Join Databricks Mar 7, 2019, to learn how using MLflow can help you keep track of experiment runs and results across frameworks, execute projects remotely on to a Databricks cluster, and quickly reproduce your runs, and more. Sign up for this webinar now.
In some domains, new values appear all the time, so it's crucial to handle them in a good way. Using deep learning, one can learn a special Out-of-Vocabulary embedding for these new values. But how can you train this embedding to generalize well to any unseen value? We explain one of the methods employed at Taboola.
To satisfy all the needs of the growing number of consumers and process enormous data chunks, data science algorithms are vital. Let’s consider several of widespread and efficient data science use cases in the travel industry.
TensorFlow.js brings TensorFlow and Keras to the the JavaScript ecosystem, supporting both Node.js and browser-based applications. Read a summary of the paper which describes the design, API, and implementation of TensorFlow.js.
Also: Python Data Science for Beginners; A comprehensive survey on graph neural networks; Convolutional Neural Networks — Simplified #NeuralNetworks; What are Some “Advanced” AI and Machine Learning Online Courses?; Artificial Neural Network Implementation using NumPy
Python 2 ends on Jan 1, 2020. Migrating from Python 2 to 3 can be a scary process, so get this solution sheet with different options for moving your existing packages and applications from Python 2 to 3, along with best practice guidelines.
Join us as we bring you the leading innovations and insights to the fast-paced world of AI & Data from Machine Learning, Healthcare, Marketing, Gaming analytics.
We discuss groundbreaking and unique methods to acquire labeled data at low cost, including 3rd-Party Plug-and-Play AI Model, Zero-Shot Learning, and Restructuring the Existing Data Set.
The many standard tasks in computer vision all require special consideration: classification, detection, segmentation, pose estimation, enhancement and restoration, and action recognition. Let me show you how to do everything in Computer Vision with Deep Learning!
Google’s BERT algorithm has emerged as a sort of “one model to rule them all.” BERT builds on two key ideas that have been responsible for many of the recent advances in NLP: (1) the transformer architecture and (2) unsupervised pre-training.
Also: Running R and Python in Jupyter; What are Some “Advanced” AI and Machine Learning Online Courses?; Python Data Science for Beginners; 6 Books About Open Data Every Data Scientist Should Read
ODSC East is the top conference for data science practitioners and AI engineers: 300+ talks, full and half-day expert-led trainings, and shorter hands-on workshops. KDnuggets subscribers save 45% with code KDN45. Register now!
Your current ML workflow probably chains together several functions executed linearly. Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG.
Real world data is messy and needs to be cleaned before it can be used for analysis. Industry experts say the data preprocessing step can easily take 70% to 80% of a data scientist's time on a project.
Io-Tahoe technology can track changes to the sensitive data landscape over time to understand how the PII and the sensitive data footprint is changing, enabling firms to continually keep track of their data on an ongoing basis.
We outline the importance of asking yourself the questions you need to ask to effectively produce something that the business wants. Once you start asking questions, it’ll become second nature and you’ll immediately see the value and find yourself asking even more questions as you gain more experience.
Where did you apply Analytics, Machine Learning, and Data Science in 2018? Take part in the latest KDnuggets poll to share your input, and see what others have to say.
Traditional tools force analysts to play the import-and-export game, so it's difficult to keep data fresh and accessible. Every Mode report or dashboard lives at a unique URL for future sharing, iterating, and building upon. Mode brings your entire team together in one platform.
Some of AI’s biggest problems can be solved by focusing on modelling our own human abilities instead of admiring NN and ML “intelligence”. We present an example that takes us in that direction in the form of chess.
Find out how many organizations are now planning AI implementations, although only a very small percentage are successfully deploying AI in production, at the AI Conference in New York Apr 15–18.
Unlock the power of data for your business in this 4-event-in-1, where over 500 data leaders will gather to analyze, develop and conquer real-world challenges with data.
We investigate several Natural Language Processing tasks and explain how Deep Learning can help, looking at Language Modeling, Sentiment Analysis, Language Translation, and more.
This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset
Gurobi would like to tap into your expertise in the field of Data Science and Analytics, and invites you to participate in their Data Science Survey. Everyone who completes this 10-minute survey can choose to be entered into a drawing to receive one of five $100 Amazon gift cards.
Analytics Summit 2019 focuses on analytics and data science content to support the growth and development of analytics efforts in business, government and non-profit organizations.
Word embeddings such as Word2Vec is a key AI method that bridges the human understanding of language to that of a machine and is essential to solving many NLP problems. Here we discuss applications of Word2Vec to Survey responses, comment analysis, recommendation engines, and more.
Check out this collection of six books which tackle the hard skills required to make sense of the changing field known as open data and muse on the ethical implications of a digitally connected world.
Python’s syntax is very clean and short in length. Python is open-source and a portable language which supports a large standard library. Buy why Python for data science? Read on to find out more.
Join conversations with Oracle, WPP, Axiata, Dyson, IBM, Netflix, Visa, AIA, Google, Bloomberg & more as they share how they utilize technology and data science.
Gartner predicts that citizen data scientists will surpass data scientists in the amount of advanced analytics produced. Does that mean that Enterprise AI and augmented analytics render the job of a data scientist obsolete? Download this white paper to found out more.
In our free guide, we show you how and where you can use extracted data from PDFs, and explain the necessary qualities you should be looking for when evaluating extraction tools.
We take a look at the arguments against implementing a machine learning solution, and the occasions when the problems faced are not ML problems and can perhaps be solved using optimization, exploratory data analysis tasks or problems that can be solved with simple statistics.
The Jupyter Project began in 2014 for interactive and scientific computing. Fast forward 5 years and now Jupyter is one of the most widely adopted Data Science IDE's on the market and gives the user access to Python and R
This is a short analysis of the interpretability of BERT contextual word representations. Does BERT learn a semantic vector representation like Word2Vec?
Also: Learn How to Listen: One of the hardest parts of being a data scientist; Top 10 Data Science Use Cases in Telecom; The Best and Worst Data Visualizations of 2018; The Analytics Engineer – new role in the data team; A Quick Guide to Feature Engineering
Predictive Analytics World for Financial is heading to Las Vegas, NV on Jun 16-20, and we're excited to announce the speaker line-up. The year’s only PAW Financial will be held alongside PAW Business, PAW Healthcare, PAW Industry 4.0, and Deep Learning World. Register now!
We recap some of the major highlights in data science and AI throughout 2018, before looking at the some of the potential newest trends and technological advances for the year ahead.
In this tutorial, you will learn how to set up a stable Python Machine Learning development environment. You’ll be able to get right down into the ML and never have to worry about installing packages ever again.
How do computers optimize mass persuasion – for marketing, presidential campaigns, and even healthcare? And why is there actually no data that directly records influence, considering it's so important? In this season finale episode, Eric Siegel introduces machine learning methods designed to persuade.
This eBook includes insights and learnings on how data scientists from four leading companies delivered impressive business results like accelerating global inventory from 48 hours to 45 minutes and reducing operational cost of analytics infrastructure by 30%. Get the eBook now!
This article summarizes a paper which presents us with a broad sweep of the graph neural network landscape. It’s a survey paper, so you’ll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them.
Listen, Be Humble, Be Present and Transform ideas. A Data Scientist will spend a large amount of their time in meetings where you can understand the business, the goals of the area, their KPIs, and their requirements.
This year’s KDD Cup will be celebrating 22 years. It’s been an exciting journey and we have come a long way! We invite industrial and academic institutions to submit proposals for organizing the 2019 KDD Cup Competition. Learn more now!
This IDC Solution Spotlight examines how automated machine learning tools can augment the analysis, modeling, and prediction of time series data to deliver easily understood and actionable insights for businesses in a simple and agile fashion. Get the report now.
An extensive introduction including a look at decision tree classification, data distribution, decision tree regression, decision tree learning, information gain, and more.
Also: GitHub: Numpy and Scipy are the most popular packages for machine learning projects; The Best and Worst Data Visualizations of 2018; 200 cognitive biases rule our everyday thinking; Neural Networks – an Intuition
This is the perfect opportunity for you to join 90+ data leaders to discuss your challenges, share success stories and offer your solutions Chief Data Officer Exchange in London next month. KDNuggets readers can benefit from a £500 saving to join as a delegate when you request an invitation.
Drexel’s new online MS in Data Science is the degree that launched a thousand opportunities. You’ll graduate workplace-ready by having experience with some of the industry’s leading technology. Success is waiting. Apply today!
The agenda for Deep Learning World Europe has just been released. Industry leaders will gather in Munich to foster progress in the value-driven operationalization of established deep learning methods. Register now and take advantage of Early Bird rates!
In a constantly changing landscape and with many companies, the roles and responsibilities of data engineers, analysts, and data scientists are changing, forcing the introduction of a new role: The Analytics Engineer.
If you’re going to do Machine Learning in Python, Scikit Learn is the gold standard. Scikit-learn provides a wide selection of supervised and unsupervised learning algorithms. Best of all, it’s by far the easiest and cleanest ML library.
If you want to translate the power of data analytics into business value, you need the skills you'll learn from the online Master of Science in Business Analytics program from the Tepper School of Business at Carnegie Mellon University.
Also: Intuitive Visualization of Outlier Detection Methods; Understanding Gradient Boosting Machines; The Best and Worst Data Visualizations of 2018; Your AI skills are worth less than you think
Starting with the basics, this free eBook covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format.
Reinforce is the perfect place to meet other professionals, network with the leaders of the AI industry and have a beer in Budapest, in the heart of Europe. Use code KDNuggets for 20% off.
Learn the top challenges in the world of Intelligent Automation, the main trends in RPA, AI, Machine Learning, and key areas of spend for technologies in 2019. Get the report!
AI represents a step change in humanity’s ability to rise to its greatest challenges. We explore three areas in which AI can contribute to the UN’s Global Goals - and why we could fall short.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Natural Language Processing and how it is used in social media analytics.
At DATAx Singapore join data science and business leaders across industries presenting how machine learning algorithms and analytics improve business results. Book by 15 Feb and get 20% off using code KDNY20.
The agenda for Predictive Analytics World Industry 4.0 has just been released. Join experts in predictive analytics on 6-7 May in Munich to discover and discuss the latest trends and technologies in machine & deep learning. Register now and take advantage of Early Bird rates!
Marketing Analytics and Data Science is coming to San Francisco, Apr 8-10, and get the tools you need to secure your data and continue business success with GDPR. Save 20% with VIP Code: MADS19KDN.
We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks.
This 8 chapter series includes interviews with technical and business leaders from a number of large companies with the aim to help you adopt machine learning in your organization.
Feature engineering plays a key role in machine learning, data mining, and data analytics. This article provides a general definition for feature engineering, together with an overview of the major issues, approaches, and challenges of the field.
QCon.ai is a three-day conference focused on the major machine learning and AI software trends affecting software engineers today. Register by Feb 23 with code "KDN" and save.
Join us at TDWI Chicago, Apr 28 - May 3 - the agenda is live! Choose from 50+ full- and half-day sessions and achieve your #DataGoals in 2019. Save up to $915 with code KD20!
We reflect on some of the best examples of Data Visualization throughout 2018, before focussing on some of the not-so-good and how these can be improved.
Thousands of top data scientists, analysts, engineers, and executives converge at Strata Data Conference every year. Early Price for Strata in San Francisco expires on Friday, February 15. Save up to $689 off a standard price Gold pass with code KDNU.
ODSC East 2019, Boston, Apr 30 - May 3, will host over 300+ of the leading experts in data science and AI. Here are a few standout topics and presentations in this rapidly evolving field. Register for ODSC East at 50% off till Feb 8.
Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this.
The following is a method I developed, which is based on my personal experience managing a data-science-research team and was tested with multiple projects. In the next sections, I’ll review the different types of research from a time point-of-view, compare development and research workflow approaches and finally suggest my work methodology.
This article outlines 10 top trending technologies for 2019, a list which covers diverse topics such as security, IoT, reinforcement learning, energy sustainability, smart cities, and much more.
Also Brilliant tour-de-force! Reinforcement Learning to solve Rubiks Cube; Dask, Pandas, and GPUs: first steps; Neural network AI is simple. So Stop pretending you are a genius.
At WGU, you could earn your data analytics degree and multiple industry-recognized certifications at the same time, for one price. Take the next step in your career! Earn the credentials you need and turn your future success up a degree.
However despite its massive popularity, many professionals still use this algorithm as a black box. As such, the purpose of this article is to lay an intuitive framework for this powerful machine learning technique.
Also: Data Scientists: Why are they so expensive to hire?; Trending Deep Learning Github Repositories; The Algorithms Aren’t Biased, We Are; Five Ways Your Safety Depends on Machine Learning; Cracking the Data Scientist Interview
If you intend to use data visualizations in a presentation or publication, be certain that your audience will understand and trust the information. Here are six mistakes you will want to avoid.
Check out this visualization for outlier detection methods, and the Python project from which it comes, a toolkit for easily implementing outlier detection methods on your own.
The 2019 INFORMS Conference on Business Analytics and Operations Research offers a rich experience for Analytics professionals and other business leaders. Get early rates until March 4.
Mega-Paw, Jun 16-20, delivers brand-name, cross-industry, vendor-neutral case studies purely on machine learning's commercial deployment, and the hottest topics and techniques. Check out 5 reasons to join us in Las Vegas!
This webinar from Looker is designed to introduce you to the benefits of embedded analytics, criteria for evaluation tools and vendors and the Lookers platform vs the alternatives.
This article uses direct marketing campaign data from a Portuguese banking institution to predict if a customer will subscribe for a term deposit. We’ll be working with R’s Caret package to achieve this.
Eric Siegel tells you about five ways your safety depends on machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.
If you have data-savvy analytics talent, then you have a solid foundation to begin your AI journey. The next step: automated machine learning. Will this be the year your team starts implementing AI? Join DataRobot @ 1 PM ET, Feb 7, for more info.
Making Databases Work is a collection of chapters written by leading database researcher and enterpreneur Michael Stonebraker and 38 of his collaborators: world-leading database researchers, world-class systems engineers, and business partners.
We provide some reasoning behind the high cost factor of hiring a data scientist, including the increasing amount of data ready to be analyzed, the structural shortage of people with the appropriate skills, and more.
4 Reasons Why Your Machine Learning Code is Probably Bad
Python Data Science for Beginners
Top 10 Technology Trends of 2019
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