Search results for Common Vulnerabilities Exposures

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  • Data Science Interview Guide

    ...lits to stop it from getting pure leafs (common tactic to fix over-fitting problems). The Information Gain calculated to split the tree is important. COMMON INTERVIEW PROBLEM! ENSURE YOU KNOW HOW INFORMATION GAIN IS CALCULATED!!! The common Information Gain calculation functions are Gini and...

    https://www.kdnuggets.com/2018/04/data-science-interview-guide.html

  • Common Sense in Artificial Intelligence… by 2026?

    ...ere ten years… I must say that despite the challenge, I am with Chace. At 50-to-1 odds, I would bet for the software industry. The incentive to offer common sense is great. After all, you can’t drive a car, clean a house or serve burgers without some common sense. What the deep learning craze has...

    https://www.kdnuggets.com/2016/08/common-sense-artificial-intelligence-2026.html

  • A Complete Exploratory Data Analysis and Visualization for Text Data: Combine Visualization and NLP to Generate Insights

    ...=sorted(words_freq, key = lambda x: x[1], reverse=True) return words_freq[:n] common_words = get_top_n_words(df['Review Text'], 20) for word, freq in common_words: print(word, freq) df1 = pd.DataFrame(common_words, columns = ['ReviewText' , 'count'])...

    https://www.kdnuggets.com/2019/05/complete-exploratory-data-analysis-visualization-text-data.html

  • Machine Learning for Text Classification Using SpaCy in Python

    ...tle('Most Common Words used in the research papers for conference INFOCOM') plt.show() Figure 3 IS_common_words = [word[0] for word in IS_counts.most_common(20)] IS_common_counts = [word[1] for word in IS_counts.most_common(20)] fig = plt.figure(figsize=(18,6)) sns.barplot(x=IS_common_words,...

    https://www.kdnuggets.com/2018/09/machine-learning-text-classification-using-spacy-python.html

  • DARPA SBIR: Defense Against National Vulnerabilities in Public Data

    …asure the national security impact of public data and to defend against the malicious use of public data against national interests. DESCRIPTION: The vulnerabilities to individuals from a data compromise are well known and documented now as “identity theft.” These include regular stories published…

    https://www.kdnuggets.com/2013/08/darpa-sbir-defense-against-national-vulnerabilities-public-data.html

  • How to tackle common data cleaning issues in R

    ...ote Re-typing a date is important so that R knows to use the value as an actual date and you can use the various R data functions correctly.   A common example is when data contains cases with dates that are perhaps formatted as YYYY/MM/DD and you want to perform a time series analysis showing...

    https://www.kdnuggets.com/2018/05/packt-tackle-common-data-cleaning-issues-r.html

  • AI is a Big Fat Lie

    ...s making its way toward the ability to reason like people. To gain humanlike "common sense." That’s a powerful brand. But it’s an empty promise. Your common sense is more amazing – and unachievable – than your common sense can sense. You're amazing. Your ability to think abstractly and "understand"...

    https://www.kdnuggets.com/2019/01/dr-data-ai-big-fat-lie.html

  • 7 Common Data Science Mistakes and How to Avoid Them

    …f either group in the population can skew the model and some important variables might fall into the under-represented segment. These are some of the common mistakes data scientists make doing data science. If you can think of any other common data science mistakes, we would love to hear your…

    https://www.kdnuggets.com/2016/01/7-common-data-science-mistakes.html

  • Must-Know: What are common data quality issues for Big Data and how to handle them?">Gold Blog, May 2017Must-Know: What are common data quality issues for Big Data and how to handle them?

    ...sed, etc.). Usually, a lot of these data sources are outside an organization and thus, it is very hard to ensure good metadata for such data. Another common issue is syntactic inconsistencies. For example, “time-stamp” values from different sources would be incompatible unless they are captured...

    https://www.kdnuggets.com/2017/05/must-know-common-data-quality-issues-big-data.html

  • 7 common mistakes when doing Machine Learning

    ...actitioners by default use kernel when training a SVM model. However, when the data has n<

    common in industries like medical data -- the richer feature space implies a much higher risk to overfit the data. In fact, high variance models...

    https://www.kdnuggets.com/2015/03/machine-learning-data-science-common-mistakes.html

  • Avoid These Common Data Visualization Mistakes

    …web technology, small business and marketing on his site Skillcode and in such leading sites as Huffington Post, Shopify, and MediaTemple. Related: 7 Common Data Science Mistakes and How to Avoid Them 7 common mistakes when doing Machine Learning 10 things statistics taught us about big data…

    https://www.kdnuggets.com/2016/02/common-data-visualization-mistakes.html

  • Common mistakes when carrying out machine learning and data science">Gold BlogCommon mistakes when carrying out machine learning and data science

    ...as a good starting point for solving something big let people find their own insights by providing interactive dashboards don’t restrict yourself to common feature engineering as multiplying two variables. Try to find additional sources of data or explanations try out ensembles and stacked models...

    https://www.kdnuggets.com/2018/12/common-mistakes-data-science.html

  • Top Stories, Dec 3-9: Common mistakes when carrying out machine learning and data science; AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019

    ...rojects Employers Want To See: How To Show A Business Impact, by John Sullivan - Dec 04, 2018. The Machine Learning Project Checklist - Dec 07, 2018. Common mistakes when carrying out machine learning and datascience - Dec 06, 2018. Best Machine Learning languages, Data Visualization Tools, DL...

    https://www.kdnuggets.com/2018/12/top-news-week-1203-1209.html

  • KDnuggets™ News 18:n47, Dec 12: Common mistakes when doing machine learning; Here are the most popular Python IDEs / Editors

    ...Learning Project Checklist; The difference between learning ML and learning DS; A very comprehensive list of ML resources, and more.   Features Common mistakes when carrying out machine learning and data science Machine Learning & AI Main Developments in 2018 and Key Trends for 2019 Here...

    https://www.kdnuggets.com/2018/n47.html

  • The Executive Guide to Data Science and Machine Learning

    ...rategy is so important. Data engineers work with capturing and optimally storing data for future use in analyses or reporting. Hadoop and SQL are two common databases for data storage (where the data “lives” while it awaits analysis). SQL is common in established businesses and businesses that...

    https://www.kdnuggets.com/2018/05/executive-guide-data-science-machine-learning.html

  • 17 More Must-Know Data Science Interview Questions and Answers, Part 3">Silver Blog, March 201717 More Must-Know Data Science Interview Questions and Answers, Part 3

    ...30 minutes, Kenn Elliott Data Visualization for Human Perception, landmark work by Stephen Few (key ideas summarized here) Q14. What are some of the common data quality issues when dealing with Big Data? What can be done to avoid them or to mitigate their impact?   Anmol Rajpurohit answers:...

    https://www.kdnuggets.com/2017/03/17-data-science-interview-questions-answers-part-3.html

  • Implementing Your Own k-Nearest Neighbor Algorithm Using Python

    ...n a similar way, you can grab the classes of the nearest neighbours, tally up how frequently the different class labels occur, and then find the most common label. This most common label will be the class prediction of the test instance. Running our algorithm That's about it. Now, just string the...

    https://www.kdnuggets.com/2016/01/implementing-your-own-knn-using-python.html

  • The 5 Common Mistakes That Lead to Bad Data Visualization

    ...asics: is your data clean? Use checks at every stage the data goes through — collection, sourcing, cleaning, and compiling — before it is visualized. Common errors include data duplication, missed data, NA values not marked, and so on. For instance, in this pie chart, the three sectors of the pie...

    https://www.kdnuggets.com/2017/10/5-common-mistakes-bad-data-visualization.html

  • Top stories for Jan 24-30: 7 Common Data Science Mistakes; Businesses Will Need 1M Data Scientists by 2018

    ...ed to Become a Data Scientist 7 Common Data Science Mistakes and How to Avoid Them Top 10 Data Analysis Tools for Business    Most shared 7 Common Data Science Mistakes and How to Avoid Them - Jan 26, 2016. Businesses Will Need One Million Data Scientists by 2018 - Jan 28, 2016. Modern...

    https://www.kdnuggets.com/2016/01/top-news-week-jan-24.html

  • Top KDnuggets tweets, Feb 2-3: Avoiding a Common Mistake with Time Series; A New Year in Data Science, great overview

    ...d: Avoiding a Common Mistake with Time Series: use de-trending #DataScience #MachineLearning t.co/NFfPXC69x6 t.co/2WlHbRgYls Most Clicked: Avoiding a Common Mistake with Time Series: use de-trending #DataScience #MachineLearning t.co/NFfPXC69x6 t.co/2WlHbRgYls Top 10 most engaging Tweets Avoiding a...

    https://www.kdnuggets.com/2015/02/top-tweets-feb02-03.html

  • Simplifying Data Pipelines in Hadoop: Overcoming the Growing Pains

    ...readily saw the importance of standardizing the development of ETL pipelines in the Hadoop/Hive/Impala environment. This included 4 key components: a common framework, common tools, an edge node, and JupyterHub. Common framework. ETL engineers used a common development framework so ETL jobs can be...

    https://www.kdnuggets.com/2017/05/simplify-data-pipelines-hadoop.html

  • Unfolding Naive Bayes From Scratch

    ...abilities of individual words ( as found above ) in order to find the numerical value of the term : product ( p of a test word “ j ” in class c ) The Common Pitfall of Zero Probabilities!By now, we have numerical values for both the terms i.e ( p of class c and product ( p of a test word “ j ” in...

    https://www.kdnuggets.com/2018/09/unfolding-naive-bayes.html

  • 4 Common Data Fallacies That You Need To Know

    ...ions. To avoid falling for these tricks, the first step is to be aware of them so you can avoid being a victim. That’s why we put together a guide to common data fallacies. We’ve also designed this poster for your workspace, so that you can always be reminded of these fallacies when you’re working...

    https://www.kdnuggets.com/2017/12/4-common-data-fallacies.html

  • Named Entity Recognition and Classification with Scikit-Learn">Gold BlogNamed Entity Recognition and Classification with Scikit-Learn

    ...eight in state_features: print("%0.6f %-8s %s" % (weight, label, attr)) print("Top positive:") print_state_features(Counter(crf.state_features_).most_common(30)) print("\nTop negative:") print_state_features(Counter(crf.state_features_).most_common()[-30:]) Figure 17 Observations: 5.183603 B-tim...

    https://www.kdnuggets.com/2018/10/named-entity-recognition-classification-scikit-learn.html

  • How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls">Gold BlogHow (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls

    ...ed to many other prediction tasks. This post will go through the task of time series forecasting using machine learning, and how to avoid some of the common pitfalls. Through a concrete example, I will demonstrate how one could seemingly have a good model and decide to put it into production,...

    https://www.kdnuggets.com/2019/05/machine-learning-time-series-forecasting.html

  • Common Machine Learning Obstacles

    ...ta Analytics, MathWorks Engineers and scientists who are modeling with machine learning often face challenges when working with data. Two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting. Classification models assign items to a discrete...

    https://www.kdnuggets.com/2019/09/mathworks-common-machine-learning-obstacles.html

  • Data Science and Big Data: Definitions and Common Myths

    Big data is nowadays one of the most common buzzwords you might have heard of. There are many ways to define what big data is, and this is why probably it still remains a really difficult concept to grasp. Someone describes big data as dataset bigger than a certain threshold, e.g., over a...

    https://www.kdnuggets.com/2016/12/data-science-big-data-definitions-common-myths.html

  • 8 Common Pitfalls That Can Ruin Your Prediction

    ...tilize others’ observations, not just yours. Data-based predictions can increase your company’s profit or make your life better. But be careful! Some common mistakes can cause your predictions to be useless or even misleading. Common Predictions You Are Always Calculating If you will need an...

    https://www.kdnuggets.com/2018/03/8-common-pitfalls-ruin-prediction.html

  • Top stories for Feb 1-7: Avoiding a Common Mistake with Time Series; Top Big Data Influencers and Brands

    Most viewed news items Avoiding a Common Mistake with Time Series - Feb 2, 2015. (Deep Learning's Deep Flaws)'s Deep Flaws - Jan 26, 2015. Two Most Important Trends in Analytics and Big Data in 2015 - Feb 6, 2015. Top Big Data Influencers and Brands - Feb 2, 2015. Data Science 102: K-means...

    https://www.kdnuggets.com/2015/02/top-news-week-feb-1.html

  • Tree Kernels: Quantifying Similarity Among Tree-Structured Data

    ...rack: get_subpaths(graph, node, track, path + [node, ] ) def kernel_subpath(t1, t2, common_track) : kernel_v = 0 for p in subpath_track: kernel_v + = common_track[t1] [p] *common_track[t2] [p] return kernel_v In this example, we used the weighting parameter w|s| w|p| = 1. This gives all subpaths...

    https://www.kdnuggets.com/2016/02/tree-kernels-quantifying-similarity-tree-structured-data.html

  • KDnuggets™ News 15:n08, Mar 11: 7 common Machine Learning mistakes; Statistical Reasoning

    ...Software | Opinions | Interviews | Reports | News | Webcasts | Courses | Meetings | Jobs | Academic | Tweets | Publications | CFP | Quote Features 7 common mistakes when doing Machine Learning - Mar 7, 2015. In statistical modeling, there are various algorithms to build a classifier, and each...

    https://www.kdnuggets.com/2015/n08.html

  • KDnuggets™ News 15:n04, Feb 4: Top Big Data Influencers; A Common Mistake with Time Series; Ayasdi

    ...alysis, but what are its underlying assumptions and drawbacks? We examine what happens for non-spherical data and unevenly sized clusters. Avoiding a Common Mistake with Time Series - Feb 2, 2015. We explore a common mistake in analyzing relationships between time series, and show how de-trending...

    https://www.kdnuggets.com/2015/n04.html

  • Top stories in March: 7 common Machine Learning mistakes; Deep Learning for Text Understanding from Scratch

    Most viewed news items 7 common mistakes when doing Machine Learning - Mar 7, 2015. Deep Learning for Text Understanding from Scratch - Mar 13, 2015. More Free Data Mining, Data Science Books and Resources - Mar 25, 2015. Deep Learning, The Curse of Dimensionality, and Autoencoders - Mar 12, 2015....

    https://www.kdnuggets.com/2015/04/top-news-2015-mar.html

  • Data Mining for Predictive Social Network Analysis – Brazil Elections Case Study

    …trend topic between two cities, there is an edge (i.e., link) between those cities. Each edge is weighted according to the number of trend topics in common between those two cities (i.e., the more trend topics two cities have in common, the heavier the weight that is attributed to the link between…

    https://www.kdnuggets.com/2015/11/data-mining-predictive-social-network-analysis.html

  • Big Data Assessment – Key Business Drivers, Expected Benefits and Common Challenges

    ..., trying to understand the industry benchmarks so that they can set up their own specific business goals, and prepare themselves for some of the most common challenges faced by Big Data initiatives. To better understand the business drivers, expected benefits, and challenges of Big Data, QuinStreet...

    https://www.kdnuggets.com/2014/06/big-data-assessment-business-drivers-benefits-challenges.html

  • Comparison of the Text Distance Metrics

    ...pletely the same. You may be guessing what is the difference between comparing strings on the basis of the longest common subsequence and the longest common substring. Longest common subsequence doesn’t take into account if there are some letters between characters from subsequence. For example,...

    https://www.kdnuggets.com/2019/01/comparison-text-distance-metrics.html

  • Building NLP Classifiers Cheaply With Transfer Learning and Weak Supervision

    ...h my workflow will hopefully save you a lot of time of trial and error. Below is an example of a LF that returns Positive if the tweet has one of the common insults against jew. Otherwise, it abstains. # Common insults against jews. INSULTS = r"\bjew...

    https://www.kdnuggets.com/2019/03/building-nlp-classifiers-cheaply-transfer-learning-weak-supervision.html

  • The State of Transfer Learning in NLP

    ...and libraries   For sharing and accessing pretrained models, different options are available: Hubs Hubs are central repositories that provide a common API for accessing pretrained models. The two most common hubs are TensorFlow Hub and PyTorch Hub. Hubs are generally simple to use; however,...

    https://www.kdnuggets.com/2019/09/state-transfer-learning-nlp.html

  • Text Preprocessing in Python: Steps, Tools, and Examples

    ...ndrew Kachites McCallum, University of Massachusetts Amherst, 2002 Includes sophisticated tools for document classification and sequence tagging Java Common Public License Support for inference in general graphical models [11] Pattern, T. De Smedt & W. Daeleman,  2012 Web mining module...

    https://www.kdnuggets.com/2018/11/text-preprocessing-python.html

  • Machine Learning 101: The What, Why, and How of Weighting

    ...he DV=1 cases. So, how can we make the DV=1 cases more important to the model? Idea #1: Oversampling: Duplicate existing rare event records and leave common event records alone. Idea #2: Undersampling: Remove some of the common event records, keep all the rare event records. In both cases, we...

    https://www.kdnuggets.com/2019/11/machine-learning-what-why-how-weighting.html

  • All you need to know about text preprocessing for NLP and Machine Learning

    ...xts where physicians take notes in non-standard ways. I’ve also found it useful for topic extraction where near synonyms and spelling differences are common (e.g. topic modelling, topic modeling, topic-modeling, topic-modelling). Unfortunately, unlike stemming and lemmatization, there isn’t a...

    https://www.kdnuggets.com/2019/04/text-preprocessing-nlp-machine-learning.html

  • 10 Exciting Ideas of 2018 in NLP

    ...d datasets is not easy and even popular ones show large biases. This year, there have been some well-executed datasets that seek to teach models some common sense such as Event2Mind and SWAG, both from the University of Washington. SWAG was solved unexpectedly quickly. My highlight: Visual...

    https://www.kdnuggets.com/2019/01/10-exciting-ideas-2018-nlp.html

  • Avoiding a Common Mistake with Time Series

    ...m and Stanton (PWS-KENT, 1989). Chapter 4 of their book discusses regression over time series, including this issue. Original: svds.com/post/avoiding-common-mistake-time-series Bio: Tom Fawcett is Principal Data Scientist at Silicon Valley Data Science. Co-author of the popular book Data Science...

    https://www.kdnuggets.com/2015/02/avoiding-common-mistake-time-series.html

  • A Beginner’s Guide to Data Engineering – Part II

    ...which means that any subset of the data can be looked up extremely quickly. This technique can greatly improve query performance. In particular, one common partition key to use is datestamp (ds for short), and for good reason. First, in data storage system like S3, raw data is often organized by...

    https://www.kdnuggets.com/2018/03/beginners-guide-data-engineering-part-2.html

  • Big Data for the Common Good “Collider”, at Frankfurt / Berkeley

    ...ley, Calif. – March 31, 2015– The Frankfurt Big Data Lab and ODBMS.org aim to support the creation of Collider project proposals for Big Data for the Common Good to be submitted to the Center for Entrepreneurship & Technology (CET) at UC Berkeley, and if selected implemented at UC Berkeley...

    https://www.kdnuggets.com/2015/04/big-data-common-good-frankfurt-berkeley.html

  • Top stories for Mar 8-14: 7 common Machine Learning mistakes; Deep Learning for Text Understanding from Scratch

    Most viewed news items 7 common mistakes when doing Machine Learning - Mar 7, 2015. Deep Learning for Text Understanding from Scratch - Mar 13, 2015. SQL-like Query Language for Real-time Streaming Analytics - Mar 12, 2015. 10 Steps to Success in Kaggle Data Science Competitions - Mar 11, 2015....

    https://www.kdnuggets.com/2015/03/top-news-week-mar-8.html

  • Robots Need “Common Sense” AI to Work Out Our Uncertain World

    ...d advances in mobile robot task planning and manipulation, with an overview of the field and examples of work from his lab, including machine vision, common sense reasoning and robotic grasping. I asked him a few questions to learn more about his work in this area, and to find out what we can...

    https://www.kdnuggets.com/2016/08/robots-need-common-sense-ai.html

  • Mining Twitter Data with Python Part 4: Rugby and Term Co-occurrences

    ...SYMBOL__', 1154)] Interestingly, a new token we didn’t account for, an Emoji symbol (in this case, the Irish Shamrock). If we have a look at the most common hashtags, we need to consider that#rbs6nations will be by far the most common token (that’s our search term for downloading the tweets), so we...

    https://www.kdnuggets.com/2016/06/mining-twitter-data-python-part-4.html

  • What do Postgres, Kafka, and Bitcoin Have in Common?

    ...ty?) as one of the first cryptocurrencies to really gain momentum. Underneath the hood, all three of these technologies have one interesting thing in common: their use of the immutable log. In order to achieve replication, Postgres uses something called an immutable Write-Ahead log, or WAL....

    https://www.kdnuggets.com/2016/07/postgres-kafka-bitcoin-common.html

  • How to solve 90% of NLP problems: a step-by-step guide">Silver BlogHow to solve 90% of NLP problems: a step-by-step guide

    ...approaching a problem, a general best practice is to start with the simplest tool that could solve the job. Whenever it comes to classifying data, a common favorite for its versatility and explainability is Logistic Regression. It is very simple to train and the results are interpretable as you...

    https://www.kdnuggets.com/2019/01/solve-90-nlp-problems-step-by-step-guide.html

  • From Data to Viz: how to select the the right chart for your data">Silver BlogFrom Data to Viz: how to select the the right chart for your data

    ...ommon dataviz pitfalls, with suggested workarounds. About the caveat gallery The best way to visualize data efficiently is probably to avoid the most common pitfalls. The caveat gallery lists about 40 common caveats, and the list is still growing. For instance, it points out that a barplot is much...

    https://www.kdnuggets.com/2018/08/data-visualization-right-chart.html

  • DynamoDB vs. Cassandra: from “no idea” to “it’s a no-brainer”

    ...a offers client-to-node and inter-node encryption. DynamoDB also provides ways to work with user authentication and access authorization. It’s a more common practice to assign certain permissions and access keys to users than go with user roles. And the smallest level of access granularity is an...

    https://www.kdnuggets.com/2018/08/dynamodb-vs-cassandra.html

  • How To Fine Tune Your Machine Learning Models To Improve Forecasting Accuracy

    ...in the feature set first. If you feed poor quality data in then the model will yield poor results. Please have a look at this article that highlights common techniques to we can use to enrich the features: Processing Data To Improve Machine Learning Model Accuracy If you are unsure whether your...

    https://www.kdnuggets.com/2019/01/fine-tune-machine-learning-models-forecasting.html

  • The Long Tail of Medical Data

    ...es can deviate so much from the data distribution that they end up in areas of the feature space for which the model has not been optimized. The most common sign of cancer on mammograms are masses: white blobs are typically quite small, something in the order of one to three centimeters. Larger...

    https://www.kdnuggets.com/2018/11/long-tail-medical-data.html

  • Pro Tips: How to deal with Class Imbalance and Missing Labels

    ...computed. In other words, two points are similar if they have indistinguishable starting points. The graph-based semi-supervised learning approach is common for image segmentation problems where each pixel of an image is represented by a node, and a few foreground and background pixels are either...

    https://www.kdnuggets.com/2019/11/tips-class-imbalance-missing-labels.html

  • The Notebook Anti-Pattern

    ...plication: One cannot import one notebook into another, therefore when trying multiple experiments in different notebooks one tends to copy paste the common pieces across. Should one of these notebooks change, the others are immediately out of date. This can be improved by extracting the common...

    https://www.kdnuggets.com/2019/11/notebook-anti-pattern.html

  • How to Become a (Good) Data Scientist – Beginner Guide">Platinum BlogHow to Become a (Good) Data Scientist – Beginner Guide

    ...ement them in code even at the novice level. Probability Distributions represent the probabilities of all possible values in the experiment. The most common in Data Science are a Uniform Distribution that has is concerned with events that are equally likely to occur, a Gaussian, or Normal...

    https://www.kdnuggets.com/2019/10/good-data-scientist-beginner-guide.html

  • Understanding Cloud Data Services">Gold BlogUnderstanding Cloud Data Services

    ...tunately, tools (such as Unravel) exist to monitor, optimize, and plan migrations for big data systems and pipelines. During migration planning it is common to discover inefficient, redundant, and even unnecessary pipelines actively running, chewing up compute, and costing the organization time and...

    https://www.kdnuggets.com/2019/06/understanding-cloud-data-services.html

  • Towards Automatic Text Summarization: Extractive Methods

    ...measures the density of the topic words. Frequency-driven approaches This approach uses frequency of words as indicators of importance. The two most common techniques in this category are: word probability and TF-IDF (Term Frequency Inverse Document Frequency). The probability of a word w is...

    https://www.kdnuggets.com/2019/03/towards-automatic-text-summarization.html

  • Feature engineering, Explained

    ...d. These examples show the never changing truth - know your data! And this is important during the feature engineering as well. That was missing! One common practice is to introduce a boolean feature indicating whether a given sample had a missing value in the given feature. It takes on either True...

    https://www.kdnuggets.com/2018/12/feature-engineering-explained.html

  • Key Algorithms and Statistical Models for Aspiring Data Scientists">Gold BlogKey Algorithms and Statistical Models for Aspiring Data Scientists

    ...rate out traditional statistical methods from new methods, I will separate the two branches in the list. Statistical methods include some of the more common methods overviewed in bootcamps and certificate programs, as well as some of the less common methods that are typically taught in graduate...

    https://www.kdnuggets.com/2018/04/key-algorithms-statistical-models-aspiring-data-scientists.html

  • Hadoop Key Terms, Explained

    ...iltering and sorting. The other one is the Reduce() part, designed to perform summary of the output from the Map part. 3. Hadoop Common   Apache Common contains common utilities to support different Hadoop modules. It is basically a library of common tools and utilities. Hadoop common is...

    https://www.kdnuggets.com/2016/05/hadoop-key-terms-explained.html

  • Big Data for Social Good: UC Berkeley and Geisinger Health Collider Project

    ...effective. New hope lies in integrated analysis of complete medical and social histories. Using EMRs, we can see patterns that obese patients have in common, infer risk of obesity from other medical conditions, and also find new ways to characterize patients that could be successfully treated. Can...

    https://www.kdnuggets.com/2015/10/big-data-social-good-berkeley-geisinger-health.html

  • WebDataCommons – the Data and Framework for Web-scale Mining

    ...nt of pre-processing and large data management expertise is required to extract the relevant pieces of data from the corpus. This is where the WebDataCommons project jumps in and supports data miners by extracting structured data from the Common Crawl and providing this data for public download....

    https://www.kdnuggets.com/2015/05/webdatacommons-data-web-scale-mining.html

  • Interview: Vince Darley, King.com on the Serious Analytics behind Casual Gaming

    ...ost common use cases of Machine Learning and Predictive Analytics at King? VD: King really cares about the long-term perspective, so perhaps the most common case is about predicting or modeling the long-term customer-lifetime-value impact of a game/network feature-change on particular groups of...

    https://www.kdnuggets.com/2015/03/interview-vince-darley-king-analytics-gaming.html

  • Doing Data Science: A Kaggle Walkthrough Part 4 – Data Transformation and Feature Extraction

    ...rmation is undertaken with the intention to enhance the ability of the classification algorithm to extract information from the data. Below are a few common data transformation methods used. Bucketing/Binning A common method for manipulating numeric data, binning or bucketing is when the numerical...

    https://www.kdnuggets.com/2016/06/doing-data-science-kaggle-walkthrough-data-transformation-feature-extraction.html

  • Deep Learning for Visual Question Answering

    …n word embeddings We have a number of choices when using word embeddings, and I experimented with three of them: GloVe Word Embeddings trained on the common-crawl: These gave the best performance, and all results reported here are using these embeddings. Goldberg and Levy 2014: These are the…

    https://www.kdnuggets.com/2015/11/deep-learning-visual-question-answering.html

  • Semi-supervised Feature Transfer: The Practical Benefit of Deep Learning Today?

    ...2000, 4000 examples. All models tested on 4000 examples. Strategy B: Integrate a pre-built API.   Some problems, such as sentiment analysis, are common enough that pre-trained APIs may be available for the specific problem. Although you should confirm that your use case and data domain are...

    https://www.kdnuggets.com/2016/07/semi-supervised-feature-transfer-deep-learning.html

  • 12 Useful Things to Know About Machine Learning">Silver Blog12 Useful Things to Know About Machine Learning

    ...is key to the efficiency of the learner, and also helps determine the classifier produced if the evaluation function has more than one optimum. It is common for new learners to start out using off-the-shelf optimizers, which are later replaced by custom-designed ones.   2 — It’s Generalization...

    https://www.kdnuggets.com/2018/04/12-useful-things-know-about-machine-learning.html

  • Machine Learning Reveals 9 Elements of Deal-Closing Sales

    …ive the impression that you have a solution in search of a problem. Act II: The Upside Down Demo The research revealed that successful demos are most commonly conducted in an “upside down pyramid” manner. They start with the conclusion, rather than end with it. Instead of “building up” to the most…

    https://www.kdnuggets.com/2017/09/gongio-9-elements-deal-closing-sales-demos.html

  • One Deep Learning Virtual Machine to Rule Them All

    ...wever is is a competitive race to become the ruling standard. That is, one computational graph to rule them all. Can we not all band together for the common good? A common standard deep learning virtual machine is a futuristic dream. One obvious idea is to leverage deep learning itself to optimize...

    https://www.kdnuggets.com/2017/04/deep-learning-virtual-machine-rule-all.html

  • How to Become a Data Scientist – Part 1">2016 Silver BlogHow to Become a Data Scientist – Part 1

    ..., you should be able to figure out how relevant your current skillset is, whatever your background. Other Scientific Disciplines This is not the most common route to data science; statistics and computer science are, as we will consider next. But with scientists from many fields having highly...

    https://www.kdnuggets.com/2016/08/become-data-scientist-part-1.html

  • Interviews with Data Scientists: Claudia Perlich

    ...learn from it – so we used decision trees on such single features as a means of discretizing the numeric feature and feed it into the linear models. Common generic tricks to help linear models: discretize numeric variables, cap extreme values of linear and add indicator, include interaction...

    https://www.kdnuggets.com/2016/12/interviews-data-scientists-claudia-perlich.html

  • Why the Data Scientist and Data Engineer Need to Understand Virtualization in the Cloud

    ...o take place. Announcements were made in late 2016 that the full VMware Software Defined Data Center portfolio will execute in AWS as a service. This common infrastructure across private and public cloud extends the flexibility and choice for the data scientist/data engineer. Analysis workloads on...

    https://www.kdnuggets.com/2017/01/data-scientist-engineer-understand-virtualization-cloud.html

  • How to Make Your Database 200x Faster Without Having to Pay More

    …ta scientist, you often find yourself training statistical models, parameter tuning, or just doing feature selection and engineering. One of the most common frustrations here is the massive number of parameters or features that you need to try out and the long time it takes to train machine…

    https://www.kdnuggets.com/2016/11/make-database-200x-faster.html

  • Do You Suffer From Analytic Personality Disorder (APD)?

    ...entists rather - drunk on confirmation bias will draw out even the most anecdotal "insight" from random data points and spurious correlations to defy common intuition - (since my birth I have not experienced any form of death, therefore evidence supports that I am immortal). Humanisnomerism The...

    https://www.kdnuggets.com/2016/11/analytic-personality-disorder.html

  • How to Rank 10% in Your First Kaggle Competition

    ...nal result will use the remaining data in the testing set, which is referred to as a Private LB score. The score you get by local cross validation is commonly referred to as a CV score. Generally speaking, CV scores are more reliable than LB scores. Beginners can learn a lot from Forum and Scripts....

    https://www.kdnuggets.com/2016/11/rank-ten-precent-first-kaggle-competition.html

  • Parallelism in Machine Learning: GPUs, CUDA, and Practical Applications

    ...ugh GPU Acceleration" notes speed increases, and the paper provides some additional insight into conceptualizing parallelism algorithmically. Another common task used in machine learning which is ripe for parallelization is distance calculation. Euclidean distance is a very common metric which...

    https://www.kdnuggets.com/2016/11/parallelism-machine-learning-gpu-cuda-threading.html

  • Machine Learning vs. Statistics: The Texas Death Match of Data Science">Silver Blog, Aug 2017Machine Learning vs. Statistics: The Texas Death Match of Data Science

    ...ific research. In many cases, both fields use different terminology when referring to exactly the same thing.3 Putting the two groups together into a common data science team (while often adding individuals trained in other scientific fields) can create a very interesting team dynamic. However, the...

    https://www.kdnuggets.com/2017/08/machine-learning-vs-statistics.html

  • AI and Deep Learning, Explained Simply">Silver Blog, July 2017AI and Deep Learning, Explained Simply

    …s. A trained ML can be copied in no time, unlike a brain’s experience transfer to another brain. Big providers will sell pre-trained MLs for reusable common tasks, for ex. “radiologist ML”. The ML will complement one human expert, who keeps required, and replace just the “extra” staff. An hospital…

    https://www.kdnuggets.com/2017/07/ai-deep-learning-explained-simply.html

  • Chief Data Officer Toolkit: Leading the Digital Business Transformation – Part 1

    ...keholders to identify the key decisions that they need to make in support of the targeted Business Initiative, and then we group those decisions into common subject areas or use cases (see Figure 4). Figure 4: Group Decisions Into Common Use Cases Listed below are the use cases that came out of the...

    https://www.kdnuggets.com/2016/10/schmarzo-cdo-toolkit-leading-digital-business-transformation-part-1.html

  • Design by Evolution: How to evolve your neural network with AutoML

    ...evolutionary algorithm are a)the selection process and b)the crossover/mutation strategy that needs to follow. Selection: the way parents are picked; common practice is to pick the k-best and some random individuals for diversity. More advanced selection techniques involve creating different...

    https://www.kdnuggets.com/2017/07/design-evolution-evolve-neural-network-automl.html

  • A Short Guide to Navigating the Jupyter Ecosystem

    ...rees of freedom in how we as a team can jump into the client’s existing data science framework. Each team member logging in via their own laptop to a common edge node and sharing results from a git repository is a common setup. Another one that we are seeing more frequently (and encourage the use...

    https://www.kdnuggets.com/2017/03/guide-navigating-jupyter-ecosystem.html

  • What Is Data Science, and What Does a Data Scientist Do?">Gold Blog, Mar 2017What Is Data Science, and What Does a Data Scientist Do?

    ...aries along a scale ranging from beginner, to proficient, and to expert, in the ideal case. By Calvin.Andrus (Own work) [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons While these, and other disciplines and areas of expertise (not shown here), are all...

    https://www.kdnuggets.com/2017/03/data-science-data-scientist-do.html

  • Every Intro to Data Science Course on the Internet, Ranked">Silver Blog, March 2017Every Intro to Data Science Course on the Internet, Ranked

    ...s depending on Udemy discounts, which are frequent, so you may be able to purchase access for as little as $10. Though it doesn’t check our “usage of common data science tools” box, the non-Python/R tool choices (gretl, Tableau, Excel) are used effectively in context. Eremenko mentions the...

    https://www.kdnuggets.com/2017/03/every-intro-data-science-course-ranked.html

  • The Anatomy of Deep Learning Frameworks">Silver Blog, Feb 2017The Anatomy of Deep Learning Frameworks

    .... After exploring the white papers and the dev docs, I could understand the design choices and was able to abstract the fundamental concepts that are common to all of these. In this post, I have tried to sketch out these common principles which would help you better understand the frameworks and...

    https://www.kdnuggets.com/2017/02/anatomy-deep-learning-frameworks.html

  • Algorithmia Tested: Human vs Automated Tag Generation

    ...s for 5.2% of those tags. This makes the some of the machine-generated tags somewhat less useful. But this also goes along in the same vein as having common tags used as high-level classifications of posts. Note that "KDnuggets" was an extremely common tag in the machine tags, but was removed...

    https://www.kdnuggets.com/2015/04/algorithmia-tested-automated-tag-generation.html

  • Do more with Python: Creating a graph application with Python, Neo4j, Gephi, and Linkurious

    …term frequency factor boosts terms which appear often in a document, whilst the inverse document frequency factor gets rid of terms which are overly common across the entire corpus (for example, the term ‘programming’ is common in our product copy, and whilst most of the documents ARE about…

    https://www.kdnuggets.com/2015/10/packt-graph-python-neo4j-gephi-linkurious.html

  • Integrating Python and R into a Data Analysis Pipeline, Part 1

    …sing in arguments as required. Pros Simplest method, so commonly the quickest Can view the intermediate outputs easily Parsers already exist for many common file formats: CSV, JSON, YAML Cons Need to agree upfront on a common schema or file format Can become cumbersome to manage intermediate…

    https://www.kdnuggets.com/2015/10/integrating-python-r-data-analysis-part1.html

  • KDnuggets™ News 15:n11, Apr 15: Big Data Predictive Analytics Gainers & Losers; Awesome Public Datasets

    ...pr 4, 2015. Poll from Bart Baesens at KU Leuven asks about your usage of Machine Learning APIs and other predictive analytics tools. Big Data for the Common Good "Collider", at Frankfurt / Berkeley - Apr 1, 2015. The Frankfurt Big Data Lab and ODBMS.org cooperate with the Center for...

    https://www.kdnuggets.com/2015/n11.html

  • KDnuggets™ News 15:n09, Mar 25: Deep Learning from Scratch; 10 steps to Kaggle Success; US CDS DJ Patil Cartoon

    ...when doing Machine Learning; White House report on Big Data and Differential Pricing; Why Data Gravity Cannot be Ignored. Top stories for Mar 8-14: 7 common Machine Learning mistakes; Deep Learning for Text Understanding from Scratch - Mar 15, 2015. 7 common mistakes when doing Machine Learning;...

    https://www.kdnuggets.com/2015/n09.html

  • Behind the Dream of Data Work as it Could Be

    ...Some are just getting started in their field and some remember a time before data science became “Data Science.” But here’s what many of them had in common: Their skills are under increasing demand, but in short supply within their organizations. They are dedicated and overworked. They want to be...

    https://www.kdnuggets.com/2016/09/behind-dream-data-work.html

  • All Machine Learning Models Have Flaws

    ...e is not simply "woe unto us". There are several implications which seem important. The multitude of models is a point of continuing confusion. It is common for people to learn about machine learning within one framework which often becomes there "home framework" through which they attempt to...

    https://www.kdnuggets.com/2015/03/all-machine-learning-models-have-flaws.html

  • Software development skills for data scientists

    …ct3_final.py, project3_final_do_not_delete_final_revised.py and so on. Version control provides a centralized way for one to many people to work on a common codebase at the same time without writing over each other’s work. Each person “checks out” a copy of the code and makes changes to it on a…

    https://www.kdnuggets.com/2015/12/software-development-skills-data-scientists.html

  • 7 Steps to Understanding Deep Learning

    ...mization method which acts to minimize the weights that are subsequently distributed (via backpropagation), in order to minimize the loss function. A common optimization method in deep neural networks is gradient descent. First, read these introductory notes on gradient descent by Marc Toussaint of...

    https://www.kdnuggets.com/2016/01/seven-steps-deep-learning.html

  • Data Science and Cognitive Computing with HPE Haven OnDemand: The Simple Path to Reason and Insight

    ...and more. What’s more, the API returns valuable metadata, such as Wikipedia links and stock ticker symbols, about the found entities. One of the most common text analytics insight tasks is sentiment analysis. Haven OnDemand’s Sentiment Analysis API is able to perform said analysis on supplied text...

    https://www.kdnuggets.com/2016/05/hpe-haven-ondemand-data-science-cognitive-computing.html

  • Mining Twitter Data with Python Part 3: Term Frequencies

    ...n’t usually convey a particular meaning, especially if taken out of context. This is the case of articles, conjunctions, some adverbs, etc. which are commonly called stop-words. In the example above, we can see three common stop-words – to, andand on. Stop-word removal is one important step that...

    https://www.kdnuggets.com/2016/06/mining-twitter-data-python-part-3.html

  • A Data Science Approach to Writing a Good GitHub README

    …then convert all the README’s into a common format for further processing. We chose to convert everything to HTML. When we did this, we also removed common words from a stop word list, and then stemmed the remaining words to find the common base — or root — of each word. We used NLTK Snowball…

    https://www.kdnuggets.com/2016/05/algorithmia-data-science-approach-good-github-readme.html

  • The “Thinking” Part of “Thinking Like A Data Scientist”

    ...ople have a tendency to blindly trust a claim from any source that they deem credible, even if it completely conflicts with their own experiences, or common sense. It only takes a couple stats and lack of common sense to make a dangerous conclusion and claim it’s a fact. It’s harder to buy a gun in...

    https://www.kdnuggets.com/2016/04/thinking-part-thinking-like-data-scientist.html

  • 21 Must-Know Data Science Interview Questions and Answers">2016 Gold Blog21 Must-Know Data Science Interview Questions and Answers

    ...that the results are repeatable with near similar results Examine whether the results reflect local maxima/minima or global maxima/minima   One common way to achieve the above guidelines is through A/B testing, where both the versions of algorithm are kept running on similar environment for a...

    https://www.kdnuggets.com/2016/02/21-data-science-interview-questions-answers.html

  • Deep Learning for Chatbots, Part 1 – Introduction

    ...aluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation researchers find that none of the commonly used metrics really correlate with human judgment. Intention and Diversity A common problem with generative systems is that they tend to...

    https://www.kdnuggets.com/2016/04/deep-learning-chatbots-part-1.html

  • Deep Conversations: Lisha Li, Principal at Amplify Partners

    ...amples are so generalizable across different models without real access to the architecture and more so, the problem you are training on. Given these vulnerabilities, I would like to see more research on, how do you guard against these in a more robust way?   JM: Is there a mathematical way of...

    https://www.kdnuggets.com/2018/05/deep-conversations-lisha-li-amplify-partners.html

  • 5 Best Practices for Big Data Security

    …. If your Big Data project relies on a third-party cloud-based (public or private) solution, then retrospective attack simulation can be used to find vulnerabilities with the third-party application hosted on the cloud. If the attack succeeds, then you should investigate the issue further to find a…

    https://www.kdnuggets.com/2016/06/5-best-practices-big-data-security.html

  • Data Mining finds JASBUG, a Critical Security Vulnerability

    ...s that connect to corporate networks via the public Internet (e.g. from hotels and coffee shops) – are at heightened risk. Unlike recent high-profile vulnerabilities like Heartbleed, Shellshock, Gotofail, and POODLE, this is a design problem, not an implementation problem, making this type of...

    https://www.kdnuggets.com/2015/02/data-mining-simmachines-jasbug-critical-security-vulnerability.html

  • Data Anonymization – History and Key Ideas

    ...nly bug bounty for an anonymization method with the Aircloak Attack Challenge. We encourage privacy experts to test and attack our anonymization. Any vulnerabilities that are identified in our software are immediately investigated, and we release a patch as soon as possible. This ensures that our...

    https://www.kdnuggets.com/2019/10/data-anonymization-history-key-ideas.html

  • Dissecting the Big Data Twitter Community through a Big data Lens

    …ations for the everyday marketer (422) marrying #data to #analytics a major theme at #hp’s conference (153) combining analytics and security to treat vulnerabilities like ants (150) sbi uses big data mining to check defaults biz loss: when state bank of india (141) Following are most tweeted tweets…

    https://www.kdnuggets.com/2015/09/dissecting-big-data-twitter-community.html

  • Interview: Mark Weiner, Temple University Health System on Addressing Healthcare Data Gaps through Advanced Simulation

    ...st or average laboratory parameter. The pace and order of the availability of findings can often be as important as the results. AR: Q8. What are the common myths in healthcare analytics? MW: A great deal of resources are expended to create and purchase analytical tools that are easy enough for...

    https://www.kdnuggets.com/2015/05/interview-mark-weiner-temple-university-healthcare-data.html

  • Big Data generates Big Returns, says VC Roger Ehrenberg

    ...source of competitive advantage, most other firms are well behind. And with the increasing stress on risk management and real-time information about exposures spanning the globe, Wall Street firms are going to have to take some big chances in re-shaping their technical architecture to support a...

    https://www.kdnuggets.com/2013/03/roger-ehrenberg-big-data-generates-big-returns.html

  • Big Data Analytics for Lenders and Creditors

    …n. Analysts apply the scorecard and the pooling definition to a historical data set. The long-run historical averages of the default rate, losses and exposures can then be calculated by pool and used as input into the RWA calculation. There are various ways to group customers into segments using a…

    https://www.kdnuggets.com/2015/10/big-data-analytics-lenders-creditors.html

  • NAIC: Analyst I (Capital Markets) [New York, NY]

    ...portfolios or specific parts of an investment portfolio of insurance companies, identifying specific risks and potential concerns and any significant exposures that could impact insurer solvency Writes and interprets SQL or Access queries for standard as well as ad hoc data mining purposes. Builds...

    https://www.kdnuggets.com/jobs/18/10-29-naic-analyst-capital-markets.html

  • A Non-Compromising Approach to Privacy-Preserving Personalized Services

    ...ers (ISPs) to collect, share and sell their customers' Web browsing data without their consent. All of these private information leakage and identity exposures may result in unexpected harms ranging from persecution by governments to targeted frauds. The onus is now on the users to protect their...

    https://www.kdnuggets.com/2019/01/privacy-preserving-personalized-services.html

  • 3 Big Problems with Big Data and How to Solve Them

    ...authorized insiders. Absent or insufficient security audits. The lack of oversight and/or resources for regular quality security assessments can be a common occurrence. Big data and the systems based on it prove a challenge in themselves, and adding sufficient security check-ups and standards can...

    https://www.kdnuggets.com/2019/04/3-big-problems-big-data.html

  • The Evolution of Build Engineering in Managing Open Source [Webinar Replay]

    ...key trends: massive wide-scale adoption of open source; the most devastating cyber-attacks in recent history tied to unpatched dependencies and other vulnerabilities. Reconciling these trends will enable enterprises to unlock the the potential of open source and mitigate the risks. Further, our...

    https://www.kdnuggets.com/2018/11/activestate-evolution-build-engineering-open-source.html

  • Ethical AI: EU’s New Guidelines and the Future of AI Trustworthiness

    ...itch to immediately cease its operations and delegate control to a human operator? Was a comprehensive risk assessment performed to safeguard against vulnerabilities and cyberattacks? What are the ramifications, and types of harm, that may occur if the system makes an inaccurate prediction? Will...

    https://www.kdnuggets.com/2019/05/ethical-ai-eu-new-guidelines-ai-trustworthiness.html

  • Python 2 End of Life Survey – Are You Prepared?

    ...cations yourself, or find a third-party provider during the end-of-life process Top challenges such as backporting Python 3 security fixes, or fixing vulnerabilities in your code base Survey participants will receive a copy of the results, and have a chance to win a camera drone!   Take the...

    https://www.kdnuggets.com/2019/09/activestate-python-2-end-life-survey-prepared.html

  • Self-Service Data Prep Tools vs Enterprise-Level Solutions? 6 Lessons Learned

    ...ts as the currency for file/data exchange, data manipulation, and reporting? Why is this not headline news? Certainly, no enterprise wants their data vulnerabilities exposed. So, I understand why senior management and IT don’t want to publicize how large and potentially impactful this not-so-secret...

    https://www.kdnuggets.com/2018/08/self-service-data-prep-tools-6-lessons-learned.html

  • Do Conv-nets Dream of Psychedelic Sheep?

    ...oped to understand attribution with spatial activations, and with the recent publication of activation atlases the authors demonstrated insights into vulnerabilities to adversarial examples and strange feature correlations. Activation atlases are the latest in a line of research progressing from...

    https://www.kdnuggets.com/2019/06/conv-nets-dream-psychedelic-sheep.html

  • Data Mining / Analytic News, Aug 2013

    ...ta Mining at Bethesda company, Bethesda, MD; Data Mining Programmer at Real Time Data Solution, Toronto, Canada; DARPA SBIR: Defense Against National Vulnerabilities in Public Data - Aug 23, 2013. Could a modestly funded group deliver nation-state type effects using only public data? This DARPA...

    https://www.kdnuggets.com/2013/08/index.html

  • Interview: Reiner Kappenberger, HP Security Voltage on Security Checklist for Data Architectures

    ...intend to store in your Hadoop environment. Next, perform threat modeling on sensitive data. The goal of threat modeling is to identify the potential vulnerabilities of at-risk data and to know how the data could be used against you if stolen. Then, identify the business-critical values within...

    https://www.kdnuggets.com/2015/07/interview-reiner-kappenberger-hp-security-voltage-checklist.html

  • Big Data Lessons from Microsoft “how-old” Experiment

    …(people routinely being inconvenienced) and false-negatives (threats that always go undetected) are both frequent, costly, and will lead to hazardous vulnerabilities. Unfortunately, we operate in a commercial world where Watson and Deep Blue are forced only through brute force (not through…

    https://www.kdnuggets.com/2015/05/face-numbers-big-data-microsoft-how-old.html

  • AnalyticsStreet Panel Report: Frontiers and Dangers of Analytics and Big Data

    ...a program should do. From a cybersecurity perspective software analytics can be used for both attach and defense operations, detecting and exploiting vulnerabilities in the Big Data space of code that makes up a typical enterprise network. This is an area where I would like to see more commercial...

    https://www.kdnuggets.com/2014/11/analyticsstreet-startup-panel-frontiers-dangers-analytics.html

  • KDnuggets™ News 13:n21, Aug 28

    ...op jobs: Software Developer, Machine Learning at SGI; Data Mining Programmer at Real Time Data Solution Software DARPA SBIR: Defense Against National Vulnerabilities in Public Data - Aug 23, 2013. Could a modestly funded group deliver nation-state type effects using only public data? This DARPA...

    https://www.kdnuggets.com/2013/n21.html

  • How IoT is Jeopardizing Your Business Security

    ...truth is that the extra productivity and cost savings an enterprise gets out of this whole deal is proportional with the increasing number of network vulnerabilities that can be exploited via unsecured endpoints. Read our article on endpoint security here. The Internet of Things or the Internet of...

    https://www.kdnuggets.com/2016/04/iot-jeopardizing-business-security.html

  • Generative Adversarial Networks – Hot Topic in Machine Learning

    …onment of a country with two rival political parties. Each party continuously attempts to improve on its weaknesses while trying to find and leverage vulnerabilities in their adversary to push their agenda. Over time both parties become better operators. As for the impact of RLs and GANs on…

    https://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html

  • Causation in a Nutshell">Gold BlogCausation in a Nutshell

    ...be reciprocal. For instance, A influences B, then B influences A. Brand usage and image frequently interact in a similar fashion. Lagged effects are common in time-series data. The impact of a new ad campaign, for example, may not show up in sales data for several days or weeks. If we analyze data...

    https://www.kdnuggets.com/2018/07/gray-causation-nutshell.html

  • Importance of Data Science for IoT business

    …in IT sector. Data Science is being used in encrypting security applications to ensure data safety from cyber-attacks, malware’s and virus and other vulnerabilities. Financial Sector – Data Science and IoT have come together to improve working in financial sector and make transactions more secure…

    https://www.kdnuggets.com/2015/12/importance-data-science-iot-business.html

  • United Nations University: Data Science Lead

    ...edge products related to (1) methodologies for identifying global, national, and other incidence of the forms of exploitation covered by SDG 8.7; (2) vulnerabilities and risk factors; (3) monitoring and evaluation of programmes related to SDG 8.7; and (4) geographic and/or sectoral typologies or...

    https://www.kdnuggets.com/jobs/17/05-25-united-nations-university-data-science-lead.html

  • The Industries That Can Benefit Most From Predictive Analytics

    ...ial damage. Because predictive analytics often make recommendations on how to improve an environment, cybersecurity professionals may become aware of vulnerabilities they hadn’t been aware of before using data-based tools.   Predictive Analytics as a Competitive Advantage The industries above...

    https://www.kdnuggets.com/2018/07/industries-benefit-most-predictive-analytics.html

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