InfoQ Homepage TensorFlow Content on InfoQ
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Google Open-Sources GPipe Library for Faster Training of Large Deep-Learning Models
Google AI is open-sourcing GPipe, a TensorFlow library for accelerating the training of large deep-learning models.
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Introducing TensorFlow Privacy, a New Machine Learning Library for Protecting Sensitive Data
In a recent blog post, TensorFlow announced TensorFlow Privacy, an open source library that allows researchers and developers to build machine learning models that have strong privacy. Using this library ensures user data are not remembered through the training process based upon strong mathematical guarantees.
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Deep Learning for Speech Synthesis of Audio from Brain Activity
Research teams use deep learning neural networks to synthesize speech from electrical signals recorded in human brains, to help people with speech challenges.
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Uber Open-Sources Ludwig Code-Free Deep-Learning Toolkit
Uber Engineering is open-sourcing Ludwig, a deep-learning toolkit that allows users to experiment with a variety of neural network structures without writing code.
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TensorSpace.js Delivers Neural Network 3D Visualization Framework
TensorSpace.js provides an open source browser-based neural network data visualization framework to complement the growing machine learning landscape by supporting pre-trained models created with TensorFlow.js, Keras, or TensorFlow.
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Google Open-Sources BERT: A Natural Language Processing Training Technique
In a recent blog post, Google announced they have open-sourced BERT, their state-of-the-art training technique for Natural Language Processing (NLP) . Google has decided to do this, in part, due to a lack of public data sets that are available to developers. In addition, optimizations have been made to Cloud TPUs to reduce the amount of time required for training NLP.
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Face-api.js: JavaScript Face Recognition Leveraging TensorFlow.js
Face-api.js is a JavaScript API for face detection and face recognition in the browser implemented on top of the tensorflow.js core API, which implements a series of convolutional neural networks (CNNs), optimized for the web and for mobile devices.
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Apple Has Released Core ML 2
At WWDC Apple released Core ML 2: a new version of their machine learning SDK for iOS devices. The new release of Core ML should create an inference time speedup of 30% for apps developed using Core ML 2. An important new feature of the Core ML SDK is Create ML. Developers can create and train custom machine learning models on their mac.
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Tensorflow with Javascript Brings Deep Learning to the Browser
Google launched Tensorflow.js, a Javascript implementation of its open-source Tensorflow deep-learning framework during the recent TensorFlow Dev Summit 2018. Tensorflow.js enables training models directly in the browser by leveraging the WebGL JavaScript API for faster computations.
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How Booking.com Uses Kubernetes for Machine Learning
Sahil Dua explained how Booking.com was able to scale machine learning (ML) models for recommending destinations and accommodation to their customers using Kubernetes, at the QCon London conference. In particular, he stressed how Kubernetes elasticity and resource starvation avoidance on containers helps them run computationally (and data) intensive, hard to parallelize, machine learning models.
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Facebook Releases Open Source "Detectron" Deep-Learning Library for Object Detection
Recent releases from Facebook and Google implement the most current deep-learning algorithms to take a crack at the challenging problem of machine object detection.
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Q&A on Machine Learning and Kubernetes with David Aronchick of Google from Kubecon 2017
InfoQ caught up with David Aronchick, product manager at Google and contributor to Kubeflow about the synergy between Kubernetes and Machine Learning at Kubecon 2017.
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Machine Learning and Artificial Intelligence - Two Conferences to Attend in 2018
The IEEE publishes an annual list of the Top 10 Technology Trends for each upcoming year. Making the list for 2018 are multiple topics surrounding artificial intelligence and machine learning. Deep learning comes in as the IEEE hottest trend for 2018.
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Modern Big Data Pipelines over Kubernetes
Container management technologies like Kubernetes make it possible to implement modern big data pipelines. Eliran Bivas, senior big data architect at Iguazio, spoke at the recent KubeCon + CloudNativeCon North America 2017 Conference about big data pipelines and how Kubernetes can help develop them.
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Building GPU Accelerated Workflows with TensorFlow and Kubernetes
Daniel Whitenack spoke at the recent KubeCon + CloudNativeCon North America 2017 Conference about GPU based deep learning workflows using TensorFlow and Kubernetes technologies. He discussed the open source data pipeline framework Pachyderm.