TensorFlow


Tensorflow Tutorial: Part 1 – Introduction

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This series is excerpts from a Webinar tutorial series I have conducted as part of the United Network of Professionals. Many applications as of today have tensorflow embedded as part of their machine learning applications. Let's explore the tensorflow environment and how the flexible architecture makes implementation so easy. This means you can execute code locally in your laptop with a CPU of a GPU if you have one.


Keras shoot-out: TensorFlow vs MXNet – Julien Simon – Medium

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A few months, we took an early look at running Keras with Apache MXNet as its backend. Keras supports multiple backends for training and it's very easy to switch from one to the other. After a while, here's the result (full log here). Here's the result after 100 epochs (full log here): 43 minutes, 99.4% training accuracy, 62% test accuracy.


Wanna spend a day getting deeply hands-on with machine learning?

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Laurent is a Senior Research Associate at the University of Cambridge, where his work focuses on the development and application of machine learning methods to understand high throughput biological data. Alternatively, Barbara Fusinska will be doing an all-day session on Practical Deep Learning with TensorFlow. Using an interactive learning platform, attendees will have a practical opportunity to use TensorFlow when building deep networks, training them and evaluating the results. After two days of conference sessions, either workshop is an excellent opportunity to dive deep on the fundamentals of machine learning and deep networks.


Google open-source TensorFlow

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It is a machine-learning library using data flow graphs to build models. TensorFlow has been created for Deep Learning to let a user create a neural network architecture by himself (or herself, of course). Actually, tensors flow in the graph from node to node, thus making the name of the library sound logical. For some of you it may be interesting if there is any difference between TensorFlow and libraries like Theano, which also can make their own Deep Learning with multi-dimensional arrays and GPU.


Top Machine Learning, Deep Learning, NLP, and Data Mining Libraries

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It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. Machine Learning for Language Toolkit (MALLET) is a Java toolkit fro statistical natural language processing, document classification, clustering, topic modeling and information extraction.


Google wants to speed up image recognition in mobile apps

Engadget

Google wants to spread the deep learning to more developers, so it has unveiled a mobile AI vision model called MobileNets. The tech is part of TensorFlow, Google's deep learning model that recently shrunk down to mobile size in a new version called TensorFlow Lite. The larger the model, the better it is at recognizing landmarks, faces or doggos, with the most CPU-intensive ones hitting scores of between 70.7 and 89.5 percent accuracy. Those aren't far from Google's cloud-based AI, which can recognize and caption objects with around 94 percent accuracy, last we checked.



Google's latest platform play is artificial intelligence, and it's already winning

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The heart of this offering is Google's machine learning software TensorFlow. He says his team uses a range of machine learning frameworks to create in-house tools for tasks like categorizing customer feedback, but that TensorFlow is often a good place to start. "There are technical differences between [different AI frameworks], but machine learning communities live off community support and forums, and in that regard Google is winning," he tells The Verge. But Google didn't forget to feed the community either, and to complement these announcements unveiled new tools to help developers build AI services that work better on mobile devices.


Top Machine Learning, Deep Learning, NLP, and Data Mining Libraries

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The readers will love our list because it is Data-Driven & Objective. Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language.[2]


[N] TensorFlow Build On Windows Now Supports GPU • /r/MachineLearning

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It says "we are working on providing a GPU build as well." If GPU is enabled you need to install the CUDA 8.0 Toolkit and CUDNN 5.1. Thanks, they should probably update the readme then, it's a bit misleading when they say "CPU support only" under current known limitations:) Anyway, great news! It is the same but as I've got the link from the forked readme there is the TF repo