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KDnuggets News: 2019 News | Schedule | Archive
Top stories: Most viewed and Most shared
How to submit a blog to KDnuggets
KDnuggets News: 2019 News | Schedule | Archive
Latest:
Dusting Under the Bed: Machine Learners’ Responsibility for the Future of Our Society - Dec 13, 2019.
The Machine Learning community must shape the world so that AI is built and implemented with a focus on the entire outcome for our society, and not just optimized for accuracy and/or profit.
Build Pipelines with Pandas Using pdpipe - Dec 13, 2019.
We show how to build intuitive and useful pipelines with Pandas DataFrame using a wonderful little library called pdpipe.
KDD 2020 Call for Research, Applied Data Science Papers - Dec 12, 2019.
ACM SIGKDD Invites Industry and Academic Experts to Submit Advancements in Data Mining, Knowledge Discovery and Machine Learning for 26th Annual Conference in San Diego.- Plotnine: Python Alternative to ggplot2 - Dec 12, 2019.
Python's plotting libraries such as matplotlib and seaborn does allow the user to create elegant graphics as well, but lack of a standardized syntax for implementing the grammar of graphics compared to the simple, readable and layering approach of ggplot2 in R makes it more difficult to implement in Python. - What just happened in the world of AI? - Dec 12, 2019.
The speed at which AI made advancements and news during 2019 makes it imperative now to step back and place these events into order and perspective. It's important to separate the interest that any one advancement initially attracts, from its actual gravity and its consequential influence on the field. This review unfolds the parallel threads of these AI stories over this year and isolates their significance. - Python Dictionary and Dictionary Methods - Dec 12, 2019.
Check out this introduction to creating, accessing, and updating dictionaries in Python. - Deploying a pretrained GPT-2 model on AWS - Dec 12, 2019.
This post attempts to summarize my recent detour into NLP, describing how I exposed a Huggingface pre-trained Language Model (LM) on an AWS-based web application. 
Top KDnuggets tweets, Dec 04-10: AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020 - Dec 11, 2019.
AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments and Key Trends; Down with technical debt! Clean #Python for #DataScientists; Calculate Similarity - the most relevant Metrics in a Nutshell.
Math for Programmers! - Dec 11, 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.- AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020 - Dec 11, 2019.
We asked leading experts - what are the most important developments of 2019 and 2020 key trends in AI, Analytics, Machine Learning, Data Science, and Deep Learning? This blog focuses mainly on technology and deployment. - Interpretability: Cracking open the black box, Part 2 - Dec 11, 2019.
The second part in a series on leveraging techniques to take a look inside the black box of AI, this guide considers post-hoc interpretation that is useful when the model is not transparent. - NeurIPS 2019 Outstanding Paper Awards - Dec 11, 2019.
NeurIPS 2019 is underway in Vancouver, and the committee has just recently announced this year's Outstanding Paper Awards. Find out what the selections were, along with some additional info on NeurIPS papers, here. - KDnuggets™ News 19:n47, Dec 11: 10 Free Top Notch Machine Learning Courses; AI, Analytics, ML, DS Main Developments and Key Trends - Dec 11, 2019.
We asked top experts: What were the main developments in AI, Data Science, Deep Learning, and Machine Learning Research in 2019, and what key trends do you expect in 2020? Read their answers, and also check 10 Free Top Notch Machine Learning Courses; 4 Hottest Trends in Data Science; The Essential Toolbox for Data Cleaning, and more - Top November Stories: How to Speed up Pandas by 4x with one line of code - Dec 10, 2019.
Also: 10 Free Must-read Books on AI; Data Science for Managers: Programming Languages; The Complete Data Science LinkedIn Profile Guide. - Deployment of Machine learning models using Flask - Dec 10, 2019.
This blog will explain the basics of deploying a machine learning algorithm, focusing on developing a Naïve Bayes model for spam message identification, and using Flask to create an API for that model. - Scalable graph machine learning: a mountain we can climb? - Dec 10, 2019.
Graph machine learning is a developing area of research that brings many complexities. One challenge that both fascinates and infuriates those working with graph algorithms is — scalability. We take a close look at scalability for graph machine learning methods covering what it is, what makes it difficult, and an example of a method that tackles it head-on. - Intro to Grafana: Installation, Configuration, and Building the First Dashboard - Dec 10, 2019.
One of the biggest highlights of Grafana is the ability to bring several data sources together in one dashboard with adding rows that will host individual panels. Let's look at installing, configuring, and creating our first dashboard using Grafana. - 5 Great New Features in Latest Scikit-learn Release - Dec 10, 2019.
From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention. - Moving Predictive Maintenance from Theory to Practice - Dec 9, 2019.
Here are four common hurdles that need to be overcome before tapping into the benefits of predictive maintenance. - The 4 Hottest Trends in Data Science for 2020 - Dec 9, 2019.
The field of Data Science is growing with new capabilities and reach into every industry. With digital transformations occurring in organizations around the world, 2019 included trends of more companies leveraging more data to make better decisions. Check out these next trends in Data Science expected to take off in 2020. - AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020 - Dec 9, 2019.
As we say goodbye to one year and look forward to another, KDnuggets has once again solicited opinions from numerous research & technology experts as to the most important developments of 2019 and their 2020 key trend predictions. - Top Stories, Dec 2-8: How to Speed up Pandas by 4x with one line of code; 10 Free Top Notch Machine Learning Courses - Dec 9, 2019.
Also: Data Science Curriculum Roadmap; Enabling the Deep Learning Revolution; The Essential Toolbox for Data Cleaning; A Non-Technical Reading List for Data Science; The Future of Careers in Data Science & Analysis - DeepMind Unveils MuZero, a New Agent that Mastered Chess, Shogi, Atari and Go Without Knowing the Rules - Dec 9, 2019.
The new model showed great improvements over the previous AlphaZero agent. - Webinar: Natural Language Processing for Digital Transformation of Unstructured Text - Dec 6, 2019.
Learn how pharma and healthcare organizations are using the power of Natural Language Processing (NLP) to transform unstructured text into actionable structured data. - Accuracy Fallacy: The Media’s Coverage of AI Is Bogus - Dec 6, 2019.
Such as the gross exaggerations Stanford researchers broadcasted about their infamous "AI gaydar" project, there exists a prevalent "accuracy fallacy" in relation to AI from the media. Find out more about how the press constantly misleads the public into believing that machine learning can reliably predict psychosis, heart attacks, sexuality, and much more. -
10 Free Top Notch Machine Learning Courses - Dec 6, 2019.
Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills. - 5 Techniques to Prevent Overfitting in Neural Networks - Dec 6, 2019.
In this article, I will present five techniques to prevent overfitting while training neural networks. - Why software engineering processes and tools don’t work for machine learning - Dec 5, 2019.
While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning. - The Essential Toolbox for Data Cleaning - Dec 5, 2019.
Increase your confidence to perform data cleaning with a broader perspective of what datasets typically look like, and follow this toolbox of code snipets to make your data cleaning process faster and more efficient. - Enabling the Deep Learning Revolution - Dec 5, 2019.
Deep learning models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another. Read this post on some of the numerous composite technologies which allow deep learning its complex nonlinearity. - Artificial Friend or Virtual Foe - Dec 5, 2019.
Is AI making more good than harm? - Top KDnuggets tweets, Nov 27 – Dec 03: Data Science Books you should read in 2020 - Dec 4, 2019.
Also: WTF is a Tensor?!?; A Reality Check on #DataScience Hype; Is Data Science dying?; Indeed Fastest-Rising Tech Skills, 2018-2019; Cartoon: #Thanksgiving, Big Data, and Turkey #DataScience… - Explainability: Cracking open the black box, Part 1 - Dec 4, 2019.
What is Explainability in AI and how can we leverage different techniques to open the black box of AI and peek inside? This practical guide offers a review and critique of the various techniques of interpretability. - KDnuggets Poll: How well do current AutoML solutions work? - Dec 4, 2019.
Take part in our latest poll, asking readers their opinions on the effectiveness of current automated machine learning solutions. - The Rise of User-Generated Data Labeling - Dec 4, 2019.
Let’s say your project is humongous and needs data labeling to be done continuously - while you’re on-the-go, sleeping, or eating. I’m sure you’d appreciate User-generated Data Labeling. I’ve got 6 interesting examples to help you understand this, let’s dive right in! - PyTorch in 2019 and where in Europe you can learn about PyTorch in 2020 - Dec 4, 2019.
The Reinforce AI Conference is coming to Budapest again. Join us Apr 6-7 for the conference days, and optionally Apr 8 for workshops. Stefan Otte returns as a speaker, while Francois Chollet joins this time as well. - KDnuggets™ News 19:n46, Dec 4: The Future of Data Science Careers; Which Data Visualization Should I Use? - Dec 4, 2019.
This week: The Future of Careers in Data Science & Analysis; Task-based effectiveness of basic visualizations; Open Source Projects by Google, Uber and Facebook for Data Science and AI; Getting Started with Automated Text Summarization; A Non-Technical Reading List for Data Science; and much more! - Statistical Thinking for Industrial Problem Solving: a free online course - Dec 3, 2019.
This online course is available – for free – to anyone interested in building practical skills in using data to solve problems better. - Popular Deep Learning Courses of 2019 - Dec 3, 2019.
With deep learning and AI on the forefront of the latest applications and demands for new business directions, additional education is paramount for current machine learning engineers and data scientists. These courses are famous among peers, and will help you demonstrate tangible proof of your new skills. - Vega-Lite: A grammar of interactive graphics - Dec 3, 2019.
Vega and Vega-lite follow in a long line of work that can trace its roots back to Wilkinson’s ‘The Grammar of Graphics.’ Since then VegaLite has come into existence, bringing high-level specification of interactive visualisations to the Vega-Lite world. -
Data Science Curriculum Roadmap - Dec 3, 2019.
What follows is a set of broad recommendations, and it will inevitably require a lot of adjustments in each implementation. Given that caveat, here are our curriculum recommendations. - A Non-Technical Reading List for Data Science - Dec 2, 2019.
The world still cannot be reduced to numbers on a page because human beings are still the ones making all the decisions. So, the best data scientists understand the numbers and the people. Check out these great data science books that will make you a better data scientist without delving into the technical details. - Top 7 Data Science Use Cases in Trust and Security - Dec 2, 2019.
What are trust and safety? What is the role of trust and security in the modern world? Read this overview of 7 data science application use cases in the realm of trust and security. - Top Stories, Nov 25 – Dec 1: How to Speed up Pandas by 4x with one line of code; Open Source Projects by Google, Uber and Facebook for Data Science and AI - Dec 2, 2019.
Also: Getting Started with Automated Text Summarization; A Doomed Marriage of Machine Learning and Agile; The Future of Careers in Data Science & Analysis; The Future of Careers in Data Science & Analysis; Can Neural Networks Develop Attention? Google Thinks they Can - Google Open Sources MobileNetV3 with New Ideas to Improve Mobile Computer Vision Models - Dec 2, 2019.
The latest release of MobileNets incorporates AutoML and other novel ideas in mobile deep learning. - Two Years In The Life of AI, Machine Learning, Deep Learning and Java - Nov 29, 2019.
Where does Java stand in the world of artificial intelligence, machine learning, and deep learning? Learn more about how to do these things in Java, and the libraries and frameworks to use. - Markov Chains: How to Train Text Generation to Write Like George R. R. Martin - Nov 29, 2019.
Read this article on training Markov chains to generate George R. R. Martin style text. - Lit BERT: NLP Transfer Learning In 3 Steps - Nov 29, 2019.
PyTorch Lightning is a lightweight framework which allows anyone using PyTorch to scale deep learning code easily while making it reproducible. In this tutorial we’ll use Huggingface's implementation of BERT to do a finetuning task in Lightning. -
Open Source Projects by Google, Uber and Facebook for Data Science and AI - Nov 28, 2019.
Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public. - Cartoon: Thanksgiving, Big Data, and Turkey Data Science… - Nov 28, 2019.
A classic KDnuggets Thanksgiving cartoon examines the predicament of one group of fowl Data Scientists. - A Doomed Marriage of Machine Learning and Agile - Nov 28, 2019.
Sebastian Thrun, the founder of Udacity, ruined my machine learning project and wedding. -
Getting Started with Automated Text Summarization - Nov 28, 2019.
This article will walk through an extractive text summarization process, using a simple word frequency approach, implemented in Python. - Top KDnuggets tweets, Nov 20-26: How to Speed up Pandas by 4x with one line of code - Nov 27, 2019.
Also: Deep Learning for Image Classification with Less Data; How to Speed up Pandas by 4x with one line of code; 25 Useful #Python Snippets to Help in Your Day-to-Day Work; Automated Machine Learning Project Implementation Complexities - The Future of Careers in Data Science & Analysis - Nov 27, 2019.
As the fields of data science and analysis continue to expand, the next crop of bright minds is always needed. Learn more about the nuances of these jobs and find where you can fit in for a rewarding and interesting career. - Spark NLP 101: LightPipeline - Nov 27, 2019.
A Pipeline is specified as a sequence of stages, and each stage is either a Transformer or an Estimator. These stages are run in order, and the input DataFrame is transformed as it passes through each stage. Now let’s see how this can be done in Spark NLP using Annotators and Transformers. - Task-based effectiveness of basic visualizations - Nov 27, 2019.
This is a summary of a recent paper on an age-old topic: what visualisation should I use? No prizes for guessing “it depends!” Is this the paper to finally settle the age-old debate surrounding pie-charts?? - KDnuggets™ News 19:n45, Nov 27: Interpretable vs black box models; Advice for New and Junior Data Scientists - Nov 27, 2019.
This week: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead; Advice for New and Junior Data Scientists; Python Tuples and Tuple Methods; Can Neural Networks Develop Attention? Google Thinks they Can; Three Methods of Data Pre-Processing for Text Classification 
AXA, State Auto and Hippo on how to fuse innovative tech to your company - Nov 26, 2019.
Join this live webinar: Fast, Agile, Service-Driven Insurance: Fuse Innovative Tech to Your Company DNA - AI, Chatbots, Automation and More, Dec 11 at 10:00am EST, to get actionable insight to develop your strategy.- Machine Learning 101: The What, Why, and How of Weighting - Nov 26, 2019.
Weighting is a technique for improving models. In this article, learn more about what weighting is, why you should (and shouldn’t) use it, and how to choose optimal weights to minimize business costs. - Content-based Recommender Using Natural Language Processing (NLP) - Nov 26, 2019.
A guide to build a content-based movie recommender model based on NLP. - Probability Learning: Naive Bayes - Nov 26, 2019.
This post will describe various simplifications of Bayes' Theorem, that make it more practical and applicable to real world problems: these simplifications are known by the name of Naive Bayes. Also, to clarify everything we will see a very illustrative example of how Naive Bayes can be applied for classification. - Would you buy insights from this guy? (How to assess and manage a Data Science vendor) - Nov 25, 2019.
With all the hype from data science vendors selling "actionable insights" to boost your company's bottom line, selecting your analytics partner should proceed through the same, careful process as any traditional business endeavor. Follow these questions and best practices to ensure you manage accordingly. - Top 8 Data Science Use Cases in Marketing - Nov 25, 2019.
In this article, we want to highlight some key data science use cases in marketing. Let us concentrate on several instances that present particular interest and managed to prove their efficiency in the course of time. - Top Stories, Nov 18-24: How to Speed up Pandas by 4x with one line of code; Python, Selenium & Google for Geocoding Automation: Free and Paid - Nov 25, 2019.
Also: Automated Machine Learning Project Implementation Complexities; Text Encoding: A Review; The Notebook Anti-Pattern; Data Science for Managers: Programming Languages; 10 Free Must-read Books on AI - Can Neural Networks Develop Attention? Google Thinks they Can - Nov 25, 2019.
Google recently published some work about modeling attention mechanisms in deep neural networks. - Advice for New and Junior Data Scientists - Nov 22, 2019.
If you are a new Data Scientist early in your professional journey, and you’re a bit confused and lost, then follow this advice to figure out how to best contribute to your company. - Automated Machine Learning Project Implementation Complexities - Nov 22, 2019.
To demonstrate the implementation complexity differences along the AutoML highway, let's have a look at how 3 specific software projects approach the implementation of just such an AutoML "solution," namely Keras Tuner, AutoKeras, and automl-gs. - Text Encoding: A Review - Nov 22, 2019.
We will focus here exactly on that part of the analysis that transforms words into numbers and texts into number vectors: text encoding. - Three Methods of Data Pre-Processing for Text Classification - Nov 21, 2019.
This blog shows how text data representations can be used to build a classifier to predict a developer’s deep learning framework of choice based on the code that they wrote, via examples of TensorFlow and PyTorch projects. - Python, Selenium & Google for Geocoding Automation: Free and Paid - Nov 21, 2019.
This tutorial will take you through two options that have automated the geocoding process for the user using Python, Selenium and Google Geocoding API. - Neural Networks 201: All About Autoencoders - Nov 21, 2019.
Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problems, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation. - Top KDnuggets tweets, Nov 13-19: A whole lot of Data Science Cheatsheets - Nov 21, 2019.
Also: Bring the scientific rigor of reproducibility to your Data Science projects; Neutrinos Lead to Unexpected Discovery in Basic Math ; The media gets really excited about AI. Maybe a bit too excited - The Notebook Anti-Pattern - Nov 21, 2019.
This article aims to explain why this drive towards the use of notebooks in production is an anti pattern, giving some suggestions along the way. - Python Tuples and Tuple Methods - Nov 21, 2019.
Brush up on your Python basics with this post on creating, using, and manipulating tuples. - The Semiconductor Imperative for Driving Meaningful Innovation - Nov 20, 2019.
The fundamental fact is that more information than ever will need to be analyzed on millions of devices. And that’s where 5G will make accessing data dramatically faster and more efficient. At Samsung, we’re excited about what 5G can truly enable and to be a central player in the new 5G world. - Pro Tips: How to deal with Class Imbalance and Missing Labels - Nov 20, 2019.
Your spectacularly-performing machine learning model could be subject to the common culprits of class imbalance and missing labels. Learn how to handle these challenges with techniques that remain open areas of new research for addressing real-world machine learning problems. - Deep Learning for Image Classification with Less Data - Nov 20, 2019.
In this blog I will be demonstrating how deep learning can be applied even if we don’t have enough data. - Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead - Nov 20, 2019.
The two main takeaways from this paper: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models. - KDnuggets™ News 19:n44, Nov 20: How I Got Better at Machine Learning; Tips for a cost-effective ML project - Nov 20, 2019.
Read tips and tricks that helped one Data Scientist to get better at Machine Learning; Learn how to make ML project cost-effective; Consider submitting a blog to KDnuggets - you can be profiled here; and study how to manipulate Python lists. - Why write for KDnuggets? Calling for original blogs and new authors - Nov 19, 2019.
KDnuggets is calling for original blogs and contributions from new authors on AI, Data Science, Machine Learning, and related topics. The authors of most popular such blogs in December will be profiled in KDnuggets. - How to apply machine learning and deep learning methods to audio analysis - Nov 19, 2019.
Find out how data scientists and AI practitioners can use a machine learning experimentation platform like Comet.ml to apply machine learning and deep learning to methods in the domain of audio analysis. - Reproducibility, Replicability, and Data Science - Nov 19, 2019.
As cornerstones of scientific processes, reproducibility and replicability ensure results can be verified and trusted. These two concepts are also crucial in data science, and as a data scientist, you must follow the same rigor and standards in your projects. - The Math Behind Bayes - Nov 19, 2019.
This post will be dedicated to explaining the maths behind Bayes Theorem, when its application makes sense, and its differences with Maximum Likelihood. - Data Science for Managers: Programming Languages - Nov 19, 2019.
In this article, we are going to talk about popular languages for Data Science and briefly describe each of them. - Top Stories, Nov 11-17: How to Speed up Pandas by 4x with one line of code - Nov 18, 2019.
Also: The Complete Data Science LinkedIn Profile Guide; How Data Analytics Can Assist in Fraud Detection; Research Guide for Depth Estimation with Deep Learning; 10 Free Must-read Books on AI; Beginners Guide to the Three Types of Machine Learning - Live Webinar: Continual Learning with Human-in-the-loop - Nov 18, 2019.
Join this live webinar from cnvrg, Continual Learning with Human-in-the-loop, Nov 26 @ 12 PM EST, and learn the role of human-in-the-loop in your ML pipeline, how to close the loop in your pipeline, and much more. - Generalization in Neural Networks - Nov 18, 2019.
When training a neural network in deep learning, its performance on processing new data is key. Improving the model's ability to generalize relies on preventing overfitting using these important methods. - The Reinforcement-Learning Methods that Allow AlphaStar to Outcompete Almost All Human Players at StarCraft II - Nov 18, 2019.
The new AlphaStar achieved Grandmaster level at StarCraft II overcoming some of the limitations of the previous version. How did it do it? - GitHub Repo Raider and the Automation of Machine Learning - Nov 18, 2019.
Since X never, ever marks the spot, this article raids the GitHub repos in search of quality automated machine learning resources. Read on for projects and papers to help understand and implement AutoML. - On the sensationalism of artificial intelligence news - Nov 15, 2019.
With artificial intelligence and machine learning now a mainstay of our daily awareness, news organizations have been seen to overstate the reality behind progress in the field. Learn more about recent examples of media hyperbole and explore why this may be happening.
