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Practical
TensorFlow
Illia Polosukhin, Google Research
@ilblackdragon
Why Machine Learning?
★ Allows to solve problems we don’t have exact solution
for.
○ E.g. recommendations, predictions, clustering.
★ Given y = F(X), where we observe y, we can estimate F.
★ Becomes better with more data
○ when hard coded solution usually becomes worse with more code :)
Google Products Using Machine Learning
Big Data Challenges
★ Variety of data
★ Learning many things at once
★ Small data where matters
Deep Learning
Input
Out
Input
Out
Deep Learning for Perception Tasks
Input
Out
Deep Learning for Perception Tasks
Input
Out
Deep Learning for Perception Tasks
Input
Out
Deep Learning for Perception Tasks
Input
Out
Deep Learning for Perception Tasks
Deep Learning combines many components
Predictions
GoogLeNet
Recurrent Neural Network
Deep Learning: Leverage pre-training
What is
TensorFlow?
TensorFlow Ecosystem
Researchers Developers
Data
Scientists
TensorFlow Core Execution Engine
CPU GPU Android iOS ...
C++ FrontendPython Frontend ...
TensorFlow: Google backed
★ Google supported (growing army of
engineers are working on improving it).
★ Used in 100s of products across Google
How can I use this?
TensorFlow Learn
Simple example:
How can I use this?
Predictive Example
Going to deep neural network is easy:
Understanding Images
Image Classification
Recommendation Example
Recommendation Example
Image Description Item User
N layers of
convolutions
RNN encoder
Item
embeddings
User
embeddings
Concatenate
N fully connected
layers
Item embeddings P(item)
Scaling Out
TensorFlow scales with number of Machines.
You can use Google Cloud ML or Docker containers in VMs.
https://arxiv.org/abs/1604.00981
TensorFlow Serving: Serving models in production
Open Source project.
Check it out:
http://github.com/tens
orflow/serving
Questions?
Illia Polosukhin
@ilblackdragon
#TensorFlow
http://github.com/tensorflow/tensorflow
http://tensorflow.org

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Big data app meetup 2016-06-15

Editor's Notes

  1. Why are we using machine learning? Allows us to find solution for problems that we don’t exactly sure how to solve. Or they are probabilistic in nature. You can always to implement something in code based on your assumption, but it’s usually better to leverage machine learning and actual data. Problems like prediction and classification - how much gas will this jet engine use in next flight, is tumor a cancer or not. Recommending better match to users needs. Grouping into similar cluster based on various properties. All this problems is really hard to code. We can define this given some observed inputs X and outputs Y, we are trying to find a function F. One of the great properties of machine learning systems, they become better with more data. It’s very much untrue about code.
  2. ML is already in use in many products across various companies, and not in trivial ways; Here are just few highlight products that Google use machine learning for. Inbox classifies email into different buckets and suggests various . Google Now analyzes your timing to suggest traffic reports. Search leverages machine learning in various way, from understanding speech to ranking what better results to show. Translate is only possible because of machine learning. Play and Youtube give personalized recommendation for what video, app or music next to check out. And many many more.
  3. In general ML been advancing well with Big Data rise. We have scaled up our models to handle more data and more features. But there are still challenged that people face building practical applications. It’s relatively straightforward to handle continuous / categorical features directly related to target. But using side information or various other format like text or images is a challenge. We manage to train a model to predict or classify one thing, but problems co-learning multiple tasks or generating new things are also hard. Even in this day of big data, we do have a problem of small data - when the task you need doesn’t actually have that much supervised (labelled) data we fall short with many of methods or deliver subpar results. It’s hard to transfer knowledge from other domains where we have enough data.
  4. Deep Learning it’s not that new way to represent machine learning models that combine various non-linear processing units into one optimization model. If you look at more conventional method like decision trees or SVM - they required a lot of time to code the method or invent the math to optimize it. And it’s quiet hard to customize them for new types of data or even easily scale across machines. This leads to feature engineering and a lot of work around to scale up learning. Part of the appeal is the promise that you don’t need to feature engineer, just provide raw inputs and outputs. Instead of feature engineering - spending days or weeks on building new features, now it’s about designing models. With proper structure of the model, it can learn sophisticated features from data.
  5. Deep Learning models are good at perception problems: Image understanding The network takes an image as input, and predicts a label (lion) as output.
  6. Voice recognition Here it can take a sound file as input, and return a string as output
  7. Search The network can take a string as input (“restaurants near me”) and predict how likely the user is to click on a search result
  8. Translation The network can take an english sentence as input, and return the translation in french!
  9. Automatic image captioning (wow!) The network can take an image as input, and produce a description of the image as output.
  10. Allows to combine various components into one model: For example the image captioning model is combination of image understanding component (GoogLeNet) and RNN for generating text. Other example, combine regular methods (like logistic regression on continuous / categorical features) with image and text understanding in one model.
  11. Pre-training and transfer learning is becoming mainstream. For example, Word2Vec and Paragraph vectors are trained on large corpus and can be leveraged to train model from 100s of records. There is now a variety of successful applications of transfer learning and semi-supervised learning. Prominent examples are word2vec, paragraph vectors and image understanding models. Learning from one corpus where we have a lot of data, we can then leverage this in the new domain where we have limited amount of supervised data. Some examples include using word2vec and paragraph vectors for document classification with few number of examples per class.
  12. Numerical library that computes data flow graphs, largely used to define machine learning algorithms. And It natively supports deep learning models.
  13. Most popular ML framework on github! Researchers publish papers, and model specification with real code using TensorFlow Data Scientists train models on their data using these provided models, and customize it to their needs Developers then deploy these models to production using the same APIs Pre-trained models available for direct deployment, e.g. Google’s best image model trained on a million images Same API used for training and deployment
  14. Trains on CPU / GPU, locally or in Cloud, serve almost everywhere.
  15. So how one can start using all this for day to day tasks? Enter TensorFlow Learn - framework to provide an easy way to build models and combine.
  16. This is very similar to other popular machine learning frameworks, specifically sklearn.
  17. A bit simplified, but here is a two layers convolutional network image classifier.
  18. “You Might Like” model: * Items to recommend, * Users, * Images and descriptions of items. * Acquisition data from many users of many items.
  19. Schematics of the model that combines item and user embeddings, description text and image features to provide final recommendations.
  20. Full code from previous two slides. Hey, still fits on one page!
  21. Cloud ML Demo by Jeff Dean at GCP Next 2016: https://youtu.be/HgWHeT_OwHc?t=2h9m37s