TensorFlow


TensorFlow* Optimizations on Modern Intel Architecture

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TensorFlow* is a leading deep learning and machine learning framework, which makes it important for Intel and Google to ensure that it is able to extract maximum performance from Intel's hardware offering. This paper introduces the Artificial Intelligence (AI) community to TensorFlow optimizations on Intel Xeon and Intel Xeon Phi processor-based platforms. These optimizations are the fruit of a close collaboration between Intel and Google engineers announced last year by Intel's Diane Bryant and Google's Diane Green at the first Intel AI Day. We describe the various performance challenges that we encountered during this optimization exercise and the solutions adopted. We also report out performance improvements on a sample of common neural networks models.


Getting Started with Deep Learning

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Editor's note: Please note that, while this chart and post was up to date when it was first published, the landscape has changed in such a way that the table below is not depict a fully accurate picture at this point (e.g. Keras now supports a greater number of frameworks). The post is still beneficial, however, with this caveat noted. At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the model performance.


tensorflow/lattice

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TensorFlow Lattice is a library that implements lattice based models which are fast-to-evaluate and interpretable (optionally monotonic) models, also known as interpolated look-up tables. It includes a collection of TensorFlow Lattice Estimators, which you can use like any TensorFlow Estimator, and it also includes lattices and piecewise linear calibration as layers that can be composed into custom models. Note that TensorFlow Lattice is not an official Google product. A lattice is an interpolated look-up table that can approximate arbitrary input-output relationships in your data. It overlaps a regular grid on your input space, and it learns values for the output in the vertices of the grid.


Best Python libraries for Machine Learning and Data Science : (Part -1)

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Python is becoming popular day by day and has started to replace many popular languages in the industry. The main reason for Python's popularity is because of the following reasons: The simplicity of python has attracted many developers to build libraries for Machine learning and Data Science, because of all these libraries, Python is almost popular as R for Data Science. If you are working or interested about Machine Learning, then you might have heard about this famous Open Source library known as Tensorflow. It was developed at Google by Brain Team. Almost all Google's Applications use Tensorflow for Machine Learning.


Bringing the Power of Deep Learning to More Data Scientists - THINK Blog

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New AI technologies like machine learning and deep learning are fitting ever more snugly into the shifting enterprise landscape. Deep learning in particular is being adopted by an increasing number of enterprises for expanded insights and with the aim to better serving their clients. Thanks to more powerful systems and graphics processing units (GPUs), we are able to train complex AI models that enable these insights. IBM has long been one of the leaders in analytics and over the last year or two introduced two key new products, Data Science Experience and IBM PowerAI, designed to enable enterprises to more easily start using advanced AI technologies. Today we're unveiling that we are bringing these two key software tools for data scientists together.


Book Review Python Machine Learning - Second Edition

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The book python machine learning, second edition by Sebastian Raschka and Vahid Mirjalili is a tutorial to a broad range of machine learning applications with Python. It provides a practical introduction to machine learning using popular libraries like SciPy, NumPy, scikit-learn, Matplotlib, and pandas. The main revision to the first edition is more chapters on neural network practices. There are now five chapters that discuss neural networks, and their implementation in TensorFlow. Besides the additional content, a lot of concepts of the first edition are refined.


TensorFlow or Keras? Which one should I learn? – Imploding Gradients – Medium

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With plenty of libraries out there for deep learning, one thing that confuses a beginner in this field the most is which library to choose. In this blog post, I am only going to focus on Tensorflow and Keras. This will give you a better insight about what to choose and when to choose either. Tensorflow is the most famous library used in production for deep learning models. It has a very large and awesome community.


Machine Learning Interface for Medical Image Analysis

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TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Our goal is to extend the TensorFlow API to accept raw DICOM images as input; 1513 DaTscan DICOM images were obtained from the Parkinson's Progression Markers Initiative (PPMI) database. DICOM pixel intensities were extracted and shaped into tensors, or n-dimensional arrays, to populate the training, validation, and test input datasets for machine learning. We implemented a neural network classifier that produces diagnostic accuracies on par with excellent results from previous machine learning models.


Applied Machine Learning: The Less Confusing Guide

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ML essentially aims to understand patterns in large sets of input data and then predict outputs based on the models it generate. The goal of machine learning is to properly train ML algorithms to create such models. Follow this tutorial to learn four techniques used to prepare a linear regression model: Simple Linear Regression, Ordinary Least Squares, Gradient Descent and Regularization. One of the most exciting features of deep learning is its performance in feature learning; the algorithms perform particularly well in being able to detect features from raw data.


Deep Learning with TensorFlow - Welcome

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Enroll in the course for free at: https://bigdatauniversity.com/courses... 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. 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.