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Research Blog: Accelerating Deep Learning Research with the Tensor2Tensor Library
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and compare the results.

Today, we are happy to release Tensor2Tensor (T2T), an open-source system for training deep learning models in TensorFlow. T2T facilitates the creation of state-of-the art models for a wide variety of ML applications, such as translation, parsing, image captioning and more, enabling the exploration of various ideas much faster than previously possible. This release also includes a library of datasets and models, including the best models from a few recent papers (Attention Is All You Need, Depthwise Separable Convolutions for Neural Machine Translation and One Model to Learn Them All) to help kick-start your own DL research.
TensorFlow  machine_learning 
2 days ago by amy
Research Blog: Accelerating Deep Learning Research with the Tensor2Tensor Library
Announcing the Tensor2Tensor open-source system for training a wide variety of deep learning models in
TensorFlow  from twitter_favs
3 days ago by hustwj
Bilderkennung: Google gibt Objekt-Erkennungs-API für Tensorflow frei - Golem.de
Anwender von Googles freiem Machine-Learning-Framework Tensorflow können künftig auf eine API zur Objekterkennung in Bildern zugreifen. Google nutzt die Technik intern für
tensorflow 
3 days ago by andreaskoch
[1706.05137] One Model To Learn Them All
Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an attention mechanism, and sparsely-gated layers. Each of these computational blocks is crucial for a subset of the tasks we train on. Interestingly, even if a block is not crucial for a task, we observe that adding it never hurts performance and in most cases improves it on all tasks. We also show that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all.
TensorFlow  machine_learning  google 
3 days ago by amy
Research Blog: Supercharge your Computer Vision models with the TensorFlow Object Detection API
RT : Our in-house object detection system is now open source! Learn more about the Object Detection API at
TensorFlow  from twitter
5 days ago by alpinegizmo

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