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Building a Chatbot with TensorFlow and Keras

Sophie Turol

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Digital assistants built with machine learning solutions are gaining their momentum. At TensorBeat 2017, one of the sessions covered how to deliver an answer bot with Keras and TensorFlow, what tools may help to address the issues, as well as tips on training a model and improving prediction results.

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TensorFlow in Finance: Discussing Predictive Analytics and Budget Planning

Sophie Turol

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Though practical usage of TensorFlow within finance is still in its germ phase, the scenarios are already quite a few. The fireside chat at TensorBeat 2017 discussed how the world of finance can drive value and improve customer experience from employing TensorFlow-based solutions.

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Deep Q-Networks and Practical Reinforcement Learning with TensorFlow

Sophie Turol

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This blog post highlights things-to-know while enabling reinforcement learning with TensorFlow, as discussed at one of the sessions at TensorBeat 2017. You will find out what toolkit simplifies the work done within an environment, how to handle pitfalls of distributed learning, boost performance across multiple environments, etc.

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ML Toolkit for TensorFlow: Out-of-the-Box Algorithms to Boost Training Data by 50x

Sophie Turol

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At TensorFlow Dev Summit 2017, Google’s Ashish Agarwal introduced a TensorFlow-based toolkit of machine learning algorithms. The toolkit provides out-of-the-box packed solutions to enable researchers and developers to create high-level custom model architectures.

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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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How TensorFlow Can Detect and Predict Wildfires

Sophie Turol

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At the recent Tensorflow meetup in Washington DC, the attendees learnt how TensorFlow can help in automating wildfire detection/prediction, as well as what’s underlying the TensorFlow four core concepts.

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Experimenting with Deep Neural Networks for X-ray Image Segmentation

Sergey Kovalev

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Deep neural networks present a great interest for the field of medical image segmentation. This article shares the results of the exploratory phase of the research aimed at examining the potential of deep learning methods and encoder-decoder convolutional neural networks for lung image segmentation. The study was conducted by our partners at the Biomedical Image Analysis Department of the United Institute of Informatics Problems, National Academy of Sciences of Belarus.

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Image and Text Recognition with TensorFlow Using Convolutional Neural Networks

Sophie Turol

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Convolutional neural networks (CNN) solve a variety of tasks related to image/speach recognition, text analysis, etc. These topics were discussed at a recent Dallas TensorFlow meetup—organized and sponsored by Altoros. The sessions demonstrated how CNN can foster deep learning with TensorFlow in the context of image recognition. The examples featured MNIST, a large data set of handwritten digits, and Word2vec, a group of models used to generate word embeddings.

Watch the videos below for more detail.

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Using k-means Clustering in TensorFlow

Sergey Kovalev

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The goal of this TensorFlow tutorial is to use the k-means algorithm for grouping data into clusters with similar characteristics. When working with k-means, the data in a training set does not need labels. As an unsupervised learning method, the algorithm builds clusters based on the data itself.

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Videos from TensorFlow Meetup in Boston, Mar 7, 2016

Sophie Turol
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