All (99) | Courses, Education (4) | Meetings (10) | News, Features (11) | Opinions, Interviews (33) | Top Stories, Tweets (10) | Tutorials, Overviews (26) | Webcasts & Webinars (5)
- Train AI 2018: Garry Kasparov, Andrej Karpathy keynotes and the best in AI - Apr 30, 2018.
On May 9th, Figure Eight's co-founder Lukas Biewald will lead an Introduction to Deep Learning workshop in San Francisco. Don't miss this chance to learn from the best in AI. Use code KDNuggets30 to get 30% off the registration fee!
- Operational Machine Learning: Seven Considerations for Successful MLOps - Apr 30, 2018.
In this article, we describe seven key areas to take into account for successful operationalization and lifecycle management (MLOps) of your ML initiatives
- Top Stories, Apr 23-29: Blockchain Explained in 7 Python Functions; Building Convolutional Neural Network using NumPy from Scratch - Apr 30, 2018.
Also: Choosing the Right Metric for Evaluating Machine Learning Models – Part 1; Top 16 Open Source Deep Learning Libraries and Platforms; Data Science Interview Guide; Why so many data scientists are leaving their jobs
- How to Make AI More Accessible - Apr 30, 2018.
I recently was a guest speaker at the Stanford AI Salon on the topic of accessiblity in AI, which included a free-ranging discussion among assembled members of the Stanford AI Lab. There were a number of interesting questions and topics, so I thought I would share a few of my answers here.
- Ultra-compact workstation for top deep learning frameworks - Apr 27, 2018.
For workstation development platforms purpose-built for Tensorflow, PyTorch, Caffe2, MXNet, and other DL frameworks, the solution is BOXX. We're bringing deep learning to your deskside with the all-new APEXX W3!
- What should be focus areas for Machine Learning / AI in 2018? - Apr 27, 2018.
This article looks at what are the recent trends in data science/ML/AI and suggests subareas DS groups need to focus on.
- Choosing the Right Metric for Evaluating Machine Learning Models – Part 1 - Apr 27, 2018.
Each machine learning model is trying to solve a problem with a different objective using a different dataset and hence, it is important to understand the context before choosing a metric.
- Blockchain Explained in 7 Python Functions - Apr 27, 2018.
It wasn’t until I wrote my own simple Blockchain, that I truly understood what it is and the potential applications for it. So without further ado, lets set up our 7 functions!
- Hear the latest AI advancements in robotics & automation from Uber, Hitachi, Google & more - Apr 26, 2018.
The Summits will bring together 550 experts and 60 speakers using AI and deep learning to improve operations in manufacturing, and creating the next generation of intelligent robots. Save 20% with code KDNUGGETS.
- Machine Learning Engineer, Researcher, Data Scientist have the highest job satisfaction - Apr 26, 2018.
KDnuggets poll finds that Machine Learning Engineer, Researcher, and Data Scientist have the highest job satisfaction. Job satisfaction usually starts high, but drops significantly after 4 years on the job. Data professionals in Asia and Latin America are most unsatisfied.
- The Dirty Little Secret Every Data Scientist Knows (but won’t admit) - Apr 26, 2018.
Most people don’t realize, but the actual “fancy” machine learning algorithm is like the last mile of the marathon. There is so much that must be done before you get there!
- Doing Real-Time Data Analysis With Db2 Event Store - Apr 26, 2018.
IBM unveiled the updated Db2 Event Store platform and various features at its Think 2018 conference, tailored for those in the data industry, including data scientists, and application developers.
- Building Convolutional Neural Network using NumPy from Scratch - Apr 26, 2018.
In this article, CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), ReLU, and max pooling.
- Modernize your data infrastructure with Looker + AWS - Apr 25, 2018.
Learn how you can improve performance and optimize resources using Looker + AWS and Amazon Redshift with Looker extensive pre-built analytics models for AWS data. As a bonus, we will give 1K credit towards AWS data warehouse.
- Top KDnuggets tweets, Apr 18-24: Top 20 Python #AI and #MachineLearning Open Source Projects; 7 Books to Grasp Mathematical Foundations of #DataScience - Apr 25, 2018.
Also: #Python Regular Expressions Cheat Sheet; Monte-Carlo Search for Magic: The Gathering; NLP – Building a Question Answering Model
- Bitcoin Trade Signals - Apr 25, 2018.
This article covers the transformation of public emotions, big news and blockchain data into signals which can provide us with a better understanding as well as instructions for investing.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model - Apr 25, 2018.
The GloVe model stands for Global Vectors which is an unsupervised learning model which can be used to obtain dense word vectors similar to Word2Vec.
- Data Science Interview Guide - Apr 25, 2018.
Traditionally, Data Science would focus on mathematics, computer science and domain expertise. While I will briefly cover some computer science fundamentals, the bulk of this blog will mostly cover the mathematical basics one might either need to brush up on (or even take an entire course).
- ML Powering Marketing Automation: New Guidebook - Apr 24, 2018.
Understanding and quantifying a customer's journey - otherwise known as marketing attribution - is essential for marketers to analyze the ROI from campaigns. Get the latest guidebook to understand how its done!
- Top 16 Open Source Deep Learning Libraries and Platforms - Apr 24, 2018.
We bring to you the top 16 open source deep learning libraries and platforms. TensorFlow is out in front as the undisputed number one, with Keras and Caffe completing the top three.
- How I Unknowingly Contributed To Open Source - Apr 24, 2018.
This article explains what is meant by the term 'open source' and why all data scientists should be a part of it.
- KDnuggets Recognized as a Top Data Science Influencer for 2018 - Apr 24, 2018.
Check out Onalytica's Data Science Influencers Report for 2018, and see where KDnuggets (and others) were ranked.
- Swiftapply – Automatically efficient pandas apply operations - Apr 24, 2018.
Using Swiftapply, easily apply any function to a pandas dataframe in the fastest available manner.
- Low Prices for Mega-PAW End Friday – Predictive Analytics World & Deep Learning World in Vegas - Apr 23, 2018.
This Friday, April 27 is the regular pricing deadline for Mega-PAW 2018, June 3-7 in Las Vegas. Don't miss your chance to save up to $450.00 when you register for the 2018 event.
- Data Exchange and Marketplace, a New Business Model in Making - Apr 23, 2018.
This article covers how an ever-increasing amount of data will trigger the evolution of a new ecosystem that will spur entrepreneurial activity, offering an opportunity to start a wide range of new businesses.
- Top Stories, Apr 16-22: 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning; Python Regular Expressions Cheat Sheet - Apr 23, 2018.
Also: Key Algorithms and Statistical Models for Aspiring Data Scientists; Why Deep Learning is perfect for NLP; Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step; Top 8 Free Must-Read Books on Deep Learning
- Introducing the Anaconda Data Science Certification Program - Apr 20, 2018.
This program gives data scientists a way to verify their proficiency, and organizations an independent standard for qualifying current and prospective data science experts. Register now!
- Why Deep Learning is perfect for NLP (Natural Language Processing) - Apr 20, 2018.
Deep learning brings multiple benefits in learning multiple levels of representation of natural language. Here we will cover the motivation of using deep learning and distributed representation for NLP, word embeddings and several methods to perform word embeddings, and applications.
- Neural Network based Startup Name Generator - Apr 20, 2018.
How to build a recurrent neural network to generate suggestions for your new company’s name.
- NLP – Building a Question Answering Model - Apr 20, 2018.
In this blog, I want to cover the main building blocks of a question answering model.
- Understanding What is Behind Sentiment Analysis – Part 2 - Apr 20, 2018.
Fine-tuning our sentiment classifier...
- Leverage the Power of Data-Literacy - Apr 19, 2018.
Optimizing your business for AI success is the only way to leverage its growing power; data-literacy represents the foundation of that optimization.
- Let’s Admit It: We’re a Long Way from Using “Real Intelligence” in AI - Apr 19, 2018.
With the growth of AI systems and unstructured data, there is a need for an independent means of data curation, evaluation and measurement of output that does not depend on the natural language constructs of AI and creates a comparative method of how the data is processed.
- Presto for Data Scientists – SQL on anything - Apr 19, 2018.
Presto enables data scientists to run interactive SQL across multiple data sources. This open source engine supports querying anything, anywhere, and at large scale.
- Python Regular Expressions Cheat Sheet - Apr 19, 2018.
The tough thing about learning data is remembering all the syntax. While at Dataquest we advocate getting used to consulting the Python documentation, sometimes it's nice to have a handy reference, so we've put together this cheat sheet to help you out!
- Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step - Apr 19, 2018.
What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.
- Top KDnuggets tweets, Apr 11-17: Boost your #datascience skills. Learn linear algebra. - Apr 18, 2018.
Also: Don’t learn #MachineLearning in 24 hours; Top 8 Free Must-Read Books on #DeepLearning; How Attractive Are You in the Eyes of Deep #NeuralNetwork?; Ten #MachineLearning Algorithms You Should Know to Become a #DataScientist
- Wharton: Successful Applications of Customer Analytics – May 9-10, Philadelphia - Apr 18, 2018.
The WCAI annual conference, Successful Applications of Customer Analytics is dedicated to real-world applications that balance high-level rigor and business know-how, and to elevating the role of analytics in an organization strategic decision-making.
- Hedge Yourself From a Risky Data Science Job - Apr 18, 2018.
This article covers why it's important to consider all the factors when being hired as a data scientist.
- Deep Learning With Apache Spark: Part 1 - Apr 18, 2018.
First part on a full discussion on how to do Distributed Deep Learning with Apache Spark. This part: What is Spark, basics on Spark+DL and a little more.
- [ebook] 7 Steps for a Developer to Learn Apache Spark - Apr 17, 2018.
We offer a step-by-step guide to technical content and related assets that to help you learn Apache Spark, whether you're getting started with Spark or are an accomplished developer.
- Join AI pioneers and insurance innovators – Chubb, MetLife, Hippo and many more! - Apr 17, 2018.
Insurance AI & Analytics USA Summit is bringing together 400+ insurance innovators and AI pioneers to discuss the operational advantages of rolling out AI in your business, and then delve into the specifics of pricing, marketing, claims and underwriting. Save with code KDNuggets.
- 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning - Apr 17, 2018.
It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.
- Robust Word2Vec Models with Gensim & Applying Word2Vec Features for Machine Learning Tasks - Apr 17, 2018.
The gensim framework, created by Radim Řehůřek consists of a robust, efficient and scalable implementation of the Word2Vec model.
- Role of IoT in Education - Apr 17, 2018.
In this article, I will discuss the significance of IoT and gain insights on why this technology is becoming an integral part of the daily learning and teaching methodologies.
- When Do We Trust Machines? - Apr 16, 2018.
We propose a framework of "trust heatmap", show how the trust in machines depends on two key elements: their error rate and the costs of mistakes, and examine the automation frontier.
- Online Master’s in Data Science from Northwestern - Apr 16, 2018.
Build essential technical, analytical, and leadership skills for today's data-driven world in Northwestern’s online MASTER OF SCIENCE IN DATA SCIENCE program.
- Top Stories, Apr 9-15: Top 8 Free Must-Read Books on Deep Learning; Why so many data scientists are leaving their jobs - Apr 16, 2018.
Also: Ten Machine Learning Algorithms You Should Know to Become a Data Scientist; Top 10 Technology Trends of 2018; 12 Useful Things to Know About Machine Learning
- Can’t-Miss Keynotes at PAW Manufacturing, plus 3 other PAWs in Vegas – Save ’til April 27 - Apr 16, 2018.
Don't miss the opportunity to witness keynote sessions by industry heavyweights at the upcoming Predictive Analytics World for Manufacturing conference in Las Vegas. Save hundreds by registering by April 27.
- Key Algorithms and Statistical Models for Aspiring Data Scientists - Apr 16, 2018.
This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques.
- Are High Level APIs Dumbing Down Machine Learning? - Apr 16, 2018.
Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?
- Don’t learn Machine Learning in 24 hours - Apr 13, 2018.
When it comes to machine learning, there's no quick way of teaching yourself - you're in it for the long haul.
- Top 10 Technology Trends of 2018 - Apr 13, 2018.
In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.
- Understanding What is Behind Sentiment Analysis – Part 1 - Apr 13, 2018.
Build your first sentiment classifier in 3 steps.
- Machine Learning Engineer, Data Scientist among the best US Jobs in 2018 - Apr 12, 2018.
Machine Learning Engineer, with avg. salary of $136K and Data Scientist, with avg. salary $133K are among the top US jobs in 2018, according to job site Indeed.
- Unlock the Next Era of Analytics – AI and Machine Learning at Scale - Apr 12, 2018.
Join us on Apr 19 for an interactive virtual event to hear from a panel of analytic experts as they dispel the myths and dive into the nitty-gritty of how AI and machine learning will impact analytic teams.
- Onboarding Your Machine Learning Program - Apr 12, 2018.
Machine Learning's popularity is continuing to grow and has engraved itself in pretty much every industry. This article contains lessons from a data scientist on how to unlock it's full potential.
- 12 Useful Things to Know About Machine Learning - Apr 12, 2018.
This is a summary of 12 key lessons that machine learning researchers and practitioners have learned include pitfalls to avoid, important issues to focus on and answers to common questions.
- Top KDnuggets tweets, Apr 04-10: Introduction to Markov Chains - Apr 11, 2018.
Also: 5 Things to Know About #MachineLearning; A "Weird" Intro to #DeepLearning; How Do I Get My First #DataScience Job?
- Get a headstart with Looker and 1K credits for your AWS data warehouse - Apr 11, 2018.
Looker partnered with AWS to offer, for a limited time, a free trial of Looker with a bonus of $1,000 credits towards your AWS data warehouse.
- Introducing Drexel New Online MS in Business Analytics – Get the Edge - Apr 11, 2018.
Only small fraction of data is actually analyzed, with text, audio, and video largely unused. Drexel online MS in Business Analytics will teach you to analyze this overlooked data to give your company and yourself a competitive edge.
- AI in Healthcare - Apr 11, 2018.
AI is completely transforming every corner of the healthcare industry. Don’t miss out on IEN’s 2nd Annual AI in Healthcare Summit, Jun 11-12, in San Francisco.
- Ten Machine Learning Algorithms You Should Know to Become a Data Scientist - Apr 11, 2018.
It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.
- Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works - Apr 11, 2018.
PyTorch has emerged as a major contender in the race to be the king of deep learning frameworks. What makes it really luring is it’s dynamic computation graph paradigm.
- New Poll: Data Scientist and Data Community Job Satisfaction - Apr 10, 2018.
Is Data Scientist / Machine Learning Engineer still the sexiest profession or have you been burned? Please vote in the new poll on job satisfaction.
- Managing model complexity in the fight against fraud, Apr 18 Webinar - Apr 10, 2018.
Learn how to optimize your models by leveraging robust data sets that improve performance; avoiding endless feature engineering and overfitting; and other useful steps.
- Top 8 Free Must-Read Books on Deep Learning - Apr 10, 2018.
Deep Learning is the newest trend coming out of Machine Learning, but what exactly is it? And how do I learn more? With that in mind, here's a list of 8 free books on deep learning.
- The Cold Start Problem with AI - Apr 10, 2018.
Why companies struggle with implementing AI and how to overcome it.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The Skip-gram Model - Apr 10, 2018.
Just like we discussed in the CBOW model, we need to model this Skip-gram architecture now as a deep learning classification model such that we take in the target word as our input and try to predict the context words.
- Can’t-Miss Keynotes at PAW Financial, plus 3 other PAWs in Vegas – Save ’til April 27 - Apr 9, 2018.
Don't miss the opportunity to witness keynote sessions by industry heavyweights at the upcoming Predictive Analytics World for Financial conference in Las Vegas, Jun 3-7.
- Comet.ml – Machine Learning Experiment Management - Apr 9, 2018.
This article presents comet.ml – a platform that allows tracking machine learning experiments with an emphasis on collaboration and knowledge sharing.
- Machine Learning for Text - Apr 9, 2018.
This book covers machine learning techniques from text using both bag-of-words and sequence-centric methods. The scope of coverage is vast, and it includes traditional information retrieval methods and also recent methods from neural networks and deep learning.
- Where Analytics, Data Science, Machine Learning Were Applied: Trends and Analysis - Apr 9, 2018.
CRM/Consumer Analytics, Finance, and Banking are still the leading applications, but Health Care and Fraud Detection are gaining. Anti-spam, Manufacturing, and Social are the fastest growing sectors in 2017, while Oil / Gas / Energy and Social Networks analysis have declined.
- Why so many data scientists are leaving their jobs, by Jonny Brooks - Apr 9, 2018.
We look at some of the big challenges and frustrations that data scientists face on a regular basis.
- Top Stories, Apr 2-8: Top 20 Deep Learning Papers, 2018 Edition; How Do I Get My First Data Science Job? - Apr 9, 2018.
Also: Supervised vs. Unsupervised Learning; Why Data Scientists Must Focus on Developing Product Sense; A Day in the Life of a Data Scientist: Part 4; How To Choose The Right Chart Type For Your Data
- Loading Terabytes of Data from Postgres into BigQuery - Apr 9, 2018.
Despite the fact that an ETL task is pretty challenging when it comes to loading Big Data, there’s still the scenario in which you can load terabytes of data from Postgres into BigQuery relatively easy and very efficiently.
- Build a Foundation that Supports AI and Machine Learning - Apr 6, 2018.
In an upcoming livestream on April 19, we’ll dig into how to build a foundation that supports AI and Machine Learning with industry experts and uncover what many companies are going through.
- Why Data Scientists Must Focus on Developing Product Sense - Apr 6, 2018.
Data Scientists should focus on developing product sense to move fast and systematically, create models that are relevant and to able to know when to stop.
- Descriptive Statistics: The Mighty Dwarf of Data Science – Crest Factor - Apr 6, 2018.
No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.
- Are New Technologies Killing Their Ancestors? - Apr 6, 2018.
Are automatic feature learning models (e.g. CNN) killing their previous manually engineered models? This is an important question that is to be answered in this article.
- Top Data Science, Machine Learning Courses from Udemy – April 2018 - Apr 5, 2018.
Udemy April $10.99 sale is now going on top courses from leading instructors and learn Machine Learning, Data Science, Python, Azure Machine Learning, and more.
- Top March Stories: Will GDPR Make Machine Learning Illegal? - Apr 5, 2018.
Also: The Two Sides of Getting a Job as a Data Scientist; 5 Things You Need to Know about Big Data.
- Advancing Your Analytics Career With Automated Machine Learning - Apr 5, 2018.
Join DataRobot on Apr 26 at 1:00 pm EST for this webinar, in which industry expert Jen Underwood will show how you can use automated machine learning to quickly develop predictive models and advance your career beyond traditional business intelligence.
- Data Science and the Art of Producing Entertainment at Netflix - Apr 5, 2018.
Each Netflix production is a logistical challenge that consumes and produces a vast amount of data. The tech giant is utilising this data to help them create new content and assist them at every stage, from pre-production to launching the show.
- Scalable Select of Random Rows in SQL - Apr 5, 2018.
Performance boosts are achieved by selecting random rows or the sampling technique. Let’s learn how to select random rows in SQL.
- Top KDnuggets tweets, Mar 28 – Apr 03: A “Weird” Intro to Deep Learning; Why so many data scientists are leaving their jobs - Apr 4, 2018.
Also: Don't learn #MachineLearning in 24 hours; Introduction to Functional Programming in #Python
- What Does GDPR Mean for Machine Learning? - Apr 4, 2018.
This post investigates how the GDPR, which comes into force at the end of May, will effect machine learning.
- Supervised vs. Unsupervised Learning - Apr 4, 2018.
Understanding the differences between the two main types of machine learning methods.
- Solve Data Science Challenges Through Collaboration - Apr 3, 2018.
Get this eBook to learn key issues that hamper fragmented data science teams; how accelerate innovation via collaborative workspaces, and how top data science teams boosted productivity by up to 4x.
- Hear From Data Science Luminaries Nate Silver and Cathy O’Neil at Rev, May 30-31, SF - Apr 3, 2018.
Rev is for data science leaders and practitioners, offering interactive sessions, stimulating conversations, and tutorials about how to run, manage, and accelerate data science as an organizational capability. Get early bird rates until April 15.
- Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: April and Beyond - Apr 3, 2018.
Coming soon: AnacondaCON Austin, QCon.ai SF, INFORMS Baltimore, AI Conference NYC, Data Science Salon Dallas, AI Expo Global London, ODSC Boston, and many more.
- How To Choose The Right Chart Type For Your Data - Apr 3, 2018.
The power of charts to assist in accurate interpretation is massive and that's why it is vital to select the correct type when you are trying to visualize data.
- Why You Should Start Using .npy Files More Often - Apr 3, 2018.
In this article, we demonstrate the utility of using native NumPy file format .npy over CSV for reading large numerical data set. It may be an useful trick if the same CSV data file needs to be read many times.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The Continuous Bag of Words (CBOW) - Apr 3, 2018.
The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words).
- Get a headstart with Looker – and $1,000 credits towards your AWS data warehouse - Apr 2, 2018.
Looker has partnered with AWS to offer, for a limited time, a free trial of Looker with a bonus of $1,000 credits towards your AWS data warehouse.
- Minimizing Model Risk with Automated Data Preparation & Machine Learning, Apr 19 - Apr 2, 2018.
Join DataRobot, Apr 19 at 2:00 pm ET/11:00 am PT, for a webinar on how to use Automated Data Preparation & Machine Learning to gain a competitive advantage, while quickly aligning your business operations to regulatory requirements.
- Top Stories, Mar 26 – Apr 1: Text Data Preprocessing: A Walkthrough in Python; A Weird Introduction to Deep Learning - Apr 2, 2018.
Also: Using Tensorflow Object Detection to do Pixel Wise Classification; Understanding Feature Engineering: Deep Learning Methods for Text Data; Exploring DeepFakes; Top 20 Python AI and Machine Learning Open Source Projects
- How Do I Get My First Data Science Job? - Apr 2, 2018.
Here are the steps you need to obtain your first job in data science, including details on how to create a good portfolio, key networking tips, getting the right education and managing expectations.
- A Day in the Life of a Data Scientist: Part 4 - Apr 2, 2018.
Interested in what a data scientist does on a typical day of work? Each data science role may be different, but these contributors have insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.