Recent AI, Deep Learning, Machine Learning Study Guide 2017/09/29

15 minute guide to choose effective courses for machine learning and data science
Advice from Tirthajyoti Sarkar for young professionals in non-CS field who wants to learn and contribute to data science/machine learning. Curated from personal experience.

The Complete Guide on Learning Deep Learning
This guide by Susan Li covers almost all the courses for Deep Learning.

NVIDIA Deep Learning Institute (DLI)
The NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve the world’s most challenging problems with deep learning.

Meet with Harry Shum and Xuedong Huang in Seattle

During my recent trip to Seattle, I was glad to meet Harry Shum, Executive VP, Microsoft Artificial Intelligence and Research Group Group and listened to his excellent keynote speech in the 2nd North America Tsinghua Alumni Convention.

During the same convention, I also met Xuedong Huang, a Microsoft Technical Fellow in AI and Research.

I was so impressed by Dr.Huang's short presentation (most in Chinese) about Microsoft Translator in the breakout session about AI.

Recent AI, Big Data, Deep Learning, Machine Learning Info Digest 2017/09/08

Scientists Use Artificial Intelligence To Spot Alzheimer's Before Onset of Symptoms
Scientists from the Douglas Mental Health University Institute’s Translational Neuroimaging Laboratory at McGill have developed an algorithm that reliably detects signs of dementia before its onset. The technology could be used to help families prepare for treatment options, and to help researchers select better candidates for clinical trials that test drug effectiveness.

Facebook creates AI that negotiates in unknown new language
Facebook AI Research (FAIR) has been working on artificial intelligence (AI) agents that negotiate for the best deal, using all the complexities of language, reasoning, and deception that humans use.

The Seven Deadly Sins of Predicting the Future of AI
Predicting the future is really hard, especially ahead of time.

What machines can tell from your face
Life in the age of facial recognition. Technology is rapidly catching up with the human ability to read faces. In America facial recognition is used by churches to track worshippers’ attendance; in Britain, by retailers to spot past shoplifters. This year Welsh police used it to arrest a suspect outside a football game. In China it verifies the identities of ride-hailing drivers, permits tourists to enter attractions and lets people pay for things with a smile. Apple’s new iPhone is expected to use it to unlock the homescreen. 

Python overtakes R, becomes the leader in Data Science, Machine Learning platforms
While in 2016 Python was in 2nd place ("Mainly Python" had 34% share vs 42% for "Mainly R"), in 2017 Python had 41% vs 36% for R. 

Object detection: an overview in the age of Deep Learning
There’s no shortage of interesting problems in computer vision, from simple image classification to 3D-pose estimation. One of the problems we’re most interested in and have worked on a bunch is object detection. 

Background removal with deep learning
Background removal is a task that is quite easy to do manually, or semi manually (Photoshop, and even Power Point has such tools) if you use some kind of a “marker” and edge detection.  However, fully automated background removal is quite a challenging task.

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/