Machine learning (ML) and deep learning (DL) content from the past 24 hours. Fundamentals — On the Algorithmia blog, Charlie Crawford writes an excellent introduction to deep learning. He clearly understands the relationships among ML/DL/AI, presents a good intro to neural networks and lists some of the top DL frameworks. My only criticism is that he omits Microsoft’s CNTK/Cognitive Toolkit
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Machine learning (ML) and deep learning (DL) content from Friday and the weekend. Here is a summary of last week’s top stories. Note to readers: Big Analytics will rebrand as ML/DL soon, with a new format. The new name reflects the increased focus on machine learning and deep learning. NVIDIA versus Intel Wall Street has a fever, and the prescription is more
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Machine learning (ML) and deep learning (DL) content from the past 24 hours, plus a few older items. Good Reads Gale Morrison’s survey of neural computing in Semiconductor Engineering is an absolute must read. She summarizes intellectual and technical developments in the field, market drivers and briefly discusses what works. Companies mentioned: Amazon, Baidu, Google, Huawei, Intel, Nervana, NVIDIA, and
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On a Google blog, Kaz Sato describes how a Japanese farmer uses TensorFlow to classify cucumbers. Very good. Perhaps now Google can set TensorFlow to work figuring out how to comply with EU regulations. Good Reads Adrian Colyer returns for the Fall semester with daily papers. Superlative Reads Dez Blanchfield heaps praise on obscure blogger’s new book. HPE Holds a Fire Sale
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In the second quarter of 2015, Hortonworks lost $1.38 for every dollar of revenue. In the second quarter of 2016, HDP lost $1.46 for every dollar of revenue. So I guess they aren’t making it up on volume. On the Databricks blog, Jules Damji summarizes Spark news from the past two weeks. AWS Launches Kinesis Analytics Amazon Web Services announces the availability
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So I guess Spark really is enterprise ready. Nick Heudecker, call your office. There are several key themes coming from the Summit: Spark Continues to Mature Spark and its contributors deserve a round of applause. Some key measures of growth since the 2014 Summit: Contributor headcount increased from 255 to 730 Committed lines of code increased from 175K to 400K
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A sellout crowd of 5,500 met at the Javits Center in New York last week for the 2014 Strata + Hadoop World conference. There were three major themes: Big Data in Action. In his keynote address, Mike Olson of Cloudera noted the shift from talking about “geeky projects like Pig, Sqoop and Oozie” to talking about applications, such as fraud detection,
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ML/AI 