All (116) | Courses, Education (10) | Meetings (9) | News, Features (16) | Opinions (30) | Top Stories, Tweets (11) | Tutorials, Overviews (32) | Webcasts & Webinars (8)
- Top KDnuggets tweets, Oct 24-30: Building a Question-Answering System from Scratch - Oct 31, 2018.
Also: Implementing Automated Machine Learning Systems with Open Source Tools; New Book: #LinearAlgebra – what you need for Machine Learning and Data Science now; A General Approach to Preprocessing Text Data; Using Deep Learning To Extract Knowledge From Job Descriptions
- Webinar – Integrate AI Across Insurance Operations to Turbocharge Tech Transformation, Nov 14 - Oct 31, 2018.
This Webinar from Insurance Nexus will give you insights into integrating analytics in real-time, turning your vision into reality, satisfying budgetary constraints with incremental technological improvement, and more.
- Labeling Unstructured Text for Meaning to Achieve Predictive Lift - Oct 31, 2018.
In this post, we examine several advance NLP techniques, including: labeling nouns and noun phrases for meaning, labeling (most often) adverbs and adjectives for sentiment, and labeling verbs for intent.
- Cartoon: Halloween Costume for Big Data. - Oct 31, 2018.
We revisit KDnuggets cartoon looking at the appropriate Halloween costume for Big Data and its companion, No Privacy.
- How Machines Understand Our Language: An Introduction to Natural Language Processing - Oct 31, 2018.
The applications of NLP are endless. This is how a machine classifies whether an email is spam or not, if a review is positive or negative, and how a search engine recognizes what type of person you are based on the content of your query to customize the response accordingly.
- In-Depth Training for the Future of Data, Orlando, Nov 11-16 – Save with code KD30 - Oct 30, 2018.
Planning and implementing new data and analytics initiatives can be overwhelming. Join us in Orlando for in-depth training that will give you the needed skills and use code KD30 thru Nov 7 to save.
- New Poll: How Important is Understanding Machine Learning Models? - Oct 30, 2018.
New KDnuggets poll is asking: When building Machine Learning / Data Science models in 2018, how often was it important that the model be humanly understandable/explainable? Please vote
- How to Mitigate Open Source License Risks - Oct 30, 2018.
This whitepaper from ActiveState investigates the various types of OSS licenses, common myths and risks, DIY risk management, the importance of enterprise legal indemnification, and more.
- Key Takeaways from AI Conference SF, Day 2: AI and Security, Adversarial Examples, Innovation - Oct 30, 2018.
Highlights and key takeaways from selected keynote sessions on day 2 of AI Conference San Francisco 2018.
- Stop Installing Tensorflow Using pip for Performance Sake! - Oct 30, 2018.
If you aren’t already using conda, I recommend that you start as it makes managing your data science tools much more enjoyable.
- Amazing consistency: Largest Dataset Analyzed / Data Mined – Poll Results and Trends - Oct 29, 2018.
The poll results show amazing consistency to past years, with median answers still in 10-100 gigabytes range. Really Big Data Scientists (100 Petabytes and more) continue to stand apart, but remain small segment where Asian data scientists lead for the first time in this poll.
- [ebook] Manipulating Data in Apache Spark - Oct 29, 2018.
In this ebook from Databricks, learn how DataFrames leverage the power of distributed processing through Spark, how to make big data processing easier for a wider audience, and more.
- Top Obstacles to Overcome when Implementing Predictive Maintenance - Oct 29, 2018.
We investigate how to create a systematic approach to predictive maintenance, ensuring there's enough data to create accurate systems. This post also explains how to identify a failure source and knowing how to predict it.
- Key Takeaways from AI Conference SF, Day 1: Domain Specific Architectures, Emerging China, AI Risks - Oct 29, 2018.
Highlights and key takeaways include Domain Specific Architectures – the next big thing, Emerging China – evolving from copying ideas to true innovation, and Addressing Risks in AI – Security, Privacy, and Ethics.
- Introduction to Deep Learning with Keras - Oct 29, 2018.
In this article, we’ll build a simple neural network using Keras. Now let’s proceed to solve a real business problem: an insurance company wants you to develop a model to help them predict which claims look fraudulent.
- Top Stories, Oct 22-28: 9 Must-have skills you need to become a Data Scientist, updated; Named Entity Recognition and Classification with Scikit-Learn - Oct 29, 2018.
Also: SQL, Python, & R in One Platform; Generative Adversarial Networks – Paper Reading Road Map; Notes on Feature Preprocessing: The What, the Why, and the How; Graphs Are The Next Frontier In Data Science; 10 Best Mobile Apps for Data Scientist / Data Analysts
- AI Masterpieces: But is it Art? - Oct 27, 2018.
While there’s no doubt that the quality of the results in style transfer is outstanding, many were left with feelings that the technique left little room for the concept of art itself, even calling it “… more of a parlor trick than the next revolution in fine art.”
- SQL, Python, & R in One Platform - Oct 26, 2018.
No more jumping between applications. Mode Studio combines a SQL editor, Python and R notebooks, and a visualization builder in one platform.
- Are you buying an apartment? How to hack competition in the real estate market - Oct 26, 2018.
Many real estate developers use online systems for sales. Things become interesting when all available data is monitored on a weekly basis, and sales progress is analysed.
- Notes on Feature Preprocessing: The What, the Why, and the How - Oct 26, 2018.
This article covers a few important points related to the preprocessing of numeric data, focusing on the scaling of feature values, and the broad question of dealing with outliers.
- Learn how to create data-driven marketing team - Oct 25, 2018.
Check out this upcoming webinar, What Happens When a Marketing Team Turns Data-driven, Nov 26, 2018 at 2:00pm EST, and find the answer to: When your distribution channel is primarily face-to-face, how important is data-driven marketing, when the sale happens offline?
- Naive Bayes from Scratch using Python only – No Fancy Frameworks - Oct 25, 2018.
We provide a complete step by step pythonic implementation of naive bayes, and by keeping in mind the mathematical & probabilistic difficulties we usually face when trying to dive deep in to the algorithmic insights of ML algorithms, this post should be ideal for beginners.
- Named Entity Recognition and Classification with Scikit-Learn - Oct 25, 2018.
Named Entity Recognition and Classification is a process of recognizing information units like names, including person, organization and location names, and numeric expressions from unstructured text. The goal is to develop practical and domain-independent techniques in order to detect named entities with high accuracy automatically.
- Implementing Automated Machine Learning Systems with Open Source Tools - Oct 25, 2018.
What if you want to implement an automated machine learning pipeline of your very own, or automate particular aspects of a machine learning pipeline? Rest assured that there is no need to reinvent any wheels.
- Top KDnuggets tweets, Oct 17-23: Machine Learning Cheat Sheets - Oct 24, 2018.
Also 5 "Clean Code" Tips That Will Dramatically Improve Your Productivity; Data Science Cheat Sheet.
- New Book: Linear Algebra – what you need for Machine Learning and Data Science now - Oct 24, 2018.
From machine learning and data science to engineering and finance, linear algebra is an important prerequisite for the careers of today and of the future. Learn the math you need with this book.
- How I Learned to Stop Worrying and Love Uncertainty - Oct 24, 2018.
This is a written version of Data Scientist Adolfo Martínez’s talk at Software Guru’s DataDay 2017. There is a link to the original slides (in Spanish) at the top of this post.
- Generative Adversarial Networks – Paper Reading Road Map - Oct 24, 2018.
To help the others who want to learn more about the technical sides of GANs, I wanted to share some papers I have read in the order that I read them.
- Building a Question-Answering System from Scratch - Oct 24, 2018.
This part will focus on introducing Facebook sentence embeddings and how it can be used in building QA systems. In the future parts, we will try to implement deep learning techniques, specifically sequence modeling for this problem.
- A Deep Look at Deep Learning: Understanding The Basics of How (and Why) it Works - Oct 23, 2018.
In this illustrated guide by Dataiku you'll learn what exactly deep learning is and why its growing and why it can be more powerful than classical machine learning (ML).
- Google, Microsoft & Fraunhofer at the First European Edition of Deep Learning World – 12 Nov, 2018 - Oct 23, 2018.
You can attend the premier conference covering the commercial deployment of Deep Learning, Nov 12, Berlin, and learn from top industry experts, from our first class conference programme, and from reputable companies to become the best in what you're doing!
- Introduction to Active Learning - Oct 23, 2018.
An extensive overview of Active Learning, with an explanation into how it works and can assist with data labeling, as well as its performance and potential limitations.
- Get a 2–6x Speed-up on Your Data Pre-processing with Python - Oct 23, 2018.
Get a 2–6x speed-up on your pre-processing with these 3 lines of code!
- Speak at Mega-PAW Vegas 2019 – on Machine Learning Deployment (Apply by Nov 15) - Oct 22, 2018.
Presenting at PAW is a fulfilling way to engage with the leading members of the machine learning community, offers a chance to share how predictive analytics delivers an impact for your organization, and provides complimentary registration/access to the PAW event.
- Don’t miss Big Data LDN 2018, 13-14 November - Oct 22, 2018.
Big Data LDN is the UK’s largest free to attend data and analytics conference & exhibition and will take place on 13-14 November 2018 at Olympia London. The event is essential for those wanting to build an bright data-driven future for their business.
- How to Define a Machine Learning Problem Like a Detective - Oct 22, 2018.
The common refrain among machine learning practitioners is that it’s as much an art as a science. True enough, but in this discipline, you can only appreciate the former if you understand the latter.
- Beginner Data Visualization & Exploration Using Pandas - Oct 22, 2018.
This tutorial will offer a beginner guide into how to get around with Pandas for data wrangling and visualization.
- Top Stories, Oct 15-21: Graphs Are The Next Frontier In Data Science; The Main Approaches to Natural Language Processing Tasks - Oct 22, 2018.
Also: GitHub Python Data Science Spotlight; The Intuitions Behind Bayesian Optimization with Gaussian Processes; 10 Best Mobile Apps for Data Scientist / Data Analysts; Apache Spark Introduction for Beginners
- Dr. Data Show Video: How Can You Trust AI? - Oct 20, 2018.
This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics.
- Start your journey into data science today - Oct 19, 2018.
Springboard’s Introduction to Data Science Course will help you build a strong foundation in R programming, communicate effectively by telling a story with data, clean and analyze large datasets, and more. Apply before Oct 22 and use code KDNUGGETSOCT500 for $500 Data Science Career Track.
- Will Models Rule the World? Data Science Salon Miami, Nov 6-7 - Oct 19, 2018.
This post is excerpted from the thoughts of Data Science Salon Miami speakers on the future of model-based decision-making.
- Holy Grail of AI for Enterprise — Explainable AI - Oct 19, 2018.
Explainable AI (XAI) is an emerging branch of AI where AI systems are made to explain the reasoning behind every decision made by them. We investigate some of its key benefits and design principles.
- The Intuitions Behind Bayesian Optimization with Gaussian Processes - Oct 19, 2018.
Bayesian Optimization adds a Bayesian methodology to the iterative optimizer paradigm by incorporating a prior model on the space of possible target functions. This article introduces the basic concepts and intuitions behind Bayesian Optimization with Gaussian Processes.
- McKinsey Datathon: The City Cup
17 November, Amsterdam, Stockholm and Zurich. Apply Now - Oct 19, 2018.While solving the challenge, you will gain insights into the types of problems that McKinsey Data Scientists solve daily to help their clients. Top prize is 5K Euro + conference attendance of your choice.
- How to Solve the ModelOps Challenge - Oct 18, 2018.
A recent study shows that while 85% believe data science will allow their companies to obtain or sustain a competitive advantage, only 5% are using data science extensively. Join this webinar, Nov 14, to find out why.
- BI to AI: Getting Intelligent Insights to Everyone, Oct 30 - Oct 18, 2018.
Tellius is a Search and AI-Powered Analytics Platform that makes it easy for users to ask questions of their business data and discover insights using machine learning, with just a single click.
- Apache Spark Introduction for Beginners - Oct 18, 2018.
An extensive introduction to Apache Spark, including a look at the evolution of the product, use cases, architecture, ecosystem components, core concepts and more.
- New Jobs Sure to Emerge Alongside Artificial Intelligence - Oct 18, 2018.
There’s a lot of doomsaying about AI pushing humans out of jobs and destroying entire industries. Is it as bad as all that? Maybe not!
- Graphs Are The Next Frontier In Data Science - Oct 18, 2018.
GraphConnect 2018, Neo4j’s bi-annual conference, was held in New York City in mid-September. Read about what happened, and why graphs are the next big thing in data science.
- Top KDnuggets tweets, Oct 10-16: 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning; 6 Books Every Data Scientist Should Keep Nearby - Oct 17, 2018.
Also Machine Learning Cheat Sheets; Top 8 Free Must-Read Books on Deep Learning.
- Citizen Data Scientists | Why Not DIY AI? - Oct 17, 2018.
This live debate will feature AutoML advocate Gene Ferruzza (Valassis Digital), countered by Gregory Piatetsky-Shapiro (KDnuggets) who asks if you would really fly with a citizen pilot. Fasten your seatbelts and register now - seats are limited!
- The Definitive Guide to AI’s “Black Box” Problem - Oct 17, 2018.
The Amazing, Anti-Jargon, Insight Filled, and Totally Free Handbook to Integrating AI in Highly Regulated Industries - get it now.
- Music for Data Scientists? Music by Data Scientists? …What…?! - Oct 17, 2018.
Introducing Mean Reversion, an NYC-based songwriting duo comprising of data scientist Foster Provost and statistician Cliff Hurvich.
- The Main Approaches to Natural Language Processing Tasks - Oct 17, 2018.
Let's have a look at the main approaches to NLP tasks that we have at our disposal. We will then have a look at the concrete NLP tasks we can tackle with said approaches.
- [Webinar] Neural Network Fundamentals - Oct 16, 2018.
In this webinar, Oct 25, 2018, 10:00 am PST, we will apply your convolutional neural network using the ImageNet scenario. We will also review some of the ImageNet architectures and how convolutions work.
- Accelerating Your Algorithms in Production [Webinar Replay] - Oct 16, 2018.
Numerical algorithms are computationally demanding, which makes performance an important consideration when using Python for machine learning, especially as you move from desktop to production.
- Adversarial Examples, Explained - Oct 16, 2018.
Deep neural networks—the kind of machine learning models that have recently led to dramatic performance improvements in a wide range of applications—are vulnerable to tiny perturbations of their inputs. We investigate how to deal with these vulnerabilities.
- Applied Data Science: Solving a Predictive Maintenance Business Problem Part 3 - Oct 16, 2018.
In this post we will expand our analysis to multiple variables and then see how intuitions we develop during the exploration phase, can lead to generating new features for modelling.
- GitHub Python Data Science Spotlight: High Level Machine Learning & NLP, Ensembles, Command Line Viz & Docker Made Easy - Oct 16, 2018.
This post spotlights 5 data science projects, all of which are open source and are present on GitHub repositories, focusing on high level machine learning libraries and low level support tools.
- Bring Essential Data Governance Training to Your Team - Oct 15, 2018.
Technology trends such as big data, cloud, self-service, and agile challenge traditional data governance practices. TDWI Onsite Education sends top-rated instructors to teach the skills you need at your location. Discounts available for training scheduled prior to Dec 31, 2018.
- DATAx Guide to Data Visualization in 2019 - Oct 15, 2018.
Get DATAx Guide to Data Visualization in 2019, the definitive foundation to help you prepare for the future of data visualization, AI and machine learning. Also use KD200 to get extra $200 off DATAx New York early bird pricing until Oct 19.
- Select Your Analytics Adventure – Analytics On-boarding - Oct 15, 2018.
Lower the barriers to productivity by employing a “Choose your own adventure” approach to on-boarding your new analytics team members.
- Top Stories, Oct 8-14: 10 Best Mobile Apps for Data Scientist / Data Analysts; BIG, small or Right Data: Which is the proper focus? - Oct 15, 2018.
Also: How To Learn Data Science If You’re Broke; Top 8 Python Machine Learning Libraries; 9 Must-have skills you need to become a Data Scientist, updated; SQL, Python, & R: All in One Platform; Using Confusion Matrices to Quantify the Cost of Being Wrong
- 5 “Clean Code” Tips That Will Dramatically Improve Your Productivity - Oct 15, 2018.
TL;DR: If it isn’t tested, it’s broken; Choose meaningful names; Classes and functions should be small and obey the Single Responsibility Principle (SRP); Catch and handle exceptions, even if you don’t think you need to; Logs, logs, logs
- Learn the top things to look for in an AI Vendor - Oct 12, 2018.
In this webinar, Oct 25, find out why it's so hard to choose an AI vendor and show you what to consider as you seek a partner in AI. Additionally, get the details of how successfully implemented AI technologies at Wellen have transformed the way the company does business.
- Machine learning — Is the emperor wearing clothes? - Oct 12, 2018.
We take a look at the core concepts of Machine Learning, including the data, algorithm and optimization needed to get you started, with links to additional resources to help enhance your knowledge.
- New Poll: What was the largest dataset you analyzed / data mined? - Oct 12, 2018.
New KDnuggets Poll is asking: What was the largest dataset you analyzed / data mined? Please vote and we will analyze the trends and publish the results.
- We Sized Washington’s Edible Marijuana Market Using AI - Oct 12, 2018.
As Canada legalizes marijuana, it might look to Washington to understand how pot sells. With the RAND Corp., we used machine learning to estimate how much THC- in pot is sold in Washington.
- Join us for DataTech19, Scotland’s first technical data science conference as part of DataFest - Oct 12, 2018.
DataTech19 will take place on 14th March in Edinburgh, at the National Museum of Scotland, bringing together analysts, developers, engineers and scientists from the public sector, academia and industry for technical presentations and discussions of important topics in data science.
- SQL, Python, & R: All in One Platform - Oct 11, 2018.
Mode Studio connects a SQL editor, Python and R notebooks, and a visualization builder in one platform. Sign up now for access.
- Machine Reading Comprehension: Learning to Ask & Answer - Oct 11, 2018.
Investigating the dual ask-answer network, covering the embedding, encoding, attention and output layer, as well as the loss function, with code examples to help you get started.
- Using Confusion Matrices to Quantify the Cost of Being Wrong - Oct 11, 2018.
The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors.
- Evaluating the Business Value of Predictive Models in Python and R - Oct 11, 2018.
In these blogs for R and python we explain four valuable evaluation plots to assess the business value of a predictive model. We show how you can easily create these plots and help you to explain your predictive model to non-techies.
- Top KDnuggets tweets, Oct 3–9: 5 Reasons Logistic Regression should be the first thing you learn when becoming a Data Scientist - Oct 10, 2018.
Also: Cheat sheet: Deep learning losses & optimizers; Journey to Machine Learning – 100 Days of ML Code; Math for Machine Learning; Essential Math for Data Science: ‘Why’ and ‘How’
- TDWI In-Person and Virtual Data and Analytics Training - Oct 10, 2018.
TDWI education is designed to bring you from foundational concepts and best practices to hands-on skills and ideation so you can ultimately put your knowledge to work back in the office, immediately. Save 20% when you register for one of our 2018 Seminars through October 19. Use Code KD20.
- Data Mining Book: Chapter Download. - Oct 10, 2018.
Download this immediately useful book chapter, and learn how to create derived variables, which allow the statistical and Data Science modeling to incorporate human insights.
- Top September Stories: Essential Math for Data Science: Why and How; Machine Learning Cheat Sheets - Oct 10, 2018.
Also: Journey to Machine Learning - 100 Days of ML Code; How many data scientists are there and is there a shortage?
- 10 Best Mobile Apps for Data Scientist / Data Analysts - Oct 10, 2018.
A collection of useful mobile applications that will help enhance your vital data science and analytic skills. These free apps can improve your listening abilities, logical skills, basic leadership qualities and more.
- Preprocessing for Deep Learning: From covariance matrix to image whitening - Oct 10, 2018.
The goal of this post/notebook is to go from the basics of data preprocessing to modern techniques used in deep learning. My point is that we can use code (Python/Numpy etc.) to better understand abstract mathematical notions!
- New Online MS in Business Analytics from Drexel - Oct 9, 2018.
With Drexel University’s online MS in Business Analytics program, you’ll be able to effectively analyze this data to give your company and yourself a competitive edge.
- TEXATA Data Analytics Summit 2018 – Exclusive 30% KDnuggets Discount. - Oct 9, 2018.
The 4th Annual TEXATA Summit is only 2 weeks away! Join us on Friday October 19th in Austin, Texas to learn and connect with your fellow Industry Leaders.
- Top 8 Python Machine Learning Libraries - Oct 9, 2018.
Part 1 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
- How To Learn Data Science If You’re Broke - Oct 9, 2018.
A first-hand account on how to learn data science on a budget, with advice covering useful resources, a recommended curriculum, typical concepts, building a portfolio and more.
- Semantic Interoperability: Are you training your AI by mixing data sources that look the same but aren’t? - Oct 9, 2018.
Semantic interoperability is a challenge in AI systems, especially since data has become increasingly more complex. The other issue is that semantic interoperability may be compromised when people use the same system differently.
- Building an Image Classifier Running on Raspberry Pi - Oct 9, 2018.
The tutorial starts by building the Physical network connecting Raspberry Pi to the PC via a router. After preparing their IPv4 addresses, SSH session is created for remotely accessing of the Raspberry Pi. After uploading the classification project using FTP, clients can access it using web browsers for classifying images.
- Big career opportunities in big data - Oct 8, 2018.
Are you ready to take on a leadership role in your career? Prepare yourself with a Master's Degree in Data Analytics offered online through Penn State World Campus. Applications for spring 2019 are due Thursday, November 15.
- BIG, small or Right Data: Which is the proper focus? - Oct 8, 2018.
For most businesses, having and using big data is either impossible, impractical, costly to justify, or difficult to outsource due to the over demand of qualified resources. So, what are the benefits of using small data?
- Top Stories, Oct 1-7: Machine Learning Cheat Sheets; How to Create a Simple Neural Network in Python - Oct 8, 2018.
Also: Recent Advances for a Better Understanding of Deep Learning; Basic Image Data Analysis Using Python – Part 4; A Concise Explanation of Learning Algorithms with the Mitchell Paradigm; Essential Math for Data Science: Why and How
- Things you should know when traveling via the Big Data Engineering hype-train - Oct 8, 2018.
Maybe you want to join the Big Data world? Or maybe you are already there and want to validate your knowledge? Or maybe you just want to know what Big Data Engineers do and what skills they use? If so, you may find the following article quite useful.
- Online Master’s in Applied Data Science From Syracuse - Oct 5, 2018.
Syracuse University offers an online Master’s in Applied Data Science designed for data science professionals who are interested in advancing their technical data management and application skills without relocating. Apply by October 12.
- Basic Image Data Analysis Using Python – Part 4 - Oct 5, 2018.
Accessing the internal component of digital images using Python packages helps the user understand its properties, as well as its nature.
- Why do I Call Myself a Data Scientist? - Oct 5, 2018.
Claimed as the “sexiest job of the 21st Century” here I’ll discuss the reasons for my proclamation as a Data Scientist, beyond the hype.
- A Concise Explanation of Learning Algorithms with the Mitchell Paradigm - Oct 5, 2018.
A single quote from Tom Mitchell can shed light on both the abstract concept and concrete implementations of machine learning algorithms.
- Understand Why ODSC is the Most Recommended Conference for Applied Data Science - Oct 4, 2018.
Running 4 days, 40 training sessions, 50 workshops, and over 200 speakers, an ODSC conference offers unparalleled depth and breadth in deep learning, machine learning, and other data science topics. Save 20% offer ends tomorrow. Register now!
- Big Data Day Camp: Big Data Tools & Techniques (October 25-26) - Oct 4, 2018.
Learn how to use data to make wise, actionable data driven decisions! Our first 2-day camp, Big Data Tools & Techniques, is October 25-26 at Qualcomm Institute, UCSD.
- Semantic Segmentation: Wiki, Applications and Resources - Oct 4, 2018.
An extensive overview covering the features of Semantic Segmentation and possible uses for it, including GeoSensing, Autonomous Drive, Facial Recognition and more.
- 3 Stages of Creating Smart - Oct 4, 2018.
The technology is advancing at a pace that should enable any company to create “smart” products, things and spaces. But how does one go about actually creating smart?
- Society of Machines: The Complex Interaction of Agents - Oct 4, 2018.
In this new series we’ll focus on collective interaction of two or more machines. This interaction of machines can be among each other or with the environment.
- Top KDnuggets tweets, Sep 26 – Oct 2: Why building your own Deep Learning Computer is 10x cheaper than AWS; 6 Steps To Write Any Machine Learning Algorithm - Oct 3, 2018.
Also: New Book: Math for #MachineLearning; How to visualize decision tree; Causation in a Nutshell; Machine Learning Cheat Sheets; Brief History of Machine Learning Models Explainability
- Data Science at Northwestern - Oct 3, 2018.
In Northwestern's Online MS in Data Science, you’ll learn from an accomplished faculty of leading industry experts, choose from a wide range of specializations and electives to suit your goals, and earn your master’s degree entirely online.
- Top 3 Trends in Deep Learning - Oct 3, 2018.
We investigate the intermediate stage of deep learning, and the trends that are emerging in response to the challenges at this stage, including Interoperability and the multi-deployment options.
- Linear Regression in the Wild - Oct 3, 2018.
We take a look at how to use linear regression when the dependent variables have measurement errors.
- Sequence Modeling with Neural Networks – Part I - Oct 3, 2018.
In the context of this post, we will focus on modeling sequences as a well-known data structure and will study its specific learning framework.
- Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: October and Beyond - Oct 3, 2018.
Coming soon: INFORMS Chicago, Deep Learning Summit Toronto, ODSC West SF, Accelerate AI SF, Big Data Week London, AI Conference London, PAW Business London, Crunch Budapest, and more.
- Unleash a Faster Python on Your Data - Oct 2, 2018.
Intel provides optimized Scikit-learn, the most used Python package for classical machine learning. Get faster scikit-learn through Intel® Distribution for Python*
- DevOps 2.0: Applying Machine Learning in the CI/CD Chain - Oct 2, 2018.
Explore how ML can be implemented in your organization, so you can (for example) enable the automated assessment of test results for far more complex criteria, such as defining thresholds based on statistical significance rather than just presence/absence of specific criteria.
- How to Create a Simple Neural Network in Python, by Dr. Michael J. Garbade - Oct 2, 2018.
The best way to understand how neural networks work is to create one yourself. This article will demonstrate how to do just that.
- 5 Reasons Why You Should Use Cross-Validation in Your Data Science Projects - Oct 2, 2018.
In cross-validation, we do more than one split. We can do 3, 5, 10 or any K number of splits. Those splits called Folds, and there are many strategies we can create these folds with.
- Top Stories, Sep 24-30: Machine Learning Cheat Sheets; Learning the Mathematics of Machine Learning - Oct 1, 2018.
Also: Math for Machine Learning; Introducing Path Analysis Using R; Introduction to Deep Learning; Essential Math for Data Science: Why and How; 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study
- Learn Business Analytics at Clark University – affordable excellence - Oct 1, 2018.
Move your career forward in one of the fields with the largest demand. Business Analytics at Clark University will give you the skills employers demand by teaching you how to synthesize data into powerful information.
- Dr. Data Show Video: Why Machine Learning Is the Coolest Science - Oct 1, 2018.
This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics.
- TEXATA Data Analytics Summit 2018 – Exclusive 30% KDnuggets Discount - Oct 1, 2018.
The 4th Annual TEXATA Summit is only 3 weeks away! Join us on Friday October 19th in Austin, Texas to learn and connect with your fellow Industry Leaders.
- Robust Quality – Powerful Integration of Data Science and Process Engineering - Oct 1, 2018.
The book by Rajesh Jugulum provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies.
- A Right to Reasonable Inferences - Oct 1, 2018.
As shown in this paper, individuals are granted little control over how their personal data is used to draw inferences about them. Compared to other types, inferences are effectively ‘economy class’ personal data in the General Data Protection Regulation (GDPR).
- Recent Advances for a Better Understanding of Deep Learning - Oct 1, 2018.
A summary of the newest deep learning trends, including Non Convex Optimization, Overparametrization and Generalization, Generative Models, Stochastic Gradient Descent (SGD) and more.
- More Effective Transfer Learning for NLP - Oct 1, 2018.
Until recently, the natural language processing community was lacking its ImageNet equivalent — a standardized dataset and training objective to use for training base models.