- What I learned from looking at 200 machine learning tools - Jul 21, 2020.
While hundreds of machine learning tools are available today, the ML software landscape may still be underdeveloped with more room to mature. This review considers the state of ML tools, existing challenges, and which frameworks are addressing the future of machine learning software.
- The Most Useful Machine Learning Tools of 2020 - Mar 13, 2020.
This articles outlines 5 sets of tools every lazy full-stack data scientist should use.
- 10 Tools to Help You Learn R - Jan 4, 2018.
There are several tools to help you grasp the foundational principles and more. The list below gives you an idea of what’s available and how much it costs.
- 35 Open Source tools for Internet of Things - Jul 25, 2016.
If you have heard about the Internet of Things many times by now, its time to join the conversation. Explore the many open source tools & projects related to Internet of Things.
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- A Look Back on the 1st Three Months of Becoming a Data Scientist - Jan 13, 2016.
A person new to the Data Science field summarizes his surprising findings after a few months on the job.
- 5 Ways Data Scientists Keep Learning After College - Dec 17, 2015.
Taken from the answers experts gave, here is a compiled list of 5 essential actions and attitudes that keep data scientists learning after their degrees.
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- 50 Deep Learning Software Tools and Platforms, Updated - Dec 15, 2015.
We present the popular software & toolkit resources for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch. Explore the new list!
- Interview: Ramkumar Ravichandran, Visa on Actionable Insights – Easier Said Than Done - Jul 14, 2015.
We discuss Analytics at Visa, adapting to the Big Data world, gaps between expectations and delivery from Analytics, delivering Actionable Insights, and tools/technologies used.
- Interview: Joseph Babcock, Netflix on Genie, Lipstick, and Other In-house Developed Tools - Jun 16, 2015.
We discuss role of analytics in content acquisition, data architecture at Netflix, organizational structure, and open-source tools from Netflix.
- Interview: Hobson Lane, SHARP Labs on the Beauty of Simplicity in Analytics - May 13, 2015.
We discuss Predictive Analytics projects at Sharp Labs of America, common myths, value of simplicity, tools and technologies, and notorious data quality issues.
- Interview: Ben Werther, CEO, Platfora on Why Big Data Needs Self-Service Tools - Dec 29, 2014.
We discuss the importance of self-service model for Big Data tools, Small Data vs. Big Data, unique advantages of Platfora, key enhancements in Platfora 4.0 and more.
- Interview: Daqing Zhao, Macys.com on Building Effective Data Models for Marketing - Dec 11, 2014.
We discuss the challenges in identifying the fair price of ad media, recommendations for building effective models for online marketing, unique challenges of Mobile channel, selection of Big Data tools, and more.
- ‘Magic’ – Quickest Way to Turn Webpage Into Data - Nov 5, 2014.
Import.io's new feature - 'Magic' allows users to instantly turn web pages into tables of data: No Plugins, No Training, No Setup. Learn More.
- TweetNLP: Twitter Natural Language Processing - Oct 24, 2014.
A short overview of Natural Language Processing tools and utilities developed by Prof. Noah Smith, CMU and his team to analyze Twitter data.
- Founders of Yahoo! and MySQL Invest in Import.io - Sep 8, 2014.
Import.io raises $3M round from Jerry Yang and David Axmark. Released a streamlined version of web data extraction tool with exciting new features.
- Interview: John Funge, CTO, Knack on Why Gaming is the Next Big Thing for Hiring - Aug 18, 2014.
We discuss the gamification of hiring, founding story of Knack, applications of Predictive Human Analytics, challenges, Big Data tools and technology used, key qualities sought in data scientists, career advice and more.
- Course: Tools for Discovering Patterns in Data, Sep 8-9 - Jul 24, 2014.
Dr. John Elder describes powerful analytic methods for classification and estimation, explains the leading algorithms, compares their merits, and demonstrates their effectiveness on practical applications.
- BIDMach machine learning toolkit - Jul 14, 2014.
BIDMach machine learning toolkit offers "rooflined" (optimized to the limit) compute primitives and competitive performance on learning tasks like regression, clustering, classification, and matrix factorization.
- Domino – A Platform For Modern Data Analysis - Jun 26, 2014.
Tools that facilitate data science best practices have not yet matured to match their counterparts in the world of software engineering. Domino is a platform built from the ground up to fill in these gaps and accelerate modern analytical workflows.
- DLib: Library for Machine Learning - Jun 10, 2014.
DLib is an open source C++ library implementing a variety of machine learning algorithms, including classification, regression, clustering, data transformation, and structured prediction.
- Interview: Tom Kern, Risk Modeling Manager, Paychex on Risk Analytics and Sales Anticipation Model - Jun 2, 2014.
We discuss the role of Risk Analytics at Paychex, strategic importance of Sales Anticipation Model, optimizing business processes by leveraging Big Data, and advice for companies thinking about Big Data as well as aspiring students.
- Big Data BootCamp Santa Clara: Highlights of talks on Days 1-2 - May 9, 2014.
Highlights from the presentations by big data technology practitioners from Caspida, Datastax, ElephantScale, Hortonworks, MapR and Qubole at Big Data Bootcamp 2014 in Santa Clara.
- Top KDnuggets tweets, Mar 21-23: Machine Learning in Parallel with SVM; Good Data Sets for Data Science Practice - Mar 24, 2014.
Machine Learning in Parallel with SVM, GLM; Good Data Sets for Data Science Practice: Big enough, requires data engineering, rich; Cartoon: Why Madame Zaza, Fortune Teller, changes to Predictive Analytics; Top 45 #BigData Tools and Platforms for Developers