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• Embedded Analytics: The Future of Business Intelligence - Sep 30, 2016.
  
  An overview of the evolution of Business Intelligence, and some insight into where its future lie: embedded analytics.

  Analytics, API, Business Intelligence

• Deep Learning Reading Group: SqueezeNet - Sep 29, 2016.
  
  This paper introduces a small CNN architecture called “SqueezeNet” that achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters.

  Compression, Deep Learning, Lab41, Machine Learning, Neural Networks

• Data Science of Sales Calls: The Surprising Words That Signal Trouble or Success - Sep 29, 2016.
  
  While not as profound a problem as uncovering the secrets of the universe, how to conduct a successful sales conversation is an age-old problem, impacting millions of people every day.

  Gong.io, Machine Learning, Sales, Speech Recognition

• Top Data Scientist Claudia Perlich on Biggest Issues in Data Science - Sep 29, 2016.
  
  Find out what top data scientist Claudia Perlich believes are - and are not - the biggest issues in data science today, and why spending 80% of their time with data preparation is not a problem.

  Claudia Perlich, Data Science

• Data Science Basics: Data Mining vs. Statistics - Sep 28, 2016.
  
  As a beginner I was confused at the relationship between data mining and statistics. This is my attempt to help straighten out this connection for others who may now be in my old shoes.

  Beginners, Data Mining, Statistics

• Data Science for Internet of Things (IoT): Ten Differences From Traditional Data Science - Sep 26, 2016.
  
  The connected devices (The Internet of Things) generate more than 2.5 quintillion bytes of data daily. All this data will significantly impact business processes and the Data Science for IoT will take increasingly central role. Here we outline 10 main differences between Data Science for IoT and traditional Data Science.

  Data Science, Deep Learning, IoT, Privacy, Robots

• Comparing Clustering Techniques: A Concise Technical Overview - Sep 26, 2016.
  
  A wide array of clustering techniques are in use today. Given the widespread use of clustering in everyday data mining, this post provides a concise technical overview of 2 such exemplar techniques.

  Algorithms, Clustering, K-means, Machine Learning

• Top 16 Active Big Data, Data Science Leaders on LinkedIn - Sep 23, 2016.
  
  Who are the most active Big Data, Data Science Influencers and Leaders on LinkedIn? We analyze the data and bring you the list of key people to follow.

  About Gregory Piatetsky, Bernard Marr, Big Data, Big Data Influencers, Carla Gentry, Data Science, DJ Patil, Influencers, LinkedIn, Tom Davenport

• Deep Learning Reading Group: Deep Residual Learning for Image Recognition - Sep 22, 2016.
  
  Published in 2015, today's paper offers a new architecture for Convolution Networks, one which has since become a staple in neural network implementation. Read all about it here.

  Academics, Convolutional Neural Networks, Deep Learning, Image Recognition, Lab41, Machine Learning, Neural Networks

• Data Science Basics: 3 Insights for Beginners - Sep 22, 2016.
  
  For data science beginners, 3 elementary issues are given overview treatment: supervised vs. unsupervised learning, decision tree pruning, and training vs. testing datasets.

  Algorithms, Beginners, Datasets, Overfitting, Supervised Learning, Unsupervised Learning

• Support Vector Machines: A Concise Technical Overview - Sep 21, 2016.
  
  Support Vector Machines remain a popular and time-tested classification algorithm. This post provides a high-level concise technical overview of their functionality.

  Algorithms, Machine Learning, Support Vector Machines

• 9 Key Deep Learning Papers, Explained - Sep 20, 2016.
  
  If you are interested in understanding the current state of deep learning, this post outlines and thoroughly summarizes 9 of the most influential contemporary papers in the field.

  Pages: 1 2 3

  Academics, Deep Learning, Explained, Neural Networks

• The Great Algorithm Tutorial Roundup - Sep 20, 2016.
  
  This is a collection of tutorials relating to the results of the recent KDnuggets algorithms poll. If you are interested in learning or brushing up on the most used algorithms, as per our readers, look here for suggestions on doing so!

  Algorithms, Clustering, Decision Trees, K-nearest neighbors, Machine Learning, PCA, Poll, random forests algorithm, Regression, Statistics, Text Mining, Time Series, Visualization

• Random Forest®: A Criminal Tutorial - Sep 19, 2016.
  
  Get an overview of Random Forest here, one of the most used algorithms by KDnuggets readers according to a recent poll.

  Algobeans, CA, Crime, random forests algorithm, San Francisco

• Decision Trees: A Disastrous Tutorial - Sep 15, 2016.
  
  Get a concise overview of decision trees here, one of the most used KDnuggets reader algorithms as measured in a recent poll.

  Algobeans, Decision Trees, Titanic

• SlangSD: A Sentiment Dictionary for Slang Words - Sep 14, 2016.
  
  The Slang Sentiment Dictionary (SlangSD) includes over 90,000 slang words together with their sentiment scores, facilitating sentiment analysis in user-generated contents.

  Natural Language Processing, NLP, Sentiment Analysis

• Top Algorithms and Methods Used by Data Scientists - Sep 12, 2016.
  
  Latest KDnuggets poll identifies the list of top algorithms actually used by Data Scientists, finds surprises including the most academic and most industry-oriented algorithms.

  Pages: 1 2

  Algorithms, Clustering, Data Visualization, Decision Trees, Poll, Regression

• Urban Sound Classification with Neural Networks in Tensorflow - Sep 12, 2016.
  
  This post discuss techniques of feature extraction from sound in Python using open source library Librosa and implements a Neural Network in Tensorflow to categories urban sounds, including car horns, children playing, dogs bark, and more.

  Pages: 1 2

  Deep Learning, Feature Extraction, Machine Learning, Neural Networks, TensorFlow

• The (Not So) New Data Scientist Venn Diagram - Sep 12, 2016.
  
  This post outlines a (relatively) new(er) Data Science-related Venn diagram, giving an update to Conway's classic, and providing further fuel for flame wars and heated disagreement.

  Data Science, Data Scientist, Drew Conway, Venn Diagram, Yanir Seroussi

• Doing the Data Science That Drives Predictive Personalization - Sep 9, 2016.
  
  Agile collaboration within data science teams is essential to the vision of customer analytics and personalization. Attend IBM DataFirst Launch Event on Sep 27 in New York City to engage with open-source community leaders and practitioners.

  Clustering, Customer Analytics, IBM, New York City, NY

• Deep Learning Reading Group: Deep Networks with Stochastic Depth - Sep 8, 2016.
  
  An concise overview of a recent paper which introduces a new way to perturb networks during training in order to improve their performance, stochastic depth networks.

  Academics, Deep Learning, Lab41, Neural Networks

• A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2 - Sep 8, 2016.
  
  This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.

  Pages: 1 2

  Beginners, Convolutional Neural Networks, Deep Learning, Neural Networks

• Introducing Dask for Parallel Programming: An Interview with Project Lead Developer - Sep 7, 2016.
  
  Introducing Dask, a flexible parallel computing library for analytics. Learn more about this project built with interactive data science in mind in an interview with its lead developer.

  Analytics, Continuum Analytics, Dask, Data Science, Distributed Computing, Parallelism, Python, Scientific Computing

• KDnuggets™ News 16:n32, Sep 7: Cartoon: Data Scientist was sexiest job until…; Up to Speed on Deep Learning - Sep 7, 2016.
  
  Cartoon: Data Scientist - the sexiest job of the 21st century until...; Up to Speed on Deep Learning: July Update; How Convolutional Neural Networks Work; Learning from Imbalanced Classes; What is the Role of the Activation Function in a Neural Network?

  Balancing Classes, Convolutional Neural Networks, Data Scientist, Deep Learning, Neural Networks

• A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1 - Sep 6, 2016.
  
  Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.

  Pages: 1 2

  Beginners, Convolutional Neural Networks, Deep Learning, Neural Networks

• Cartoon: Labor Day in the era of Robotics - Sep 5, 2016.
  
  Amidst all the discussion about robots and automation taking over human jobs, new KDnuggets cartoon looks at how Labor Day can evolve by 2050.

  Automated, Cartoon, Labor Day, Robots, Skills

• The Evolution of IoT Edge Analytics: Strategies of Leading Players - Sep 2, 2016.
  
  This article explores the significance and evolution of IoT edge analytics. Since the author believes that hardware capabilities will converge for large vendors, IoT analytics will be the key differentiator.

  Analytics, Cisco, Dell, HPE, IBM, Intel, IoT, PMML

• The Human Vector: Incorporate Speaker Embeddings to Make Your Bot More Powerful - Sep 2, 2016.
  
  One of the many ways in which bots can fail is by their (lack of) persona. Learn how speaker embeddings can help with this problem, and can help improve the persona of your bot.

  Bots, Chatbot, Natural Language Processing

• Data Science vs Crime: Detecting Pickpocket Suspects from Transit Records - Sep 1, 2016.
  
  A team of US and Chinese researchers has creatively used massive data collected by automated fare collectors for identifying thieves in the public transit systems. The system was tested in Beijing and was able to identify 93% of known pickpockets.

  Anomaly Detection, Beijing, China, Crime, Hui Xiong, Mobility, Rutgers