Showing posts with label Data Engineering. Show all posts
Showing posts with label Data Engineering. Show all posts
Friday, September 1, 2017
Recent AI, Big Data, Deep Learning, Machine Learning Info Digest 2017/09/01
The Rise of the Data Engineer
Over the past 5 years working in Silicon Valley at Airbnb, Facebook and Yahoo!, and having interacted profusely with data teams of all kinds working for companies like Google, Netflix, Amazon, Uber, Lyft and dozens of companies of all sizes, Maxime Beauchemin is observing a growing consensus on what “data engineering” is evolving into, and felt a need to share some of my findings.
The Downfall of the Data Engineer
In this post, Maxime Beauchemin want to expose the challenges and risks that cripple data engineers and enumerates the forces that work against this discipline as it goes through its adolescence.
Machine Learning for Humans
Simple, plain-English explanations accompanied by math, code, and real-world examples by Vishal Maini.
How Machines Learn: A Practical Guide
Karlijn Willems lists seven steps (and 50+ resources) that can help you get started in this exciting field of Computer Science, and ramp up toward becoming a machine learning hero.
How AI can aid, not replace, humans in recruitment
One industry where the use of the technology is being actively explored is recruitment, where enterprises are drawing on its capabilities in various ways to help them find new staff.
Report shows that AI is more important to IoT than big data insights
We think that big data is the only thing we need for all of our insights. But in the world of Internet of Things (IoT), that is not the case.
Four deep learning trends from ACL 2017 (part 1)
Four deep learning trends from ACL 2017 (part 2)
In this two-part post, Abigail See describes four broad research trends that she observed at the conference (and its co-located events) through papers, presentations and discussions. The content is guided entirely by her own research interests; accordingly it’s mostly focused on deep learning, sequence-to-sequence models, and adjacent topics.
Deep Learning And Reinforcement Learning Summer School 2017
Slides: https://mila.umontreal.ca/en/cours/deep-learning-summer-school-2017/slides/
Video: http://videolectures.net/deeplearning2017_montreal/
Saturday, December 10, 2016
Recent Big Data and Machine Learning Info Digest 2016/12/10
Deep Learning Summer School, Montreal 2016
iSee: Using deep learning to remove eyeglasses from faces
Apache Spark and Amazon S3 — Gotchas and best practices
A Day in the Life of a Data Engineer
Pandas Tutorial: Data analysis with Python: Part 2
This AI Boom Will Also Bust
Big Data Extraction Tools For Good Decision-making
Hortonworks: MapR Shows a Better Way, Says Cowen
iSee: Using deep learning to remove eyeglasses from faces
Apache Spark and Amazon S3 — Gotchas and best practices
A Day in the Life of a Data Engineer
Pandas Tutorial: Data analysis with Python: Part 2
This AI Boom Will Also Bust
Big Data Extraction Tools For Good Decision-making
Hortonworks: MapR Shows a Better Way, Says Cowen
Labels:
AI,
Amazon,
Barrons,
Big Data,
Data Engineering,
Deep Learning,
eyeglasses,
Hortonworks,
MapR,
Montreal,
Pandas,
Python,
Spark
Sunday, September 11, 2016
Recent Big Data and Machine Learning Info Digest 2016/09/11
A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1
Deep Learning-Take machine learning to the next level (Udacity)
Big Data In Banking: How Citibank Delivers Real Business Benefits With Its Data-First Approach
A Survival Guide to a PhD
New Research — We’re In the Middle of a Data Engineering Talent Shortage
Deep Learning-Take machine learning to the next level (Udacity)
Big Data In Banking: How Citibank Delivers Real Business Benefits With Its Data-First Approach
A Survival Guide to a PhD
New Research — We’re In the Middle of a Data Engineering Talent Shortage
Labels:
Andrej Karpathy,
CNN,
Data Engineering,
Deep Learning,
PhD,
Udacity
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