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• Top 10 Open Dataset Resources on Github - May 31, 2016.
  
  The top open dataset repositories on Github include a variety of data, freely available for use by researchers, practitioners, and students alike.

  Datasets, GitHub, Machine Learning, Open Data

• Predicting Popularity of Online Content - May 30, 2016.
  
  A look at predicting what makes online content popular, with a particular focus on images, especially selfies.

  Pages: 1 2

  Prediction, Selfie

• Free eBook: Healthcare Social Media Analytics and Marketing - May 27, 2016.
  
  Get your free copy of a new ebook outlining social media marketing and analytics strategies (including code) for healthcare professionals.

  Free ebook

• A Concise Overview of Standard Model-fitting Methods - May 27, 2016.
  
  A very concise overview of 4 standard model-fitting methods, focusing on their differences: closed-form equations, gradient descent, stochastic gradient descent, and mini-batch learning.

  Pages: 1 2

  Cost Function, Gradient Descent, Machine Learning, Sebastian Raschka

• 5 Ways in Which Big Data Can Help Leverage Customer Data - May 25, 2016.
  
  Every business enterprise realizes the importance of big data but rarely puts the customer data that they possess to good use. Here are few ways enterprises can leverage customer data.

  Analytics, Big Data, Data Management, Data Mining

• Let Me Hear Your Voice and I’ll Tell You How You Feel - May 24, 2016.
  
  This post provides an overview of a voice tone analyzer implemented as part of a cohesive emotion detection system, directly from the researcher and architect.

  Artificial Intelligence, Deep Learning, Emotion

• 10 Must Have Data Science Skills, Updated - May 23, 2016.
  
  An updated look at the state of the data science landscape, and the skills - both technical and non-technical - that are absolutely required to make it as a data scientist.

  Pages: 1 2

  Advice, Books, Data Science Skills, Data Scientist, MOOC

• How to Explain Machine Learning to a Software Engineer - May 20, 2016.
  
  How do you explain what machine learning is to the uninitiated software engineer? Read on for one perspective on doing so.

  Automating, Machine Learning, Software Engineer

• 5 Machine Learning Projects You Can No Longer Overlook - May 19, 2016.
  
  We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.

  Data Cleaning, Deep Learning, Machine Learning, Open Source, Overlook, Pandas, Python, scikit-learn, Theano

• Tips for Data Scientists: Think Like a Business Executive - May 18, 2016.
  
  Thinking like a Data Scientist is important; it puts businesses and business leaders in an analytical frame of mind. But it is also important for Data Scientists to be able to think like business executives. Read on to find out why.

  Advice, Analytics, Data Scientist

• The Amazing Power of Word Vectors - May 18, 2016.
  
  A fantastic overview of several now-classic papers on word2vec, the work of Mikolov et al. at Google on efficient vector representations of words, and what you can do with them.

  Pages: 1 2

  Distributed Representation, NLP, word2vec

• Embrace the Random: A Case for Randomizing Acceptance of Borderline Papers - May 16, 2016.
  
  A case for using randomization in the selection of borderline academic papers, a particular use case which has parallels with many other possible scenarios.

  Academics, ICML, NIPS, Random, Randomization

• Practical skills that practical data scientists need - May 13, 2016.
  
  The long story short, data scientist needs to be capable of solving business analytics problems. Learn more about the skill-set you need to master to achieve so.

  Business Context, Data Scientist, Mathematics, Skills, SQL

• Troubleshooting Neural Networks: What is Wrong When My Error Increases? - May 13, 2016.
  
  An overview of some of the things that could lead to an increased error rate in neural network implementations.

  Deep Learning, Neural Networks, Overfitting

• Are Deep Neural Networks Creative? - May 12, 2016.
  
  Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?

  Artificial Intelligence, Deep Learning, Generative Adversarial Network, Generative Models, Recurrent Neural Networks, Reinforcement Learning, Zachary Lipton

• Deep Learning and Neuromorphic Chips - May 12, 2016.
  
  The 3 main ingredients to creating artificial intelligence are hardware, software, and data, and while we have focused historically on improving software and data, what if, instead, the hardware was drastically changed?

  AI, Brain, Deep Learning, Neural Networks

• Implementing Neural Networks in Javascript - May 12, 2016.
  
  Javascript is one of the most prevalent and fastest growing languages in existence today. Get a quick introduction to implementing neural networks in the language, and direction on where to go from here.

  Javascript, MNIST, Neural Networks

• Meet the 11 Big Data & Data Science Leaders on LinkedIn - May 6, 2016.
  
  In this post, we present a list of popular data science leaders on LinkedIn. Follow these leaders who will keep you in touch with the latest Data Science happenings!

  About Gregory Piatetsky, Bernard Marr, Big Data, Data Scientist, DJ Patil, Hilary Mason, Influencers, LinkedIn, Tom Davenport

• Why Implement Machine Learning Algorithms From Scratch? - May 6, 2016.
  
  Even with machine learning libraries covering almost any algorithm implementation you could imagine, there are often still good reasons to write your own. Read on to find out what these reasons are.

  Algorithms, Machine Learning

• How Much do Analytics Salaries Increase when Changing Jobs? - May 4, 2016.
  
  A data-informed analysis of analytics career salaries and their increase when changing jobs.

  Analytics, Burtch Works, Career, Salary

• A Data Science Approach to Writing a Good GitHub README - May 4, 2016.
  
  Readme is the first file every user will look for, whenever they are checking out the code repository. Learn, what you should write inside your readme files and analyze your existing files effectiveness.

  Algorithmia, GitHub, Text Mining

• Datasets Over Algorithms - May 3, 2016.
  
  The average elapsed time between key algorithm proposals and corresponding advances is about 18 years; the average elapsed time between key dataset availabilities and corresponding advances is less than 3 years, 6 times faster.

  Algorithms, Datasets

• How to Network and Build a Personal Brand in Data Science - May 2, 2016.
  
  SpringBoard shares some ideas on how to network and build a data career, as taken from a new guide they have put together on the topic.

  Career, KDD, Mentorship, Strata

• How to Use Cohort Analysis to Improve Customer Retention - May 2, 2016.
  
  Cohort analysis is a subset of behavioral analytics that takes the user data and breaks them into related groups for analysis. Let’s understand using cohort analysis with an example of daily cohort of app users.

  Pages: 1 2

  Churn, CleverTap, Customer Analytics, Customer Behavior