[unable to retrieve full-text content]Also: Deep Learning and Neural Networks Primer; A New Beginning to Deep Learning; The most important step in Machine Learning process.
Original Post: KDnuggets™ News 17:n32, Aug 23: The Rise of GPU Databases; Instagramming with Python for Data Analysis
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Original Post: Highlights of the Data Science Track at Microsoft Ignite
[unable to retrieve full-text content]I am writing this article to show you the basics of using Instagram in a programmatic way. You can benefit from this if you want to use it in a data analysis, computer vision, or any other cool project you can think of.
Original Post: A Guide to Instagramming with Python for Data Analysis
[unable to retrieve full-text content]Also 37 Reasons why your #NeuralNetwork is not working; Making Predictive Model Robust: Holdout vs Cross-Validation.
Original Post: Top KDnuggets tweets, Aug 09-15: #Tensorflow tutorials and best practices; Top Influencers for #DataScience
[unable to retrieve full-text content]Python vs R vs Other – What did you use for Analytics, Data Science, Machine Learning work in 2016-17? Vote and we will analyze and report results and trends.
Original Post: New Poll: Python vs R vs rest: What did you use in 2016-17 for Analytics, Data Science, Machine Learning tasks?
[unable to retrieve full-text content]This post introduces five perfectly valid ways of measuring distances between data points. We will also perform simple demonstration and comparison with Python and the SciPy library.
Original Post: Comparing Distance Measurements with Python and SciPy
[unable to retrieve full-text content]Back-to-school sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop – only $10 or $12 until Aug 10, 2017.
Original Post: Best Data Science, Machine Learning Courses from Udemy (only or till Aug 10)
[unable to retrieve full-text content]Also Hill criteria for #causality vs #correlation via #xkcd cartoons; #MachineLearning Workflows in #Python from Scratch Part 2: k-means Clustering
Original Post: Top KDnuggets tweets, Jul 26 – Aug 01: 37 Reasons why your #NeuralNetwork is not working; Machine Learning Exercises in Python
The Solutions section of the Cortana Intelligence Gallery provides more than two dozen working examples of applying machine learning, data science and artificial intelligence to real-world problems. Each solution provides sample data, scripts for model training and evaluation, and reporting of predictions. You can deploy a complete stack in Azure to implement the solution with the click of a button, or follow instructions to deploy on your own hardware. The internals of each solution is fully documented and open source, so you can easily customize it to your needs. Here’s a brief overview of some solutions that have recently been posted to the Gallery. Click on the links in bold to be taken to the main solution page. Customer Churn Prediction. This solution uses historical customer transaction data to identify new customers that are most likely to churn (switch to a…
Original Post: Applications in energy, retail and shipping
[unable to retrieve full-text content]Machine Learning Exercises in Python: An Introductory Tutorial Series; The BI & Data Analysis Conundrum: 8 Reasons Why Many Big Data Analytics Solutions Fail to Deliver Value; The Internet of Things: An Introductory Tutorial Series; How to squeeze the most from your training data
Original Post: KDnuggets™ News 17:n29, Aug 2: Machine Learning Exercises in Python; 8 Reasons Why Many Big Data Analytics Solutions Fail