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Top stories: Most viewed and Most shared
KDnuggets News: 2019 News | Schedule | Submissions | Archive
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Gender Diversity in AI Research - Aug 21, 2019.
Through an analysis of 1.5M papers from arXiv, this study reviews the evolution of gender diversity across disciplines, countries, and institutions as well as the semantic differences between AI papers with and without female co-authors.
Understanding Decision Trees for Classification in Python - Aug 21, 2019.
This tutorial covers decision trees for classification also known as classification trees, including the anatomy of classification trees, how classification trees make predictions, using scikit-learn to make classification trees, and hyperparameter tuning.- Automate Stacking In Python: How to Boost Your Performance While Saving Time - Aug 21, 2019.
Utilizing stacking (stacked generalizations) is a very hot topic when it comes to pushing your machine learning algorithm to new heights. For instance, most if not all winning Kaggle submissions nowadays make use of some form of stacking or a variation of it. - KDnuggets™ News 19:n31, Aug 21: Become a Marketable Data Scientist; Data Science Command Line Basics; Chatbots with Keras - Aug 21, 2019.
This week's news: Become More Marketable as a Data Scientist; Command Line Basics Every Data Scientist Should Know; Chatbots with Keras!; Understanding Cancer using Machine Learning; Statistical Modelling vs Machine Learning; Is Kaggle Learn a "Faster Data Science Education?"; and much more! 
Artificial Intelligence Is Not Intelligence – Interview With Andy Cotgreave (Keynote Speaker at Crunch Conf) - Aug 20, 2019.
Crunch is coming to Budapest, Hungary on 16-18 Oct. Use code KDNuggets to save on Data Science, Data Engineering, or BI tracks. But first, read this interview with keynote speaker Andy Cotgreave.- Detecting stationarity in time series data - Aug 20, 2019.
Explore how to determine if your time series data is generated by a stationary process and how to handle the necessary assumptions and potential interpretations of your result. - Is Kaggle Learn a “Faster Data Science Education?” - Aug 20, 2019.
Kaggle Learn is "Faster Data Science Education," featuring micro-courses covering an array of data skills for immediate application. Courses may be made with newcomers in mind, but the platform and its content is proving useful as a review for more seasoned practitioners as well. 
Syracuse University: Four Assistant Professor (Tenure Track) Faculty Positions – Information Studies [Syracuse, NY] - Aug 20, 2019.
Seeking scholars and leaders to fill four Assistant Professor (tenure track) faculty positions to start in Fall 2020. Exceptional candidates may be considered at the rank of Associate or Full Professor.- An Overview of Python’s Datatable package - Aug 20, 2019.
Modern machine learning applications need to process a humongous amount of data and generate multiple features. Python’s datatable module was created to address this issue. It is a toolkit for performing big data (up to 100GB) operations on a single-node machine, at the maximum possible speed. 
Math for Programmers - Aug 19, 2019.
Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer.- Crafting an Elevator Pitch for your Data Science Startup - Aug 19, 2019.
If you are launching a data science startup, these tips will give you a head start as you seek capital for seed funding or your next level of growth. - Lincoln Clean Energy: Director, Asset Performance [Austin, TX] - Aug 19, 2019.
Seeking an Asset Performance Director, a role which requires an individual that possesses a strong technical skill set and the ability to communicate findings effectively throughout the organization. - Deep Learning for NLP: Creating a Chatbot with Keras! - Aug 19, 2019.
Learn how to use Keras to build a Recurrent Neural Network and create a Chatbot! Who doesn’t like a friendly-robotic personal assistant? 
Top Stories, Aug 12-18: How to Become More Marketable as a Data Scientist; 12 NLP Researchers, Practitioners & Innovators You Should Be Following - Aug 19, 2019.
Also: How to Become More Marketable as a Data Scientist; 6 Key Concepts in Andrew Ng’s “Machine Learning Yearning”; Understanding Cancer using Machine Learning; Command Line Basics Every Data Scientist Should Know- Manual Coding or Automated Data Integration – What’s the Best Way to Integrate Your Enterprise Data? - Aug 19, 2019.
What’s the best way to execute your data integration tasks: writing manual code or using ETL tool? Find out the approach that best fits your organization’s needs and the factors that influence it. -
How to Become More Marketable as a Data Scientist - Aug 16, 2019.
As a data scientist, you are in high demand. So, how can you increase your marketability even more? Check out these current trends in skills most desired by employers in 2019. - Understanding Cancer using Machine Learning - Aug 16, 2019.
Use of Machine Learning (ML) in Medicine is becoming more and more important. One application example can be Cancer Detection and Analysis. - Pytorch Lightning vs PyTorch Ignite vs Fast.ai - Aug 16, 2019.
Here, I will attempt an objective comparison between all three frameworks. This comparison comes from laying out similarities and differences objectively found in tutorials and documentation of all three frameworks. - U. of Miami: Faculty Positions, with expertise in AI/Data Science/ML or related areas [Miami, FL] - Aug 15, 2019.
The positions require research and teaching expertise in AI/Data Science, or related areas including Data Extraction, Data Visualization, Machine Learning, and Intelligent Actuators. - Data Driven Government – Speakers Highlights - Aug 15, 2019.
The lineup of experienced, thought-leading speakers at Data Driven Government, Sep 25 in Washington, DC, will explain how to use data and analytics to more effectively accomplish your mission, increase efficiency, and improve evidence-based policymaking. - How Concerned Should You be About Predictor Collinearity? It Depends… - Aug 15, 2019.
Predictor collinearity (also known as multicollinearity) can be problematic for your regression models. Check out these rules of thumb about when, and when not, to be concerned. - Command Line Basics Every Data Scientist Should Know - Aug 15, 2019.
Check out this introductory guide to completing simple tasks with the command line. - Introducing the Plato Research Dialogue System: Building Conversational Applications at Uber’s Scale - Aug 15, 2019.
While the process of building simple, domain-specific chatbots has gotten way easier, building large scale, multi-agent conversational applications remains a massive challenge. Recently, the Uber engineering team open sourced the Plato Research Dialogue System, which is the framework powering conversational agents across Uber’s different applications. - Top KDnuggets tweets, Aug 07-13: Deep Learning Cheat Sheets; 12 NLP Researchers, Practitioners To Follow - Aug 14, 2019.
Deep Learning Cheat Sheets; 12 NLP Researchers, Practitioners & Innovators You Should Be Following; Knowing Your Neighbours: Machine Learning on Graphs. - Top July Stories: The Death of Big Data and the Emergence of the Multi-Cloud Era - Aug 14, 2019.
Also: Top 13 Skills To Become a Rockstar Data Scientist, Top 10 Data Science Leaders You Should Follow; What's wrong with the approach to Data Science? - Domain-Specific Language Processing Mines Value From Unstructured Data - Aug 14, 2019.
Processing unstructured text data in real-time is challenging when applying NLP or NLU. Find out how Domain-Specific Language Processing can also help mine valuable information from data by following your guidance and using the language of your business. - Statistical Modelling vs Machine Learning - Aug 14, 2019.
At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem. - What is Poisson Distribution? - Aug 14, 2019.
An solid overview of the Poisson distribution, starting from why it is needed, how it stacks up to binomial distribution, deriving its formula mathematically, and more. - KDnuggets™ News 19:n30, Aug 14: Know Your Neighbor: Machine Learning on Graphs; 12 NLP Researchers, Practitioners You Should Follow - Aug 14, 2019.
Machine Learning on Graphs; 12 amazing leaders in NLP; Deep Learning for NLP explained, including ANNs, RNNs and LSTMs; Benford's Law and why is it important for data science; Key concepts in Andrew Ng "Machine Learning Yearning". - PhD student position in computational science with focus on chemistry [Umeå, Sweden] - Aug 13, 2019.
Umea University, Sweden is seeking a PhD-student in computational science with focus on chemistry. The position is for 4 years of research including courses on graduate level. - Postdoctoral position (2 years) in multivariate analysis and deep learning [Umeå, Sweden] - Aug 13, 2019.
Help develop new e-science methods that fundamentally integrates Deep Learning and Multivariate analysis. The postdoc position is full-time for a period of two years - The slow, startling triumph of Reverend Bayes – John Elder’s 2019 Keynote at PAW in London - Aug 13, 2019.
The core Bayesian idea, when learning from data, is to inject information — however slight — from outside the data. In real-world applications, meta-information is clearly needed. John Elder's Predictive Analytics World keynote covers this and more. PAW London takes place 16-17 Oct. - The Easy Way to Do Advanced Data Visualisation for Data Scientists - Aug 13, 2019.
Creating effective data visualisations is a core skill for data scientists. This tutorial will guide you through how to easily develop interactive visualisations using the Python library plotly. - How Creating an AI Study Group Boosted My Skills and Got Me a Job - Aug 13, 2019.
The amount of time I had to put in to organize the AI Society left me sometimes sleep-deprived but it was definitely worth it. It was also one of the main factors why I got the job in Machine Learning after all. I hope that this article will inspire you to create your own AI study group! - Learn how to use PySpark in under 5 minutes (Installation + Tutorial) - Aug 13, 2019.
Apache Spark is one of the hottest and largest open source project in data processing framework with rich high-level APIs for the programming languages like Scala, Python, Java and R. It realizes the potential of bringing together both Big Data and machine learning. - Cambridge Analytica whistleblower Chris Wylie to headline Big Data LDN 2019 keynote programme - Aug 12, 2019.
Chris Wylie, the whistleblower who exposed Cambridge Analytica, will headline Big Data LDN 2019 programme, along with over 100 speakers at this free to attend event, Nov 13-14, London. - 6 Key Concepts in Andrew Ng’s “Machine Learning Yearning” - Aug 12, 2019.
If you are diving into AI and machine learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers. - A 2019 Guide to Semantic Segmentation - Aug 12, 2019.
Semantic segmentation refers to the process of linking each pixel in an image to a class label. These labels could include a person, car, flower, piece of furniture, etc., just to mention a few. We’ll now look at a number of research papers on covering state-of-the-art approaches to building semantic segmentation models. - Top Stories, Aug 5-11: Knowing Your Neighbours: Machine Learning on Graphs; What is Benford’s Law and why is it important for data science? - Aug 12, 2019.
Also: Deep Learning for NLP: ANNs, RNNs and LSTMs explained!; Machine Learning is Happening Now: A Survey of Organizational Adoption, Implementation, and Investment; 25 Tricks for Pandas; Getting Started with Data Science; Data Science: Scientific Discipline or Business Process? - 12 NLP Researchers, Practitioners & Innovators You Should Be Following - Aug 12, 2019.
Check out this list of NLP researchers, practitioners and innovators you should be following, including academics, practitioners, developers, entrepreneurs, and more. - Monash University: Research Fellow – Computer Vision [Melbourne, Australia] - Aug 9, 2019.
The position requires a passion for research, a proven research track record in computer vision, an ability to work independently as well as lead a team, and a willingness to work on inter-disciplinary research projects and seek external funding. The successful candidate will align with the group goal on building a world-class computer vision team. - Keras Callbacks Explained In Three Minutes - Aug 9, 2019.
A gentle introduction to callbacks in Keras. Learn about EarlyStopping, ModelCheckpoint, and other callback functions with code examples. - Introduction to Image Segmentation with K-Means clustering - Aug 9, 2019.
Image segmentation is the classification of an image into different groups. Many kinds of research have been done in the area of image segmentation using clustering. In this article, we will explore using the K-Means clustering algorithm to read an image and cluster different regions of the image. - 9 Tips For Training Lightning-Fast Neural Networks In Pytorch - Aug 9, 2019.
Who is this guide for? Anyone working on non-trivial deep learning models in Pytorch such as industrial researchers, Ph.D. students, academics, etc. The models we're talking about here might be taking you multiple days to train or even weeks or months. - Knowing Your Neighbours: Machine Learning on Graphs - Aug 8, 2019.
Graph Machine Learning uses the network structure of the underlying data to improve predictive outcomes. Learn how to use this modern machine learning method to solve challenges with connected data. - Inside Pluribus: Facebook’s New AI That Just Mastered the World’s Most Difficult Poker Game - Aug 8, 2019.
The reasons why Pluribus represents a major breakthrough in AI systems might result confusing to many readers. After all, in recent years AI researchers have made tremendous progress across different complex games. However, six-player, no-limit Texas Hold’em still remains one of the most elusive challenges for AI systems. - Data Science: Scientific Discipline or Business Process? - Aug 8, 2019.
Simply put, data science is an attempt to understand given data using the scientific method. That's why data science is a scientific discipline. You are free (and encouraged!) to apply data science to business use cases, just as you are encouraged to apply it to many other domains. - Top KDnuggets tweets, Jul 31 – Aug 06: NLP vs. NLU: from Understanding a Language to Its Processing - Aug 7, 2019.
Also: Ten more random useful things in R you may not know about; 5 Probability Distributions Every Data Scientist Should Know; Machine Learning is Happening Now: A Survey of Organizational Adoption, Implementation, and Investment; Programmers rejoice! Deep TabNine offer code autocompletion with #deeplearning - Exploratory Data Analysis Using Python - Aug 7, 2019.
In this tutorial, you’ll use Python and Pandas to explore a dataset and create visual distributions, identify and eliminate outliers, and uncover correlations between two datasets. -
What is Benford’s Law and why is it important for data science? - Aug 7, 2019.
Benford’s law is a little-known gem for data analytics. Learn about how this can be used for anomaly or fraud detection in scientific or technical publications. - Deep Learning for NLP: ANNs, RNNs and LSTMs explained! - Aug 7, 2019.
Learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and LSTMs like never before and use NLP to build a Chatbot! - Coding Random Forests in 100 lines of code* - Aug 7, 2019.
There are dozens of machine learning algorithms out there. It is impossible to learn all their mechanics; however, many algorithms sprout from the most established algorithms, e.g. ordinary least squares, gradient boosting, support vector machines, tree-based algorithms and neural networks. - KDnuggets™ News 19:n29, Aug 7: What 70% of Data Science Learners Do Wrong; Pytorch Cheat Sheet for Beginners - Aug 7, 2019.
This week on KDnuggets: What 70% of Data Science Learners Do Wrong; Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree; How a simple mix of object-oriented programming can sharpen your deep learning prototype; Can we trust AutoML to go on full autopilot?; Ten more random useful things in R you may not know about; 25 Tricks for Pandas; and much more! - Waste Management: Data Scientist [Houston, TX] - Aug 6, 2019.
Waste Management is seeking a Data Scientist in Houston, TX, to support their digital marketing, customer and other business segment teams with insights gained from analyzing customer data. - Feature selection by random search in Python - Aug 6, 2019.
Feature selection is one of the most important tasks in machine learning. Learn how to use a simple random search in Python to get good results in less time. - 25 Tricks for Pandas - Aug 6, 2019.
Check out this video (and Jupyter notebook) which outlines a number of Pandas tricks for working with and manipulating data, covering topics such as string manipulations, splitting and filtering DataFrames, combining and aggregating data, and more. - Lagrange multipliers with visualizations and code - Aug 6, 2019.
In this story, we’re going to take an aerial tour of optimization with Lagrange multipliers. When do we need them? Whenever we have an optimization problem with constraints. 
How to better manage your data science team’s workflow - Aug 5, 2019.
This workshop, Aug 14 @ 12 PM ET, will give you the proper tools and tactics to manage the entire lifecycle of your machine learning projects, from research to exploration to development and production.- Top Stories, Jul 29 – Aug 4: Top 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning; What 70% of Data Science Learners Do Wrong - Aug 5, 2019.
Also: GPU Accelerated Data Analytics & Machine Learning; Understanding Tensor Processing Units; Top 13 Skills To Become a Rockstar Data Scientist; Five Command Line Tools for Data Science; Ten more random useful things in R you may not know about - [video] Introduction to Generative Adversarial Networks (for beginners and advanced Data Scientists) - Aug 5, 2019.
Generative Adversarial Networks are driving important new technologies in deep learning methods. With so much to learn, these two videos will help you jump into your exploration with GANs and the mathematics behind the modelling. - Machine Learning is Happening Now: A Survey of Organizational Adoption, Implementation, and Investment - Aug 5, 2019.
This is an excerpt from a survey which sought to evaluate the relevance of machine learning in operations today, assess the current state of machine learning adoption and to identify tools used for machine learning. A link to the full report is inside. - Getting Started With Data Science - Aug 5, 2019.
Over the past many months, I’ve received hundreds of messages from people asking me how they could get started with Data Science. Therefore, I thought it would be useful to write down a framework for those wanting to get started with Data Science. - South Dakota State University: Data Visualization Developer and Analyst [Brookings, SD] - Aug 2, 2019.
South Dakota State University is seeking a Data Visualization Developer and Analyst in Brookings, SD, to create business intelligence tools and reports to support the use of a campus-wide business intelligence and decision support system, compile multiple visualizations into intuitive dashboards for campus-wide use, and more. 
Statistical Thinking for Industrial Problem Solving (STIPS) – a free online course. - Aug 2, 2019.
This online course is available – for free – to anyone interested in building practical skills in using data to solve problems better.- Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree - Aug 2, 2019.
This cheatsheet should be easier to digest than the official documentation and should be a transitional tool to get students and beginners to get started reading documentations soon. - GPU Accelerated Data Analytics & Machine Learning - Aug 2, 2019.
The future is here! Speed up your Machine Learning workflow using Python RAPIDS libraries support. - What 70% of Data Science Learners Do Wrong - Aug 2, 2019.
Lessons learned from repeatedly smashing my head with a 2-meter long metal pole for a college engineering course. - Easily Deploy Deep Learning Models in Production - Aug 1, 2019.
Getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail. This blog explores how to navigate these challenges. - Opening Black Boxes: How to leverage Explainable Machine Learning - Aug 1, 2019.
A machine learning model that predicts some outcome provides value. One that explains why it made the prediction creates even more value for your stakeholders. Learn how Interpretable and Explainable ML technologies can help while developing your model. - How a simple mix of object-oriented programming can sharpen your deep learning prototype - Aug 1, 2019.
By mixing simple concepts of object-oriented programming, like functionalization and class inheritance, you can add immense value to a deep learning prototyping code. - A 2019 Guide to Object Detection - Aug 1, 2019.
Object detection has been applied widely in video surveillance, self-driving cars, and object/people tracking. In this piece, we’ll look at the basics of object detection and review some of the most commonly-used algorithms and a few brand new approaches, as well. - Top KDnuggets tweets, Jul 24-30: Nothing but NumPy: Understanding and Creating Neural Nets w. Computational Graphs from Scratch; How Netflix works - Jul 31, 2019.
How Netflix works: the (hugely simplified) complex stuff that happens every time; Top Certificates and Certifications in Analytics, Data Science, ML; Nothing but NumPy: Understanding &Creating Neural Networks with Computation. - Are We Ready to Partner With Machines?
Data Science Salon Miami, September 10-11 - Jul 31, 2019.
When it comes to AI, there’s plenty of talk of the future of machines. But it’s the people behind AI development who have the insights needed to shape that future. Register now to catch all of our speakers at the Data Science Salon Miami, Sep 10-11, 2019. - Can we trust AutoML to go on full autopilot? - Jul 31, 2019.
We put an AutoML tool to the test on a real-world problem, and the results are surprising. Even with automatic machine learning, you still need expert data scientists. - Five Command Line Tools for Data Science - Jul 31, 2019.
You can do more data science than you think from the terminal. - Ten more random useful things in R you may not know about - Jul 31, 2019.
I had a feeling that R has developed as a language to such a degree that many of us are using it now in completely different ways. This means that there are likely to be numerous tricks, packages, functions, etc that each of us use, but that others are completely unaware of, and would find useful if they knew about them. - KDnuggets™ News 19:n28, Jul 31: Top 13 Skills To Become a Rockstar Data Scientist; Best Podcasts on AI, Analytics, Data Science - Jul 31, 2019.
Learn the essential skills needed to become a Data Science rockstar; Understand CNNs with Python + Tensorflow + Keras tutorial; Discover the best podcasts about AI, Analytics, Data Science; and find out where you can get the best Certificates in the field - A Data Science Playbook for explainable ML/xAI - Jul 30, 2019.
This technical webinar on Aug 14 discusses traditional and modern approaches for interpreting black box models. Additionally, we will review cutting edge research coming out of UCSF, CMU, and industry. - Understanding Tensor Processing Units - Jul 30, 2019.
The Tensor Processing Unit (TPU) is Google's custom tool to accelerate machine learning workloads using the TensorFlow framework. Learn more about what TPUs do and how they can work for you. - P-values Explained By Data Scientist - Jul 30, 2019.
This article is designed to give you a full picture from constructing a hypothesis testing to understanding p-value and using that to guide our decision making process. - Here’s how you can accelerate your Data Science on GPU - Jul 30, 2019.
Data Scientists need computing power. Whether you’re processing a big dataset with Pandas or running some computation on a massive matrix with Numpy, you’ll need a powerful machine to get the job done in a reasonable amount of time. - Monash University: Lecturer / Sr Lecturer – Blockchain [Melbourne, Australia] - Jul 29, 2019.
Seeking a Lecturer / Sr Lecturer for the Monash Blockchain Technology Centre (Monash BTC): a visionary enterprise that will bring together world-leading expertise from across Monash to explore, develop and innovate the technology of blockchain, in collaboration with various industry and societal sectors. - Monash University: Lecturer / Sr Lecturer – Cryptography [Melbourne, Australia] - Jul 29, 2019.
Seeking a Lecturer / Sr Lecturer in Cryptography. The Monash cryptography research team mainly focuses on the following areas: Post-quantum cryptography; Cloud and database security; Zero-knowledge; Privacy-enhanced technologies. - Exploring Python Basics. - Jul 29, 2019.
This free ebook is a great resource for data science beginners, providing a good introduction into Python, coding with Raspberry Pi, and using Python to building predictive models. - Monash University: Lecturer / Sr Lecturer – Cybersecurity [Melbourne, Australia] - Jul 29, 2019.
Seeking a Lecturer / Sr Lecturer in Cybersecurity. The Monash cybersecurity research team mainly focuses on 3 key streams of areas; namely: cryptography, blockchain, and interdisciplinary cybersecurity applications. -
Top 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning - Jul 29, 2019.
Check out our latest Top 10 Most Popular Data Science and Machine Learning podcasts available on iTunes. Stay up to date in the field with these recent episodes and join in with the current data conversations. - 7 Tips for Dealing With Small Data - Jul 29, 2019.
At my workplace, we produce a lot of functional prototypes for our clients. Because of this, I often need to make Small Data go a long way. In this article, I’ll share 7 tips to improve your results when prototyping with small datasets. - Decentralized and Collaborative AI: How Microsoft Research is Using Blockchains to Build More Transparent Machine Learning Models - Jul 29, 2019.
Recently, AI researchers from Microsoft open sourced the Decentralized & Collaborative AI on Blockchain project that enables the implementation of decentralized machine learning models based on blockchain technologies. - Top Stories, Jul 22-28: Top 13 Skills To Become a Rockstar Data Scientist; This New Google Technique Help Us Understand How Neural Networks are Thinking - Jul 29, 2019.
Also: Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras; Fantastic Four of Data Science Project Preparation; The Death of Big Data and the Emergence of the Multi-Cloud Era; The title CDO started out as a joke -
Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras - Jul 26, 2019.
Different neural network architectures excel in different tasks. This particular article focuses on crafting convolutional neural networks in Python using TensorFlow and Keras.
