InfoQ Homepage TensorFlow Content on InfoQ
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We Also Can Do It! Machine Learning in Javascript!
Eliran Eliassy shows how to create a prediction model with a web application using TensorFlow.js and other deep learning tools that can run in the browser.
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Machine Learning on Mobile and Edge Devices with TensorFlow Lite
Daniel Situnayake talks about how developers can use TensorFlow Lite to build machine learning applications that run entirely on-device.
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Swift for Tensorflow
Paige Bailey demonstrates how Swift for TensorFlow can make advanced machine learning research easier and faster.
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ML in the Browser: Interactive Experiences with Tensorflow.js
Victor Dibia provides a friendly introduction to machine learning, covers concrete steps on how front-end developers can create their own ML models and deploy them as part of web applications.
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Peloton - Uber's Webscale Unified Scheduler on Mesos & Kubernetes
Mayank Bansal and Apoorva Jindal present Peloton, a Unified Resource Scheduler for collocating heterogeneous workloads in shared Mesos clusters.
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Deep Learning for Recommender Systems
Oliver Gindele discusses how some DL models can be implemented in TensorFlow, starting from a collaborative filtering approach and extending that to more complex deep recommender systems.
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Comparing Machine Learning Strategies Using Scikit-Learn and TensorFlow
Oliver Zeigermann looks at different ML strategies -KNN, Decision Trees, Support Vector Machines, and Neural Networks- and visualizes how they make predictions by plotting their decision boundaries.
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Machines Can Learn - a Practical Take on Machine Intelligence Using Spring Cloud Data Flow and TensorFlow
Christian Tzolov showcases how building a complex use-case, such as real-time image recognition or object detection, can be simplified with the help of the Spring Ecosystem and TensorFlow.
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Machine Intelligence at Google Scale
Guillaume LaForge presents pre-trained ML services such as Cloud Vision API and Speech API that works without any training, introducing Cloud AutoML.
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TensorFlow: Pushing the ML Boundaries
Magnus Hyttsten talks about how Google uses Machine Learning to address problems that were not solvable a year ago, looking at models and how they can be built.
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$200 Self-Driving Cars with RasPi and Tensorflow
William Roscoe and Adam Conway build and drive the $200 open source self driving Donkey Car and talk about about the hardware components & software that let it drive, capture data, create autopilots.
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In Depth TensorFlow
Illia Polosukhin keynotes on TensorFlow, introducing it and presenting the components and concepts it is built upon.
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