Sep2017 Oct
All (77) | Courses, Education (6) | Meetings (12) | News, Features (15) | Opinions, Interviews (20) | Tutorials, Overviews (20) | Webcasts & Webinars (4)
- Data Scientist Guide to Apache Spark
- Oct 20, 2017.
Learn how data scientists can leverage Spark for advanced analytics with The Data Scientist’s Guide to Apache Spark, from Databricks!
- EGG2017: Innovate. Get Ahead. Disrupt. And Embrace Non-Conformity.
- Oct 20, 2017.
On November 30th 2017, there’s a new kind of data science & analytics conference: EGG2017, Dataiku’s first large-scale data science and analytics conference in New York, NY.
- Top 10 Machine Learning Algorithms for Beginners
- Oct 20, 2017.
A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.
- The ways that AI can change your business
- Oct 20, 2017.
AI technology involves a change in the value chain and represents a major challenge and opportunity for businesses. Managers are directly involved in this challenge, by accompanying the teams through this transition: vanquish fears, embracing innovation, transforming businesses, training teams.
- 5 Free Resources for Furthering Your Understanding of Deep Learning
- Oct 20, 2017.
This post includes 5 specific video-based options for furthering your understanding of neural networks and deep learning, collectively consisting of many, many hours of insights.
- Voices in AI – great conversations with leaders in AI, Machine Learning, Data Science
- Oct 19, 2017.
New Voices in AI podcast features conversations with top thinkers in the field, including Yoshua Bengio, Oren Etzioni, Jeff Dean, Daphne Koller, and Nick Bostrom. I am very honored to be in such company (on episode 11).
- Learn from Tesla, Google Brain, & Facebook – KDnuggets offer
- Oct 19, 2017.
Use the code KDNUGGETS to save an additional 20% on our San Francisco events. Sign up before the end of Early Bird registration (tomorrow, October 20) and you will save on top of the RE•WORK discount!
- It Only Takes One Line of Code to Run Regression
- Oct 19, 2017.
I learned how important to understand data before running algorithms, how important it is to know the context and the industry before jumping on getting insights, how it is very easy to make models but tough to get them to work for you, and finally, how it only takes one line of code to run linear regression on your dataset.
- 7 Types of Artificial Neural Networks for Natural Language Processing
- Oct 19, 2017.
What is an artificial neural network? How does it work? What types of artificial neural networks exist? How are different types of artificial neural networks used in natural language processing? We will discuss all these questions in the following article.
- 7 Techniques to Visualize Geospatial Data
- Oct 19, 2017.
In this article, we explore 7 interesting yet simple techniques to visualize geospatial data that will help you visualize your data better.
- Top KDnuggets tweets, Oct 11-17: Natural Stupidity is more Dangerous than #ArtificialIntelligence; A Beginners Guide to #DeepLearning
- Oct 18, 2017.
Also Collecting #DataScience Cheat Sheets; Luminoth: Open source toolkit for #ComputerVision.
- [webinar] Getting Started with Automated Analytics Powered By Machine Learning, Nov 8
- Oct 18, 2017.
Join Tellius and industry expert, Jen Underwood on Nov 8 to learn how companies today are moving beyond BI—leveraging automated analytics powered by machine learning to better understand their business.
- H2O World 2017: The best of data science, AI, and business transformation, Dec 4-5, Mountain View
- Oct 18, 2017.
The flagship H2O World is back to bring together the best of data science, AI, Machine Learning, and business transformation. Spaces are limited, so get a spot at 50% off w. code KDNUGGETS by Oct 21, 2017.
- Learning git is not enough: becoming a data scientist after a science PhD
- Oct 18, 2017.
Here is useful advice about moving from academia into data science after completing a PhD in a natural science.
- Key Trends and Takeaways from RE•WORK Deep Learning Summit Montreal – Part 2: The Pioneers
- Oct 18, 2017.
The most anticipated aspect of the RE•WORK Deep Learning Summit Montreal was the assembly of deep learning pioneers Yoshua Bengio, Yann LeCun, and Geoff Hinton on stage separately and together for the first time at such an event.
- Webinar: Business Growth Drives Adoption of Cloud Analytics, Oct 24
- Oct 17, 2017.
Learn how Duo Security leveraged the power of Snowflake and Looker to move to Cloud Analytics and get better reporting and insights on product usage, improved sales and marketing alignment, and more.
- Download NVIDIA DGX Systems eBook
- Oct 17, 2017.
In this eBook, you will learn how NVIDIA DGX Systems offer the fastest path to AI and deep learning, how to spend more time focused on experimentation and less time wrestling with IT, and using DGX Systems include access to NVIDIA-optimized deep learning frameworks.
- Random Forests(r), Explained
- Oct 17, 2017.
Random Forest, one of the most popular and powerful ensemble method used today in Machine Learning. This post is an introduction to such algorithm and provides a brief overview of its inner workings.
- 4 Major Trends Influencing the 2017 Predictive Analytics Hiring Market
- Oct 17, 2017.
We examine the implications of trends in hiring market, including the growth of quantitative Initiatives, blurring of the lines between Predictive Analytics and Data Science Professionals, and more .
- RE•WORK Deep Learning Summit Montreal Panel of Pioneers Interview: Yoshua Bengio, Yann LeCun, Geoffrey Hinton
- Oct 17, 2017.
At the Deep Learning Summit in Montreal last week, we saw Yoshua Bengio, Yann LeCun and Geoffrey Hinton come together to share their most cutting edge research progressions as well as discussing the landscape of AI and the deep learning ecosystem in Canada.
- KDD Impact Program to support Data Science projects with positive impact on society
- Oct 16, 2017.
The KDD Impact Program is looking to fund projects that have potential for a significant impact on society, expand outreach of data science, and strengthen the community, with grants of $10k-$100k for each project. Submission deadline is Dec 1, 2017.
- Top Stories, Oct 9-15: Want to Become a Data Scientist? Read This Interview First; An Overview of 3 Popular Courses on Deep Learning
- Oct 16, 2017.
Also: A Quick Guide to Fake News Detection on Social Media; How I started with learning AI in the last 2 months; Tidyverse, an opinionated Data Science Toolbox in R from Hadley Wickham; Understanding Machine Learning Algorithms; 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets
- Predicting State Healthcare Quality – at Predictive Analytics World Healthcare – Oct 29 – Nov 2
- Oct 16, 2017.
In anticipation of his upcoming conference presentation at Predictive Analytics World for Healthcare in New York, Oct 29–Nov 2, we asked Feras Batarseh, Research Assistant Professor, George Mason University a few questions about incorporating predictive analytics into healthcare.
- Social Media and Machine Learning Transform Self-service Data Prep
- Oct 16, 2017.
Social media and machine learning concepts are transforming self-service data prep into a collaborative data marketplace.
- Key Trends and Takeaways from RE•WORK Deep Learning Summit Montreal – Part 1: Computer Vision
- Oct 16, 2017.
Read up on what you missed from the RE•WORK Deep Learning Summit Montreal, held October 10 & 11, including talks from Aaron Courville, Ira Kemelmacher-Shlizerman, Roland Memisevic, and Raquel Urtasun.
- How LinkedIn Makes Personalized Recommendations via Photon-ML Machine Learning tool
- Oct 16, 2017.
In this article we focus on the personalization aspect of model building and explain the modeling principle as well as how to implement Photon-ML so that it can scale to hundreds of millions of users.
- Short course: Statistical Learning and Data Mining IV, NYC, Nov 2-3
- Oct 13, 2017.
This new two-day course gives a detailed and modern overview of statistical models used by data scientists for prediction and inference, with emphasis on tools useful for tackling modern-day data analysis problems.
- The Fast Path to Success with AI, DataRobot Webinar, Oct 26
- Oct 13, 2017.
Learn The Fast Path to Success with AI and see how industry leaders are generating ROI with AI Webinar Details Register today Thursday, October 26, 2017.
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An Overview of 3 Popular Courses on Deep Learning
- Oct 13, 2017.
After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera (which is not completely released) and Udacity, I believe a post about what you can expect from these 3 courses will be useful for future Deep learning enthusiasts. -
Want to Become a Data Scientist? Read This Interview First
- Oct 13, 2017.
There’s been a lot of hype about Data Science... and probably just as much confusion about it.
- [Webinar] Data Science for Big Data with Anaconda Enterprise, Oct 19
- Oct 12, 2017.
This Team Anaconda webinar, Oct 19, will demonstrate how easily the Anaconda Enterprise data science platform integrates with Hadoop and Spark clusters, giving your data scientists access to the libraries they need and empowering you to extract the most value from your Big Data.
- Data Science Bootcamp in Zurich, Switzerland, January 15 – April 6, 2018
- Oct 12, 2017.
Come to the land of chocolate and Data Science where the local tech scene is booming and the jobs are a plenty. Learn the most important concepts from top instructors by doing and through projects. Use code KDNUGGETS to save.
- Strata Data Conference, NYC – Key Trends and Highlights
- Oct 12, 2017.
Strata is a conference I very much enjoyed attending. This year, I observed a few common themes that ran across much of the conference content: Data Science Collaboration, Data Ethics, and Platform Optimization.
- Best practices of orchestrating Python and R code in ML projects
- Oct 12, 2017.
Instead of arguing about Python vs R I will examine the best practices of integrating both languages in one data science project.
- Top KDnuggets tweets, Oct 04-10: Using #MachineLearning to Predict, Explain Attrition; Tidyverse, an opinionated #DataScience Toolbox in R
- Oct 11, 2017.
Also #MachineLearning: Understanding Decision Tree Learning; #PyTorch tutorial distilled - Moving from #TensorFlow to PyTorch.
- Annual Global Artificial Intelligence Conference, Santa Clara, Jan 17-19, 2018
- Oct 11, 2017.
Over 50 leading experts in Artificial Intelligence area will present at our conference. Use code KDNUGGETS to save.
- Introducing R-Brain: A New Data Science Platform
- Oct 11, 2017.
R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker, which supports not only R, but also Python, SQL, has integrated intellisense, debugging, packaging, and publishing capabilities.
- Edge Analytics – What, Why, When, Who, Where, How?
- Oct 11, 2017.
Edge analytics is the collection, processing, and analysis of data at the edge of a network either at or close to a sensor, a network switch or some other connected device.
- Learn Generalized Linear Models (GLM) using R
- Oct 11, 2017.
In this article, we aim to discuss various GLMs that are widely used in the industry. We focus on: a) log-linear regression b) interpreting log-transformations and c) binary logistic regression.
- Don’t let a Career in Data Science Pass You By
- Oct 10, 2017.
Get the employer-aligned degree that gives you the hard skills in Python, R, SPSS, Tableau, and more from accredited and affordable Merrimack College.
- Wall Street and the New Data Paradigm at PAW Financial, Oct 29 – Nov 2
- Oct 10, 2017.
Trading is already mainly automated. The next wave is about deploying predictive models that can connect the dots from different alternative data sources to provide an edge to the decision-making process for investors.
- Tidyverse, an opinionated Data Science Toolbox in R from Hadley Wickham
- Oct 10, 2017.
Get your productivity boosted with Hadley Wickham's powerful R library, Tidyverse. It has all you need to start developing your own data science workflows.
- A Quick Guide to Fake News Detection on Social Media
- Oct 10, 2017.
Fake news is an important issue on social media. This article provides an overview of fake news characterization and detection in Data Science and Machine Learning research.
- The 5 Common Mistakes That Lead to Bad Data Visualization
- Oct 10, 2017.
Here are 5 common mistakes that lead to bad data visualization. Avoid these to get the most out of your data visualizations.
- Three Predictive Analytics Events in NYC – Business, Financial, Healthcare, Oct 29 – Nov 2
- Oct 9, 2017.
Predictive Analytics World, the leading cross-vendor event for data science and machine learning professionals will be at NYC, Oct 29-Nov 2. Pre-conference rates end Oct 27.
- IAPA National Conference on “Advancing Analytics,” October 18, Melbourne
- Oct 9, 2017.
Only three weeks until the IAPA National Conference "Advancing Analytics", October 18, Melbourne - don't miss this one-day to get up-to-date, meet with peers and hear from global leaders. KDNuggets readers receive a further 10% off full priced tickets, simply use the code ‘AAKDNUGGETS10’ at checkout.
- How I started with learning AI in the last 2 months
- Oct 9, 2017.
The relevance of a full stack developer will not be enough in the changing scenario of things. In the next two years, full stack will not be full stack without AI skills.
- Credible Sources of Accurate Information About AI
- Oct 9, 2017.
I want to recommend several credible sources of accurate information. Most of the writing on this list is intended to be accessible to anyone—even if you aren’t a programmer or don’t work in tech.
- Top Stories, Oct 2-8: Understanding Machine Learning Algorithms; XGBoost, a Top Machine Learning Method on Kaggle, Explained
- Oct 9, 2017.
Also: Using Machine Learning to Predict and Explain Employee Attrition; Data Science - The need for a Systems Engineering approach; Deep Learning for Object Detection: A Comprehensive Review; 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets
- Monty Hall chooses the final exit door
- Oct 7, 2017.
Monty Hall, the game show host, died last week. He was the host of the popular show "Let's Make a Deal", where contestants try to guess which one of 3 doors hides a valuable prize.
- Top September Stories: 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets
- Oct 6, 2017.
Also: 277 Data Science Key Terms, Explained; I built a chatbot in 2 hours and this is what I learned; Data Science and the Imposter Syndrome
- 5 overriding factors for the successful implementation of AI
- Oct 6, 2017.
Today AI is everywhere, from virtual assistants scheduling meetings, to facial recognition software and increasingly autonomous cars. We review 5 main factors for the successful AI implementation.
- How to Choose a Data Science Job
- Oct 6, 2017.
All Data Scientists worth their salt should know the importance of working with facts rather than hunches. That’s why in the following article we’ll throw light on how five emerging roles yield a proven value that companies cannot ignore.
- Deep Learning for Object Detection: A Comprehensive Review
- Oct 6, 2017.
By the end of this post, we will hopefully have gained an understanding of how deep learning is applied to object detection, and how these object detection models both inspire and diverge from one another.
- Chief AI Officer and Chief Data Scientist events, San Francisco, Nov 28-30 – special KDnuggets Offer
- Oct 5, 2017.
Join Corinium for 3 days of high level insight and discussion and learn actionable ways to benefit your business at special discounted tickets* for just $595 (saving up to $1000).
- Top 15 Master of Data Science Programs You May Want To Consider
- Oct 5, 2017.
Top MS of Data Science Programs in the US - on-campus and online that teach you how to humanize data and what you can do to make a difference in your company.
- Data Science –The need for a Systems Engineering approach
- Oct 5, 2017.
We need a greater emphasis on the Systems Engineering aspects of Data Science. I am exploring these ideas as part of my course "Data Science for Internet of Things" at the University of Oxford.
- Find Out What Celebrities Tweet About the Most
- Oct 5, 2017.
Word cloud is a popular data visualisation method. Here we show how to use R to create twitter word cloud of celebrities and politicians.
- A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)
- Oct 5, 2017.
Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.
- Applied Data Science: Solving a Predictive Maintenance Business Problem
- Oct 5, 2017.
The use case involved is to predict the end life of large industrial batteries, which falls under the genre of use cases called preventive maintenance use cases.
- Top KDnuggets tweets, Sep 27 – Oct 03: Introduction to #Blockchains & What It Means to #BigData; 7 More Steps to Mastering #MachineLearning With #Python
- Oct 4, 2017.
Also Jupyter Notebooks are Breathtakingly Featureless - Use Jupyter Lab; The 4 Types of Data #Analytics; Aspiring Data Scientists! Learn the basics with these 7 books.
- Big Data Bootcamp, Tampa, Dec 8-10
- Oct 4, 2017.
This is a fast paced, vendor agnostic, technical overview of the Big Data landscape, targeted towards people who want to understand the emerging world of Big Data. Use code KDNUGGETS to save.
- Make Your Data Mean More With Derived Variables
- Oct 4, 2017.
The chapter begins with modeling customer attrition in the cell phone industry, moves to a review of several classic variable combinations, and offers guidelines for the creation of derived variables.
- Using Machine Learning to Predict and Explain Employee Attrition
- Oct 4, 2017.
Employee attrition (churn) is a major cost to an organization. We recently used two new techniques to predict and explain employee turnover: automated ML with H2O and variable importance analysis with LIME.
- Neural Networks: Innumerable Architectures, One Fundamental Idea
- Oct 4, 2017.
At the end of this post, you’ll be able to implement a neural network to identify handwritten digits using the MNIST dataset and have a rough time idea about how to build your own neural networks.
- Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: October and Beyond
- Oct 3, 2017.
Coming soon: Deep Learning Montreal, ODSC London, TDWI Accelerate Seattle, IAPA Melbourne, Crunch Budapest, INFORMS Houston, Spark Summit Dublin, and many more.
- The Data Science Success Kit
- Oct 3, 2017.
The Data Science Success Kit is designed to get you to data science success quickly. Don't delay, offer ends October 31, 2017.
- A Course in Semantic Technologies for Designing a Proof-of-Concept
- Oct 3, 2017.
Ontotext live, online training designed to improve understanding of how Semantic Technology operates to help you make best use of it. Sign up by Oct 5 to save.
- XGBoost, a Top Machine Learning Method on Kaggle, Explained
- Oct 3, 2017.
Looking to boost your machine learning competitions score? Here’s a brief summary and introduction to a powerful and popular tool among Kagglers, XGBoost.
- Understanding Machine Learning Algorithms
- Oct 3, 2017.
Machine learning algorithms aren’t difficult to grasp if you understand the basic concepts. Here, a SAS data scientist describes the foundations for some of today’s popular algorithms.
- The 5 Best Industries to Find a Job in Data Science
- Oct 3, 2017.
There’s never been a better time to pursue a career in this field. With that in mind, here are five extremely practical and exciting fields you could leave a mark on with an education in data science.
- Statistical Mistakes Even Scientists Make
- Oct 3, 2017.
Scientists are all experts in statistics, right? Wrong.
- Top Stories, Sep 25-Oct 1: Introduction to Blockchains for Big Data; Top 10 Active Big Data, Data Science, Machine Learning LinkedIn Influencers
- Oct 2, 2017.
Also: 10 Things Everyone Should Know About Machine Learning; Top 10 Videos on Machine Learning in Finance; Machine Learning Reveals 9 Elements of Deal-Closing Sales; 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets
- Healthcare Analytics Summit 2017 – Dec 8, Harrisburg University
- Oct 2, 2017.
The 2017 Healthcare Analytics Summit is the fourth data analytics event at Harrisburg University of Science and Technology, to be held December 8.
- Data Science, AI & Deep Learning Conference – 16 November 2017, London
- Oct 2, 2017.
This conference brings together a range of expert practitioners to explore and discuss the new era of AI, Machine Learning and Deep Learning. Participants gain real insights on how to exploit these technological advances for themselves and their organisations in an increasingly ‘data-driven world’.
- GPU-accelerated, In-database Analytics for Operationalizing AI
- Oct 2, 2017.
This blog explores how the massive parallel processing power of the GPU is able to unify the entire AI pipeline on a single platform, and how this is both necessary and sufficient for overcoming the challenges to operationalizing AI.
- Key Takeaways from AI Conference in San Francisco 2017 – Day 2
- Oct 2, 2017.
Highlights and key takeaways from day 2 of AI Conference San Francisco 2017, including current state review, future trends, and top recommendations for AI initiatives.