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/

Recent AI, Big Data and Machine Learning Info Digest 2017/07/26

Deep Reinforcement Learning: An Overview (by Yuxi Li, version 3, July 15, 2017)
We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, in particular, differentiable neural computer (DNC), unsupervised learning, transfer learning, semi-supervised learning, hierarchical RL, and learning to learn. Then we discuss various applications of RL, including games, in particular, AlphaGo, robotics, natural language processing, including dialogue systems (a.k.a. chatbots), machine translation, and text generation, computer vision, neural architecture design, business management, finance, healthcare, Industry 4.0, smart grid, intelligent transportation systems, and computer systems. We mention topics not reviewed yet. After listing a collection of RL resources, we present a brief summary, and close with discussions.

ImageNet Object Localization Challenge
This year, Kaggle is thrilled to be the official host of all three ImageNet Challenges for the first time including the other two competitions:
Object Detection Challenge
Object Detection from Video Challenge

Deep Learning for NLP Best Practices
This post is a collection of best practices for using neural networks in Natural Language Processing. It will be updated periodically as new insights become available and in order to keep track of our evolving understanding of Deep Learning for NLP.

Hands-On Tour Of Apache Spark In 5 Minutes

In this tutorial, we will use an Apache Zeppelin notebook for our development environment to keep things simple and elegant. 

A list of artificial intelligence tools you can use today — for personal use (1/3)

This is Part 1 of 3 in a series of posts that looks at the landscape of the artificial intelligence industry and the companies and institutes developing products that are moving the needle of knowledge of machine intelligence and consciousness forward for humanity.

A list of artificial intelligence tools you can use today — for businesses (2/3)

This list contains companies working on artificial intelligence and machine learning products primarily for business use, non-specific to any industry. Industry specific AI will be the final part of this series.

Recent AI, Big Data and Machine Learning Info Digest 2017/07/04

How to build a data science pipeline
Start with y. Concentrate on formalizing the predictive problem, building the workflow, and turning it into production rather than optimizing your predictive model. Once the former is done, the latter is easy.

How Deep Learning Is Personalizing the Internet
Personalization is no doubt one of the strongest imperatives today in the internet industry as a whole and deep learning almost certainly holds tremendous potential in this area. Therefore, businesses that aim to remain on the cutting edge need to keep an eye out for advancements in the field.

3 Massive Big Data Problems Everyone Should Know About
There are 3 Big Data concerns that should keep people up at night: Data Privacy, Data Security and Data Discrimination.

Deep Learning Research Review Week 2: Reinforcement Learning
This is the 2nd installment of a new series called Deep Learning Research Review by Adit Deshpande . This week he focuses on Reinforcement Learning.

Hands on with Deep Learning – Solution for Age Detection Practice Problem
In this article, Faizan Shaikh explained a simple benchmark solution for Age Detection Practice Problem. 

Architecture of Convolutional Neural Networks (CNNs) demystified
Dishashree Gupta provides an intuition into convolutional neural networks by not going into the complex mathematics of CNN.