Big Data Big

Friday, October 14, 2016

Recent Big Data and Machine Learning Info Digest 2016/10/14

Deep Reinforcement Learning: Pong from Pixels

Data Mining in Python: A Guide

Top-down learning path: Machine Learning for Software Engineers

Can we open the black box of AI?

Gartner Survey Reveals Investment in Big Data Is Up but Fewer Organizations Plan to Invest

The broken promise of open-source Big Data software – and what might fix it
Posted by William at 10:04 PM No comments:
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Labels: AI, Andrej Karpathy, Big Data, Data Mining, Deep Learning, Gartner, Machine Learning, Nature, Open Source, Python, Tutorial

Friday, October 7, 2016

Recent Big Data and Machine Learning Info Digest 2016/10/07

Machine Learning in a Year

Staying on Top of The Game with Modern Big Data

Why Palantir is Silicon Valley’s most questionable unicorn

What is the difference between AI, Machine Learning, NLP, and Deep Learning?

Why Deep Learning Is Suddenly Changing Your Life
Posted by William at 11:54 PM No comments:
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Labels: AI, Big Data, Deep Learning, Machine Learning, NLP, Palantir

Saturday, October 1, 2016

Recent Big Data and Machine Learning Info Digest 2016/10/01

What are the major bottlenecks in making deep learning systems more effective (as of 2016)?

A Neural Network for Machine Translation, at Production Scale

Amazon's new GPU-cloud wants to chew through your AI and big data projects

Sentimental Analysis of the First Presidential Debate of 2016 Using Machine Learning

A Beginner's Guide to Apache Flink – 12 Key Terms, Explained

Announcing YouTube-8M: A Large and Diverse Labeled Video Dataset for Video Understanding Research
Posted by William at 11:46 PM No comments:
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Labels: AI, Amazon, Big Data, Cloud, Deep Learning, GPU, Hillary, Machine Learning, Presidential Debate, Quora, TensorFlow, Translation, Trump, YouTube

Friday, September 23, 2016

Recent Big Data and Machine Learning Info Digest 2016/09/23

Data Science: A Kaggle Walkthrough – Creating a Model

Deep Learning in a Nutshell: Reinforcement Learning

Why do deep neural nets require so much training data to perform well?

6 Startups Using AI for Algorithmic Trading Strategies

Ten Myths About Machine Learning

How Hillary's Campaign Is (Almost Certainly) Using Big Data
Posted by William at 11:43 PM No comments:
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Labels: AI, Big Data, Data Science, Deep Learning, Hillary, Kaggle, Nvidia, Quora

Saturday, September 17, 2016

Recent Big Data and Machine Learning Info Digest 2016/09/17

Beware of the gaps in Big Data

The 10 Algorithms Machine Learning Engineers Need to Know

How do you define "data science" and "data scientist"?

What is the Difference Between Deep Learning and “Regular” Machine Learning?

What You Know About Deep Learning Is A Lie


Attention and Augmented Recurrent Neural Networks


Posted by William at 9:25 PM No comments:
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Labels: Algorithms, Big Data, Data Science, Data Scientist, Deep Learning, DJ Patil, Jason Brownlee, Machine Learning, RNN

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

Posted by William at 11:35 PM No comments:
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Labels: Andrej Karpathy, CNN, Data Engineering, Deep Learning, PhD, Udacity

Friday, September 2, 2016

Recent Big Data and Machine Learning Info Digest 2016/09/02

The Simple, Elegant Algorithm That Makes Google Maps Possible

Baidu open-sources Python-driven machine learning framework

How a Japanese cucumber farmer is using deep learning and TensorFlow

Kafka in Action: 7 Steps to Real-Time Streaming From RDBMS to Hadoop

Data Ebook Archive
Posted by William at 11:02 PM No comments:
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Labels: Algorithms, Baidu, cucumber, Deep Learning, Ebook, Google, Hadoop, Kafka, Map, O'Reilly, Open Source, Python, TensorFlow
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