Using TensorFlow to Compose Music Like the One of Bach or The Beatles

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Using TensorFlow to Compose Music Like the One of Bach or The Beatles

Sophie Turol

tensorflow-summit-2017-gibbs-sampling-for-music-generation-v17

TensorFlow Dev Summit 2017 brought together deep / machine learning enthusiasts to share their experience and breakthroughs. At the session delivered by Douglas Eck of the Google Brain team, the attendees learned how TensorFlow-based Magenta project can facilitate music generation and why output evaluation is critical.

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Deep Learning for Cybersecurity: Identifying Anomalies and Malicious Traffic

Sophie Turol

deep-learning-for-cyber-security-icon-v13

A recent webinar discussed how deep learning solutions can be applied to deliver better cybersecurity. However, things don’t always come as we expect, so there are certain difficulties to solve. Steven Hutt, a consultant in deep learning and financial risk, digs into the challenges on the way and the approaches at hand to save the day.

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Natural Language Processing and TensorFlow Implementation Across Industries

Sophie Turol

tensorflow-for-improving-train-arrival-prediction-v1

Being in the limelight of machine learning, TensorFlow has been widely used to address the needs of finance, healthcare, manufacturing, custom services, and many other industries.

At the recent meetup in Madrid, the speakers recalled the outstanding use cases of TensorFlow adoption and overviewed how the library and the related tools can be applied to natural language processing.

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Optical Character Recognition Using One-Shot Learning, RNN, and TensorFlow

Sophie Turol

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Optical character recognition (OCR) drives the conversion of typed, handwritten, or printed symbols into machine-encoded text. However, the OCR process brings the need to eliminate possible errors, while extracting only valuable data from ever-growing amount of it.

At the recent TensorFlow meetup, the attendees learnt how employing the one-shot attention mechanism for token extraction in Keras using TensorFlow as a back end can help out. In addition, the meetup discussed how to enable multilingual neural machine translation with TensorFlow.

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Enabling Multilingual Neural Machine Translation with TensorFlow

Sophie Turol

brain-neural-machine-translation-model-architecture-v777

The TensorFlow ecosystem offers an array of software patterns that can add value to a AI-based project. The question is to identify the one that will work.

At the recent TensorFlow meetup, attendees learnt about some distinct patterns to use, as well as found out how to contribute one of their own. Furthermore, an engineer of the Google Brain team explored how to improve conventional machine translation with TensorFlow.

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TensorFlow for Manufacturers: Building a 3D Rendering Engine

Sophie Turol

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Machine learning marches its way into a more and more broader pool of industries. The capabilities of TensorFlow are applied to an array of tasks from predicting wildfires to generating content.

At the recent meetup in San Francisco, the attendees learnt what pitfalls may come up when developing a rendering image and how TensorFlow helps out. In addition, the speaker from Autodesk exemplified how the company employs TensorFlow to categorize 3D data, enable robots to assemble structures, etc.

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Performance Comparison of Ruby Frameworks, App Servers, Template Engines, and ORMs (2016)

Eugene Melnikov

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The Ruby ecosystem is constantly evolving. There have been many changes in the engineering world since our comparison of Ruby frameworks in 2014. During the two years, we received a few requests from fellow engineers asking for an updated benchmark. So, here is the 2016 edition with more things tested.

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TensorFlow and OpenPOWER Driving Faster Cancer Recognition and Diagnosis

Roger Strukhoff

tensorflow-and-openpower-driving-faster-cancer-recognition-and-diagnosis-v16

At IBM Edge 2016, a team of developers and data scientists presented a practical study that evaluated the efficiency of training a TensorFlow model in a distributed mode. A use case featured high-resolution images of lymph nodes used for possible cancer detection.

Relying on a distributed model of TensorFlow and high-performing nature of the OpenPOWER infrastructure, the demonstrated system can accelerate medical data analysis—depending on the number of GPUs and nodes in its cluster. The particular subjects of the research were how training time decreases when the cluster grows and whether the accuracy of the results is affected by the distributed nature of the computations. Read this post for brief results and technical details.

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How to Use Snap Packages when Collecting IoT Data with Predix Time Series

Ilya Drabenia

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Snaps, designed by Canonical, are intended for packaging applications and their dependencies, along with the instructions for running these applications. In this article, we focus on how to write time-series data from your Linux environment to the Predix Time Series service using a snap.

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Creating Your First Serverless App with AWS Lambda and the Serverless Framework

Ilya Drabenia

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Serverless architectures are one of the newest trends in computing that brings reduced infrastructure and development costs to the table. According to this approach, an application is split into multiple functions with each of them deployed separately.

Here, we demonstrate how to create a serverless application and deploy it to AWS Lambda, as well as explain some of the architecture basics.

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