TensorFlow is Google’s new, open-source, machine learning platform. But with over 20 million developers around the world, adoption was critical. Google asked us to create demos showcasing the power of TensorFlow through use cases that developers can explore and implement themselves.

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Speech recognition

Explore an app that uses a microphone to spot keywords in natural language.
Can be used for voice-based interfaces, as well as keyword recognition and advanced learning in classrooms.

See the code >


Object detection

Recognize different objects in an image, using a pre-trained model.
Can be used for image and object-based search, or manufacturing-line quality control.

See the code >


Gesture recognition

Develop and train a neural network to recognize hand gestures. 
Can be used for touch-less interfaces — E.G. surgeons, machine operators, even gesture-based authentication.

See the code >


Image Recognition

Test an image with a pre-trained model that can recognize 1000 different types of items.
Can be used for image and object-based search, or manufacturing-line quality control. 

See the code >

Gesture Recognition

Image Classification

Why us?

At YML, our Innovation Blog explores various areas of machine learning — like Age and Gender Classification, Text Recognition and Face Detection, Hand Gesture Recognition, and more. Google's Brain Team saw our leadership in ML, Python, iOS Android, and mobile web, and decided we are the perfect partner to help realize their TensorFlow developer experience.

Even though we have the capacity to do the same work, we could not have done it at the same speed and efficiency as the YML team.

Magnus Hyttsten
Developer Advocate, Google


Our work has helped over a million developers to understand the power of the platform and do great things. There have been 100,000 paid sign-ups on Coursera and Udacity for developers learning how to develop on TensorFlow. 

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