2016 Sep Tutorials, Overviews

• 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

• 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

• Spark for Scale: Machine Learning for Big Data - Sep 23, 2016.
  This post discusses the fundamental concepts for working with big data using distributed computing, and introduces the tools you need to build machine learning models.

  Apache Spark, Big Data, Hadoop, HDFS, Machine Learning, MapReduce

• 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.

  Academics, Deep Learning, Explained, Neural Networks

• Machine Learning in a Year: From Total Noob to Effective Practitioner - Sep 19, 2016.
  Read how the author went from self-described total machine learning noob to being able to effectively use machine learning effectively on real world projects at work... all within a year.

  Advice, Beginners, Machine Learning

• 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

• 7 Steps to Mastering Apache Spark 2.0 - Sep 16, 2016.
  Looking for a comprehensive guide on going from zero to Apache Spark hero in steps? Look no further! Written by our friends at Databricks, this exclusive guide provides a solid foundation for those looking to master Apache Spark 2.0.
   
   

  7 Steps, Apache Spark, Databricks

• Deep Learning Reading Group: Deep Compression - Sep 15, 2016.
  An concise overview of a paper covering three methods of compressing a neural network in order to reduce the size of the network on disk, improve performance, and decrease run time.

  Academics, Compression, Deep Learning, Lab41, Neural Networks

• 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

• 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.

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

• Automating Data Ingestion: 3 Important Parts - Sep 9, 2016.
  In the day and age of ‘Big Data”, data ingestion has to be automated on some level. How best to automate it?

  Automation, Claudia Perlich, Data Preparation, ETL, Quora

• 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.

  Beginners, Convolutional Neural Networks, 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.

  Beginners, Convolutional Neural Networks, Deep Learning, Neural Networks

• 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

• HPE Haven OnDemand: Powerful Data Connectors for the Cloud and Enterprise - Sep 1, 2016.
  HPE Haven OnDemand simplifies how you can interact with data, allowing it to be transformed into an asset anytime, anywhere. Find out how the Connector APIs can facilitate this interaction.

  API, Cloud, Haven OnDemand, HPE, Python