3/6🧵
The Python API to convert a model is straightforward and it's the recommended method of conversion
You can, during the conversion, apply some optimizations, like post training quantization to reduce your model size and latency.
3/6🧵
What is pitch?
One "team" activity that is very popular is Karaoke
You go there, sing your best, get your applause and the machine gives you a bad score and people laugh
Well, that's my experience with it so I imagine it's the same for everyone…
How can ML help here?
1/7🧵
One "team" activity that is very popular is Karaoke
You go there, sing your best, get your applause and the machine gives you a bad score and people laugh
Well, that's my experience with it so I imagine it's the same for everyone…
How can ML help here?
1/7🧵
When you sing, one important metric is Pitch
It's an attribute of musical tones, just as duration, intensity and timbre
Pitch is a quantified frequency that describes notes as high or low
I described frequency in much more detail here:
2/7🧵
It's an attribute of musical tones, just as duration, intensity and timbre
Pitch is a quantified frequency that describes notes as high or low
I described frequency in much more detail here:
2/7🧵
What if we could measure the pitch with a ML model?
The challenge here that makes this problem much harder is how to separate your voice from the background noise and instruments?
That's where the SPICE: Self-Supervised Pitch Estimation can help
ai.googleblog.com/2019/11/spice-…
3/7🧵
The challenge here that makes this problem much harder is how to separate your voice from the background noise and instruments?
That's where the SPICE: Self-Supervised Pitch Estimation can help
ai.googleblog.com/2019/11/spice-…
3/7🧵
✨💡 This is an ace idea from @sarah_edo! 💕
👩💻 Be on the lookout for a @TensorFlow Advent Calendar tomorrow, as well, highlighting meaningful, high-impact projects and papers from our community. If you'd like for yours to be considered, please shoot me an @-mention!
👩💻 Be on the lookout for a @TensorFlow Advent Calendar tomorrow, as well, highlighting meaningful, high-impact projects and papers from our community. If you'd like for yours to be considered, please shoot me an @-mention!
✨#TFadvent begins today! 😄
For our first project, I'd like to highlight this accessibility example from @shekitup that uses @TensorFlowJS to interpret sign language—and then translates those signs into input that can be used by a home assistant! 🗣️✨
medium.com/tensorflow/get…
For our first project, I'd like to highlight this accessibility example from @shekitup that uses @TensorFlowJS to interpret sign language—and then translates those signs into input that can be used by a home assistant! 🗣️✨
medium.com/tensorflow/get…
🎁 Day #2 of #TFadvent:
🤖 Check out this project from a recent hackathon at @ColbyCollege! The team trained a @TensorFlow model to learn muscle movements, then used that model to send signals to a prosthetic arm, controlling one finger at a time. ✨
👉: m.facebook.com/notes/daviscon…
🤖 Check out this project from a recent hackathon at @ColbyCollege! The team trained a @TensorFlow model to learn muscle movements, then used that model to send signals to a prosthetic arm, controlling one finger at a time. ✨
👉: m.facebook.com/notes/daviscon…
✨🧠 The ecosystem that has grown up around @TensorFlow in the last few years blows my mind. There's just so much functionality, compared to some of the other, newer frameworks.
👉Consider this an ever-expanding thread for me to take notes + wrap my brain around products. Ready?
👉Consider this an ever-expanding thread for me to take notes + wrap my brain around products. Ready?
1) @TensorFlow Extended (TFX)
It's no secret that I 💕 #TFX and all of its tooling for deploying machine learning models into production. If you care about keeping your models up-to-date and monitoring them, you should check out the product + its paper.
tensorflow.org/tfx/?hl=zh-cn
It's no secret that I 💕 #TFX and all of its tooling for deploying machine learning models into production. If you care about keeping your models up-to-date and monitoring them, you should check out the product + its paper.
tensorflow.org/tfx/?hl=zh-cn
2) @TensorFlow Hub
If you want to train your model on a small data set, or improve generalization, you'll need to use something called transfer learning. #TFHub modules make it easy—and are available in an #OSS marketplace: tfhub.dev.
site: tensorflow.org/hub/
If you want to train your model on a small data set, or improve generalization, you'll need to use something called transfer learning. #TFHub modules make it easy—and are available in an #OSS marketplace: tfhub.dev.
site: tensorflow.org/hub/
