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Nawaid Shamim
Google Developer Expert (GDE) for Google Cloud Platform
Google Developer Expert (GDE) for Google Cloud Platform
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Introducing Cloud AutoML, our new service to help businesses with limited #ML knowledge easily build their own high-quality custom models → https://www.blog.google/topics/google-cloud/cloud-automl-making-ai-accessible-every-business/
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The Google Brain team (g.co/brain) had a very productive 2017 - check out part one of a two-part series by +Jeff Dean that summarizes the team's core research in #MachineLearning, #TensorFlow releases, TPUs, research datasets and much more!
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TensorFlow 1.3 introduces datasets and estimators, an easy way to create models & to define input data streams → https://developers.googleblog.com/2017/09/introducing-tensorflow-datasets.html
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Presenting MultiModel, an exploratory first step towards the convergence of vision, audio and language understanding into a single neural network .
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The TensorFlow Object Detection API is an open-source framework built on top of #TensorFlow that makes it easy to construct, train and deploy object detection models, designed to support state-of-the-art models while allowing for rapid exploration and research.
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Something sketchy going on with machine learning

David Ha, one of this year's Brain Residents (g.co/brainresidency), wrote up a very nice Google Research blog post about the work that he and +Douglas Eck have been doing on generative models for sketch-style drawing.

David and Doug have a new Arxiv paper that was just posted about the details of the work at titled "A Neural Representation of Sketch Drawings" for the details, but the blog post gives a really nice overview of what these sorts of generative models can do. The blog post goes through many variations of pretty cool things you can do with this model:

o "Draw me a cat", and it can generate different many different cats
o "Draw me a cat that looks roughly like toothbrush", and it can draw a cat that looks like a toothbrush
o Smoothly transition between cat pictures that are just faces versus ones that show the whole body by manipulating a latent variable that is learned by the model
o Do "vector arithmetic in drawing space": "cat head + (pig with body - pig just head)" gives "cat with body"
o Complete drawings given a category and just a line or two: "Finish this fire truck"

Just a taste of the kinds of creative tools that machine learning will be able to provide in the coming years.

Blog post:
https://research.googleblog.com/2017/04/teaching-machines-to-draw.html
Arxiv paper with details: https://arxiv.org/abs/1704.03477
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New products and services to help you focus more on solving business problems with data, instead of spending time and resources on building, integrating and managing infrastructure. goo.gl/Ilf48w
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I'm very excited about the work our group (g.co/brain) is doing in various areas of medical imaging. Today, we published a preprint titled _"Detecting Cancer Metastases on Gigapixel Pathology Images" that showed that our ML model achieves a tumor localization score of 0.89 vs. 0.73 achieved by human pathologists with infinite time. The paper authors are Yun Liu, Krishna Gadepalli, Mohammad Norouzi, George E. Dahl, Timo Kohlberger, Aleksey Boyko, Subhashini Venugopalan, Aleksei Timofeev, Philip Q. Nelson, Greg Corrado, Jason D. Hipp, Lily Peng, and Martin C. Stumpe.

Some key statistics from the paper used to evaluate the effectiveness of this work:

Tumor localization score (FROC): ("find all the tumors")
0.89 (our model)
0.73 (human pathologists with infinite time)

Sensitivity at 8 false positives:
0.92 (our model)
0.73 (human pathologists with infinite time)

(It's also worth point out that Yun Liu is a member of the Google Brain Residency program: g.co/brainresidency)

This work follows on our earlier work on detection of diabetic retinopathy in retinal images.

Pathology blog post: https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html
Paper preprint: https://drive.google.com/file/d/0BwScg2QTKNZ0VVdwTURXeDluQVk/view

Earlier blog post on diabetic retinopathy work: https://research.googleblog.com/2016/11/deep-learning-for-detection-of-diabetic.html

(Edit: Slight rewording)
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A pathologist’s report after reviewing a patient’s biological tissue samples is often the gold standard in the diagnosis of many diseases. The reviewing of pathology slides is a very complex task, requiring years of training to gain the expertise and experience to do well. Even with this extensive training, there can be substantial variability in the diagnoses given by different pathologists for the same patient, which can lead to misdiagnoses.

To address these issues of limited time and diagnostic variability, we are investigating how deep learning can be applied to digital pathology, by creating an automated detection algorithm that can naturally complement pathologists’ workflow. Learn more, below.
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Daniel Bogan runs a site called usesthis.com that does interviews with different people from all kinds of professions about their "work setup" (everything from computer scientists to chefs to beekeepers). He asked me if I'd participate, and here's the result:

https://usesthis.com/interviews/jeff.dean/

Browing the other interviews is kind of fun just to hear about what tools are useful for other professions:

https://usesthis.com/interviews/
https://usesthis.com/categories/

Thanks for running this site, Daniel!
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