InfoQ Homepage Deep Learning Content on InfoQ
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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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Q&A on IBM's Fabric for Deep Learning with Chief Architect of Watson
InfoQ caught up with Ruchir Puri, chief architect of Watson, regarding the Fabric for Deep Learning (FfDL).
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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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Dataiku's Latest Release Integrates Deep-Learning for Computer Vision
Collaborative data science platform Dataiku's latest release of its Data Science Studio includes pre-trained deep learning models for image processing. The DSS platform implements each step of a data-science project from data-sourcing and visualization to production deployment. Its machine-learning module supports standard libraries and it integrates with Hadoop and multiple Spark engines.
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Microsoft Embeds Artificial Intelligence Platform in Windows 10 Update
The next Windows 10 update opens the way for the integration of artificial intelligence functionalities within Windows applications. Developers will be able to integrate pre-trained deep-learning models converted to the ONNX framework in their Windows applications.
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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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Autonomous Vehicles Became Better at Predicting Lane-Changes
Researchers created an algorithm that allows self-driving cars to predict lane-changes of the surrounding cars. The system works by using a deep-learning technique called Long Short-Term Memories (LSTMs). Although the most likely scenario on the highway is that every car stays in its own lane, their algorithm was able to slightly improve on this baseline prediction.
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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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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.
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How Apple Does Realtime Recognition of Handwritten Chinese Characters
Apple details building on-device handwritten Chinese character recognition with convolutional-neural networks and image recognition.
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Panel on the Future of AI
An SF QCon panel on the future of AI explored some issues facing machine learning today. The areas explored: critical issues facing AI right now, how has technology changed the way people are hired, how non-leading edge companies make the best use of current technologies, what the role of humans in relation to AI is, and exciting new breakthroughs on the immediate horizon.
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Teachable Machine: Teach a Machine Using Your Camera in Your Browser
Teachable Machine is a browser application that you can train with your webcam to recognize objects or expressions. In the demo you use your webcam as input to recognize three different classes of objects or expressions. Based on your camera input, the site shows different gifs, plays prerecorded sounds, or plays speech. The demo can be found here: teachablemachine.withgoogle.com
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NERSC Scales Scientific Deep Learning to 15 Petaflops
Intel, Stanford and National Energy Research Scientific Computing Center (NERSC) recently announced the first super computing cluster achieving 15 Petaflops of computing calculations power. This was achieved by a cluster of 9,622 Intel Xeon Phi processors at 1.4Ghz for a combined 2,629,696 threads of computation. In this article we will explore the hybrid approach behind achieving strong scaling.
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Apple Reveals the Inner Workings of Siri's New Intonation
Apple has explained how they use deep learning to make Siri's intonation sound more natural. IPhone owners can interact with Siri by asking questions in natural language and Siri responds by voice. At WWDC 2017, Apple announced that in iOS 11 Siri would use a new text to speech engine. In August 2017, Apple's machine learning journal unveiled how they were able to make Siri sound more human.
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Q&A with Movidius, a Division of Intel Who Just Launched the Neural Compute Stick
Recently Movidius (a division of Intel's New Technology Group) released the neural compute stick: a usb-based development kit that runs embedded neural networks. With this stick users can run neural network and computer vision models on devices with low computational power. InfoQ reached out to Gary Brown, marketing director for Movidius, Intel New Technology Group, and asked him a few questions.