Data is big
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UCI Machine Learning Repository

UCI Machine Learning Repository | Data is big | Scoop.it
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We currently maintain 253 data sets as a service to the machine learning community. You may view all data sets through our searchable interface. Our old web siteis still available, for those who prefer the old format.

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Data is big
"The future is here. It's just not evenly distributed yet." - William Gibson     :::: Follow this topic for fresh resources and ideas related to Data Science, Machine Learning, Algorithms and #bigdata :::: <a href="http://www.dataisbig.co" rel="nofollow">http://www.dataisbig.co</a>/
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Launch a GPU-backed Google Compute Engine instance and setup Tensorflow, Keras and Jupyter

Launch a GPU-backed Google Compute Engine instance and setup Tensorflow, Keras and Jupyter | Data is big | Scoop.it
In this guide I’ll describe and explain how to launch a GPU-backed Compute Engine instance and set it up with CUDA, Tensorflow, Keras, Jupyter and so on.

Via Eric Feuilleaubois
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Scaling Keras Model Training to Multiple GPUs | Parallel Forall

Scaling Keras Model Training to Multiple GPUs | Parallel Forall | Data is big | Scoop.it
How to use Keras with the MXNet backend to achieve high performance and excellent multi-GPU scaling for deep learning training.
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Customer Churn – Logistic Regression with R

Customer Churn – Logistic Regression with R | Data is big | Scoop.it
In the customer management lifecycle, customer churn refers to a decision made by the customer about ending the business relationship. It is also referred as loss of clients or customers. Customer loyalty and customer churn always add up to 100%. If a firm has a 60% of loyalty rate, then their loss or churn rate of customers is 40%. As per 80/20 customer profitability rule, 20% of customers are generating 80% of revenue.

Via Eric Feuilleaubois
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Reinforcement Learning w/ Keras + OpenAI: Actor-Critic Models

Reinforcement Learning w/ Keras + OpenAI: Actor-Critic Models | Data is big | Scoop.it
Last time in our Keras/OpenAI tutorial, we discussed a very fundamental algorithm in reinforcement learning: the DQN. The Deep Q-Network is actually a fairly new advent that arrived on the seen only…
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A Self-Study List for Data Engineers and Aspiring Data Architects

A Self-Study List for Data Engineers and Aspiring Data Architects | Data is big | Scoop.it
Study List for Data Engineers and Aspiring Data Architects
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My Curated List of AI and Machine Learning Resources from Around the Web

My Curated List of AI and Machine Learning Resources from Around the Web | Data is big | Scoop.it
When I was writing books on networking and programming topics in the early 2000s, the web was a good, but an incomplete resource. Blogging had started to take off, but YouTube wasn’t around yet, nor…
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Deep Learning with R

Deep Learning with R | Data is big | Scoop.it
For R users, there hasn’t been a production grade solution for deep learning (sorry MXNET). This post introduces the Keras interface for R and how it can be used to
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Tensorflow and Keras finally find their way to the R world
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Thinking of doing a machine learning PhD? Read this first.

Thinking of doing a machine learning PhD? Read this first. | Data is big | Scoop.it
We explain why it’s a high-impact area, how to work out if it’s for you, and exactly how and where to apply.
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Redefining Basketball Positions with Unsupervised Learning

Redefining Basketball Positions with Unsupervised Learning | Data is big | Scoop.it
The NBA Finals are over. The last of the champagne bottles have been emptied and the confetti has begun to settle. Now that the Golden State Warriors have finished unleashing their otherworldly…
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The MMC Ventures AI Investment Framework: 17 success factors for the age of AI

The MMC Ventures AI Investment Framework: 17 success factors for the age of AI | Data is big | Scoop.it

Artificial intelligence — specifically, machine learning (ML) — is a powerful ‘enabling technology’ that represents a paradigm shift in software capability.

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The 5 Levels of Machine Learning Iteration

The 5 Levels of Machine Learning Iteration | Data is big | Scoop.it
Practical machine learning has a distinct cyclical nature that demands constant iteration, tuning, and improvement. We aim to showcase its beauty.
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Everything that Works Works Because it's Bayesian: Why Deep Nets Generalize?

Everything that Works Works Because it's  Bayesian: Why Deep Nets Generalize? | Data is big | Scoop.it
The Bayesian community should really start going to ICLR. They really should have started going years ago. Some people actually have. For too long we Bayesians have, quite arrogantly, dismissed deep neural networks as unprincipled, dumb black boxes that lack elegance. We said that highly over-parametrised models fitted via maximum
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Creating a Modern OCR Pipeline Using Computer Vision and Deep Learning

In this post we will take you behind the scenes on how we built a state-of-the-art Optical Character Recognition (OCR) pipeline for our mobile document scanner.
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Deep Learning Mindmap

Deep Learning Mindmap | Data is big | Scoop.it
deeplearning-mindmap - A mindmap summarising Deep Learning concepts.
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Marc Kneepkens's curator insight, August 20, 7:52 AM

Deep Learning is part of a broader family of Machine Learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised, or unsupervised. This is an attempt to summarize this large field in one .PDF file.

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Deep Learning Our Way Through Fashion Week – Inside EDITED

Deep Learning Our Way Through Fashion Week – Inside EDITED | Data is big | Scoop.it
AI, deep learning — or whatever we decide to call this new wave of neural network applications — has many industries racing to design the latest and most intelligent learning systems in an attempt to…
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Principal Component Analysis Course Using FactoMineR - Articles - STHDA

Principal Component Analysis Course Using FactoMineR - Articles - STHDA | Data is big | Scoop.it
Statistical tools for data analysis and visualization
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Bootstrapping Reinforcement Learning – Suman Deb Roy – Medium

Bootstrapping Reinforcement Learning – Suman Deb Roy – Medium | Data is big | Scoop.it
There are 3 overarching ways to do machine learning. There’s Supervised Learning (“this is a car” �), Unsupervised Learning (“all these things look like cars” � � �) and Reinforcement learning …
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Neural networks for algorithmic trading. Multimodal and multitask deep learning

Neural networks for algorithmic trading. Multimodal and multitask deep learning | Data is big | Scoop.it
Here we are again! We already have four tutorials on financial forecasting with artificial neural networks where we compared different architectures for financial time series forecasting, realized…
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120 Machine Learning business ideas from the latest McKinsey report.

120 Machine Learning business ideas from the latest McKinsey report. | Data is big | Scoop.it
Machine learning is on the edge of revolutionizing those 12 sectors. Most leaders in those industries look at Machine Learning and see a non-stable, none viable technology in the short term.
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Zero to One — A Ton of Awe-Inspiring Deep Learning Demos with Code for Beginners

Zero to One — A Ton of Awe-Inspiring Deep Learning Demos with Code for Beginners | Data is big | Scoop.it
— said they wanted deep learning examples that they can just download and run. No Math. No Theory. No Books. It’s difficult to find deep learning examples that are open source and that also run first…
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The Data Analyst: A Potential Difference Maker | The Higher Tempo Press

The Data Analyst: A Potential Difference Maker | The Higher Tempo Press | Data is big | Scoop.it

Believe or not, not everyone on The Higher Tempo is actually any good at the game. Take me for example. My save over on my own blog has spanned five seasons – it took me three just to get promoted and I got instantly relegated then couldn’t bounce right back up. 

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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour | Data is big | Scoop.it
In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization.
ukituki's insight:
How long does it take to train the state-of-the-art imagenet model? The answer is one hour :) 

Yeah, remember when we just started with AlexNet it took a week, and our model has already grown like 10x bigger in the meantime too! So consider the "software Moore's Law" broken.
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Deep Learning Challenges from a Kaggle Competition

Vladimir Iglovikov, Kaggle Master, talks about a Deep Learning approach to the "Dstl Satellite Imagery Feature Detection" competition, challenges an
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