Recent AI, Big Data, Deep Learning, Machine Learning Info Digest 2017/08/25

Designing a Deep Learning Project 

The Present and Future of Quantum Computing for AI
Quantum computing is still in it’s infancy, and no universal architecture for quantum computers exists right now. However, their prototypes are already here and showing promising results in cryptography, logistics, modelling and optimization tasks. For AI researchers optimization and sampling is particularly important, because it allows to train Machine Learning models much faster with higher accuracy.

Artificial intelligence could be the future of banking
By leveraging AI, banks can engage with consumers in a faster and more consistent manner. They can use “bots” at contact centres for basic inquiries to free up employees for more complicated questions. They can use robo-advisers to provide basic investment services at lower cost.

New app scans your face and tells companies whether you’re worth hiring
HireVue, a company with a “video interview intelligence platform,” wants to make that easier by using artificial intelligence to do the heavy lifting for you and screen multiple candidates at once.

Pandas tips and tricks
This post includes some useful tips for how to use Pandas for efficiently preprocessing and feature engineering from large datasets.

The Hard Thing About Machine Learning
Building systems is hard; building machine learning systems that give robust predictions is especially hard.

Aug. 2017 Hive User Group Meeting @HortonWorks
1. Hive on Spark, production experience @Uber (Xuefu Zhang)
2. Reair and its usage for Uber's multi data center replication (Zheng Shao)
3. ACID, use cases in data management (Carter Shanklin)
4. Optimized Hive replication (Anishek Agarwal)
5. LLAP: Locality is dead (in the cloud) (Gopal Vijayaraghavan)
6. Don't reengineer, reimagine: Hive buzzing with Druid's magic potion (Slim Bouguerra)

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 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

Recent Big Data and Machine Learning Info Digest 2016/05/27

The real prerequisite for machine learning isn’t math, it’s data analysis

What is a resilient distributed dataset?

Microsoft HDInsight Recognized as a Leader in Forrester BigData Hadoop Cloud Wave

Learning Path for Developers & IT Professionals to become a Data Scientist

Easier data analysis in Python with pandas (video series)