Using Long Short-Term Memory Networks and TensorFlow for Image Captioning

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Using Long Short-Term Memory Networks and TensorFlow for Image Captioning

Sophie Turol

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TensorFlow and neural networks are actively used to perform image recognition and classification. At the recent TensorFlow meetup, attendees learnt how these technologies can be employed to enable a machine to recognize what is depicted in the image and to deliver a caption for it. In addition, an insightful overview of using TensorBoard was provided.

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The Magic Behind Google Translate: Sequence-to-Sequence Models and TensorFlow

Sophie Turol

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What is the magic that makes Google products so powerful? At TensorFlow Dev Summit 2017, the attendees learnt about the sequence-to-sequence models that back up language-processing apps like Google Translate. This recap explains what it takes to read and batch sequence data, as well as which of the TensorFlow-based tools enable fully dynamic calculations.

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Building a Keras-Based Image Classifier Using TensorFlow for a Back End

Sophie Turol

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Written in Python, Keras is a high-level neural networks API that can be run on top of TensorFlow. This API was designed to provide machine learning enthusiasts with a tool that enables easy and fast prototyping, supports both convolutional and recurrent neural networks (and a combination of the two), while running on a CPU or GPU.

At the recent webinar, the attendees learned how to build an image classifier from scratch using Keras on top of TensorFlow, how containerization can help, how to fight data overfitting and reach 90% of accuracy, 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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Ins and Outs of Integrating TensorFlow with Existing Infrastructure

Sophie Turol

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At TensorFlow Dev Summit 2017, Jonathan Hseu of the Google Brain team elaborated on how to integrate TensorFlow with your infrastructure. The three major steps to take can be broadly divided into three high-level perspectives: data preparation, training, and serving in production.

With all of the three steps outlined, this blog post highlights nuances to consider: the perks of distributed training, how containers help out, why input / output file formats matter, etc.

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

Sophie Turol

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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

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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

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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

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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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