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Thread: TensorFlow, open source software machine learning library, Google Inc., Mountain View, California, USA

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    TensorFlow: Open source machine learning

    Published on Nov 9, 2015

    TensorFlow is an open source software library for numerical computation using data flow graphs. Originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research.
    "TensorFlow - Google’s latest machine learning system, open sourced for everyone"

    by Jeff Dean and Rajat Monga
    November 9, 2015

    "TensorFlow: smarter machine learning, for everyone"

    by Sundar Pichai
    November 9, 2015

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    Article "Google Open Sources Machine Learning Library TensorFlow"
    With TensorFlow now open sourced by Google, companies and the research community can implement machine learning systems more easily and more efficiently.

    by Thomas Claburn
    November 11, 2015

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    Deep Learning with TensorFlow - Introduction to TensorFlow

    Published on Dec 16, 2016

    Deep Learning with TensorFlow Introduction

    The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this TensorFlow course you'll use Google's library to apply deep learning to different data types in order to solve real world problems.

    Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layer, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which is the vast majority of data in the world.

    TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

    In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

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    Introduction to TensorFlow Lattice

    Published on Oct 11, 2017

    Maya Gupta, Seungil You and Jan Pfeifer introduce TensorFlow Lattice. TensorFlow Lattice is an open source project that provides a set of prebuilt TensorFlow Estimators that are easy to use, and TensorFlow operators to build your own lattice models. Lattices are multi-dimensional interpolated look-up tables that can approximate any input-output relationships in your data. Furthermore, TensorFlow Lattice provides a way to impose a monotonic input-output relationship (an input goes up, then the output goes up) and smooth regularizers including Laplacian, Torsion to help your model generalize better for unseen feature space.

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