- Machine Learning-driven Firewall - Feb 23, 2017.
Cyber Security is always a hot topic in IT industry and machine learning is making security systems more stronger. Here, a particular use case of machine learning in cyber security is explained in detail.
- The Gentlest Introduction to Tensorflow – Part 4 - Feb 22, 2017.
This post is the fourth entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner, and focuses on logistic regression for classifying the digits of 0-9.
- The Gentlest Introduction to Tensorflow – Part 3 - Feb 21, 2017.
This post is the third entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner. This entry progresses to multi-feature linear regression.
- Stacking Models for Improved Predictions - Feb 21, 2017.
This post presents an example of regression model stacking, and proceeds by using XGBoost, Neural Networks, and Support Vector Regression to predict house prices.
- AI & Machine Learning World, London, 13-15 June 2017 – KDnuggets Offer - Feb 21, 2017.
AI & Machine Learning World, part of London Tech Week, brings together global thought leaders who have driven the adoption of machine learning within global enterprises. Use code TEC6245KD to save.
- Apache Arrow and Apache Parquet: Why We Needed Different Projects for Columnar Data, On Disk and In-Memory - Feb 16, 2017.
Apache Parquet and Apache Arrow both focus on improving performance and efficiency of data analytics. These two projects optimize performance for on disk and in-memory processing
- KDnuggets™ News 17:n06, Feb 15: So What is Big Data? 52 Useful Machine Learning APIs; Data Science finds Perfect Valentines Dates - Feb 15, 2017.
Also Making Python Speak SQL with pandasql; 52 Useful Machine Learning & Prediction APIs, updated; New Poll: Do you support Trump Immigration Ban?
- FeatureX: Software Engineer - Feb 10, 2017.
Seeking a software engineer, responsible for the design and development of machine learning and computer vision platforms and data systems.
- FeatureX: Machine Learning Research Scientist - Feb 10, 2017.
As a machine learning research scientist, you will be developing machine learning techniques for a wide variety of data sources, ranging from financial time series data to features extracted from satellite imagery.
- 50+ Useful Machine Learning & Prediction APIs, updated - Feb 8, 2017.
Very useful, updated list of 50+ APIs in machine learning, prediction, text analytics & classification, face recognition, language translation, and more.
- KDnuggets™ News 17:n05, Feb 8: Identifying Better Predictors; 5 Career Paths in Big Data, Data Science Explained - Feb 8, 2017.
Identifying Variables That Might Be Better Predictors; 5 Career Paths in Big Data and Data Science, Explained; 5 Free Courses for Getting Started in Artificial Intelligence; 3 practical thoughts on why deep learning performs so well
- Top /r/MachineLearning Posts, January: TensorFlow Updates; AlphaGo in the Wild; Self-Driving Mario Kart - Feb 7, 2017.
TensorFlow 1.0.0-alpha; Unknown bot repeatedly beats top Go players online - so far it's undefeated; TensorKart: self-driving MarioKart with TensorFlow; GTA V integration into Universe is now open-source; Keras will be added to core TensorFlow at Google
- 5 Career Paths in Big Data and Data Science, Explained - Feb 6, 2017.
Sexiest job... massive shortage... blah blah blah. Are you looking to get a real handle on the career paths available in "Data Science" and "Big Data?" Read this article for insight on where to look to sharpen the required entry-level skills.
- Top R Packages for Machine Learning - Feb 3, 2017.
What are the most popular ML packages? Let's look at a ranking based on package downloads and social website activity.
- Learning to Learn by Gradient Descent by Gradient Descent - Feb 2, 2017.
What if instead of hand designing an optimising algorithm (function) we learn it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!
- Top KDnuggets tweets, Jan 25-31: Python implementations of Andrew Ng #MachineLearning MOOC exercises - Feb 1, 2017.
#Python implementations of Andrew Ng #MachineLearning MOOC exercises; This repository contains the entire #Python #DataScience Handbook; What are the best #visualizations of #MachineLearning algorithms? Learn #TensorFlow and #DeepLearning, without a PhD.
- Is Deep Learning the Silver Bullet? - Feb 1, 2017.
With nearly every every smart young computer scientist planning to work on deep learning, are there really still artificial intelligence researchers working on other techniques? Is deep learning the AI silver bullet?
- KDnuggets™ News 17:n04, Feb 1: Data Science and Python Wrangling: Pandas Cheat Sheet; Great Collection of Machine Learning Algorithms - Feb 1, 2017.
Also Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms; Bad Data + Good Models = Bad Results; Data Scientist - best job in America, again.
- 6 areas of AI and Machine Learning to watch closely - Jan 25, 2017.
Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.
- Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms - Jan 25, 2017.
Interested in learning machine learning algorithms by implementing them from scratch? Need a good set of examples to work from? Check out this post with links to minimal and clean implementations of various algorithms.
- KDnuggets™ News 17:n03, Jan 25: Automated Machine Learning Overview; Data Science Puzzle; Chatbots on Steroids - Jan 25, 2017.
The Current State of Automated Machine Learning; The Data Science Puzzle, Revisited; Chatbots on Steroids; Data Science of Sales Calls: 3 Actionable Findings; Four Problems in Using CRISP-DM and How To Fix Them
- The Top Predictive Analytics Pitfalls to Avoid - Jan 23, 2017.
Predictive modelling and machine learning are significantly contributing to business, but they can be very sensitive to data and changes in it, which makes it very important to use proper techniques and avoid pitfalls in building data science models.
- Chatbots on Steroids: 10 Key Machine Learning Capabilities to Fuel Your Chatbot - Jan 23, 2017.
As chatbots become a common practice, the need for smarter bots arises. Empowering your bot with machine learning capabilities can really differentiate it from the rest. Check out these 10 capabilities to help fuel your chatbot.
- The Data Science Puzzle, Revisited - Jan 20, 2017.
The data science puzzle is re-examined through the relationship between several key concepts in the realm, and incorporates important updates and observations from the past year. The result is a modified explanatory graphic and rationale.
- Data Science of Sales Calls: 3 Actionable Findings - Jan 19, 2017.
How does AI help sales and marketing teams in the organisation? Let’s understand Dos and don’ts of sales calls with the help of analysis of over 70,000+ B2B SaaS sales calls.
- The Current State of Automated Machine Learning - Jan 18, 2017.
What is automated machine learning (AutoML)? Why do we need it? What are some of the AutoML tools that are available? What does its future hold? Read this article for answers to these and other AutoML questions.
- KDnuggets™ News 17:n02, Jan 18: Most Popular Language For Machine Learning; Analytics & Data Science Make Business Smarter - Jan 18, 2017.
The Most Popular Language For Machine Learning and Data Science; Analytics & Data Science Make Business Smarter; Exclusive: Interview with Jeremy Howard on Deep Learning, Kaggle, Data Science; 90 Active Blogs on Analytics, Big Data, Data Mining, and Data Science
- More Data or Better Algorithms: The Sweet Spot - Jan 17, 2017.
We examine the sweet spot for data-driven Machine Learning companies, where is not too easy and not too hard to collect the needed data.
- Discover the new Modern Data Science Academy - Jan 17, 2017.
The Modern Data Science Academy provides state-of-the-art workshops taught by San Diego Supercomputer Center experts. Coming workshops on Machine Learning (Feb 8-9) and NoSQL Databases (Mar 1-2).
- 90 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning (updated) - Jan 17, 2017.
Stay up-to-date in the data science with active blogs. This is a list of 90 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.
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- The Most Popular Language For Machine Learning and Data Science Is … - Jan 11, 2017.
When it comes to choosing programming language for Data Analytics projects or job prospects, people have different opinions depending on their career backgrounds and domains they worked in. Here is the analysis of data from indeed.com with respect to choice of programming language for machine learning and data science.
- Top /r/MachineLearning Posts, 2016: Google Brain AMA; Google Machine Learning Recipes; StarCraft II AI Research Environment - Jan 11, 2017.
Google Brain AMA; Google Machine Learning Recipes; StarCraft II AI Research Environment; Huggable Image Classifier; xkcd: Linear Regression; AlphaGO WINS!; TensorFlow Fizzbuzz
- Deep Learning in Healthcare Summit in London, 28 February – 1 March (KDnuggets Offer) - Jan 11, 2017.
Discover advances in deep learning tools and techniques from the world's leading innovators across industry, academia and the healthcare sector at the Deep Learning in Healthcare Summit in London, 28 February – 1 March. Use discount code KDNUGGETS to save 20%.
- KDnuggets™ News 17:n01, Jan 11: 5 Machine Learning Projects You Can’t Overlook; Future of Deep Learning; Self-Driving Car Surprises - Jan 11, 2017.
Also Game Theory Reveals the Future of Deep Learning; Generative Adversarial Networks - Hot Topic in ML; Cartoon: When Self-Driving Cars take you too far
- AI, Data Science, Machine Learning: Main Developments in 2016, Key Trends in 2017 - Jan 10, 2017.
2017 is here. Check out an encore installation in our "Main Developments in 2016 and Key Trends in 2017" series, where experts weigh in with their opinions.
- Machine Learning Meets Humans – Insights from HUML 2016 - Jan 6, 2017.
Report from an important IEEE workshop on Human Use of Machine Learning, covering trust, responsibility, the value of explanation, safety of machine learning, discrimination in human vs. machine decision making, and more.
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- Top /r/MachineLearning Posts, December: OpenAI Universe; Deep Learning MOOC For Coders; Musk: Tesla Gets Awesome-er - Jan 5, 2017.
OpenAI Universe; Deep Learning For Coders—18 hours of lessons for free; Elon Musk on Twitter: Tesla Autopilot vision neural net now working well; Apple to Start Publishing AI Research; Duolingo's "half-life regression" method for modeling human memory
- Top KDnuggets tweets, Dec 21 – Jan 03: R vs Python: A Comparison and Free Books to Learn; Popular Deep Learning Tools – a review - Jan 4, 2017.
R vs Python: A Comparison and Free Books to Learn; The Five Capability Levels of Deep Learning - Yann Lecun view; The Future Of Machine Learning, McKinsey 2016 Analytics Study; #BigData: Main Developments in 2016 and Key Trends in 2017
- How To Stay Competitive In Machine Learning Business - Jan 4, 2017.
To stay competitive in machine learning business, you have to be superior than your rivals and not the best possible – says one of the leading machine learning expert. Simple rules are defined here to make that happen. Let’s see how.
- Fundamentals of Machine Learning for Predictive Data Analytics, Dublin, 21-23 March, 2017 - Jan 4, 2017.
Based on the trainers book, this course presents a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
- Generative Adversarial Networks – Hot Topic in Machine Learning - Jan 3, 2017.
What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.
- 3 methods to deal with outliers - Jan 3, 2017.
In both statistics and machine learning, outlier detection is important for building an accurate model to get good results. Here three methods are discussed to detect outliers or anomalous data instances.
- Ten Myths About Machine Learning, by Pedro Domingos - Jan 3, 2017.
Myths on artificial intelligence and machine learning abound. Noted expert Pedro Domingos identifies and refutes a number of these myths, of both the pessimistic and optimistic variety.
- Machine Learning and Cyber Security Resources - Jan 2, 2017.
An overview of useful resources about applications of machine learning and data mining in cyber security, including important websites, papers, books, tutorials, courses, and more.
- 5 Machine Learning Projects You Can No Longer Overlook, January - Jan 2, 2017.
There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.
- KDnuggets™ News 16:n46, Dec 28: 4 Reasons Your Machine Learning Model is Wrong; Deep Learning for coders MOOC - Dec 28, 2016.
First Deep Learning for coders MOOC launched by Jeremy Howard; 4 Reasons Your Machine Learning Model is Wrong; 5 Capability Levels of Deep Learning Intelligence; Data Science Basics: Power Laws and Distributions.
- Top KDnuggets tweets, Dec 14-20: False positives versus false negatives: Best explanation ever - Dec 21, 2016.
Also #MachineLearning, #AI experts: Main Developments 2016, Key Trends 2017; Official code repository for #MachineLearning with #TensorFlow book; Top 10 Essential Books for the #Data Enthusiast.
- KDnuggets™ News 16:n45, Dec 21: 50+ Data Science, ML Cheat Sheets; Ethics of Self-Driving Cars? Top experts on Machine Learning Main Events, Key Trends - Dec 21, 2016.
Also New Poll: Can You Live with Ethics of Machine Learning and Self-Driving Cars? Machine Learning, AI Main Events and Key Trends; 5 Basic Types of Data Science Interview Questions.
- Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017 - Dec 20, 2016.
As 2016 comes to a close and we prepare for a new year, check out the final instalment in our "Main Developments in 2016 and Key Trends in 2017" series, where experts weigh in with their opinions.
- New Poll: Can You Live with Ethics of Machine Learning and Self-Driving Cars? - Dec 19, 2016.
The difficult thing about Machine Learning Ethics is that it forces us to consider the harsh choices people sometimes have to make but don't want to think about. Here is one such situation - what is the right choice? Please vote.
- 50+ Data Science, Machine Learning Cheat Sheets, updated - Dec 14, 2016.
Gear up to speed and have concepts and commands handy in Data Science, Data Mining, and Machine learning algorithms with these cheat sheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark, Matlab, and Java.
- arXiv Paper Spotlight: Why Does Deep and Cheap Learning Work So Well? - Dec 13, 2016.
The recent paper at hand approaches explaining deep learning from a different perspective, that of physics, and discusses the role of "cheap learning" (parameter reduction) and how it relates back to this innovative perspective.
- Just How Smart Are Smart Machines? - Dec 12, 2016.
The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization’s business needs requires a deep understanding of their capabilities. Here we examine 4 levels of intelligence across task types.
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- KDnuggets Top Blogs and Bloggers in November 2016 - Dec 8, 2016.
We recognize the best KDnuggets Bloggers who had the most popular blogs by views or social media shares in November 2016.
- Top KDnuggets tweets, Nov 30 – Dec 06: A great and useful collection of minimal and clean implementations of #MachineLearning algorithms - Dec 7, 2016.
Also: #MachineLearning Yearning book draft, Free Download, by Andrew Ng; A short guide to learn #NeuralNets, and maybe get famous and rich with #DeepLearning; Free Book: Foundations of Computer Science, Aho & Ullman.
- What You Are Too Afraid to Ask About Artificial Intelligence (Part I): Machine Learning - Dec 7, 2016.
In the first of a 2 part series, this post will briefly discuss major advancements in pure machine learning techniques - while a followup post will similarly treat neuroscience - both with an agnostic lens.
- arXiv Paper Spotlight: Automated Inference on Criminality Using Face Images - Dec 7, 2016.
This recent paper addresses the use of still facial images in an attempt to differentiate criminals from non-criminals, doing so with the help of 4 different classifiers. Results are as troubling as they are unsettling.
- Free ebooks: Machine Learning with Python and Practical Data Analysis - Dec 5, 2016.
Two free ebooks: "Building Machine Learning Systems with Python" and "Practical Data Analysis" will give your skills a boost and make a great start in the New Year.
- Why Deep Learning is Radically Different From Machine Learning - Dec 5, 2016.
There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference?
- Top /r/MachineLearning Posts, November: StarCraft II for AI Research; Google AI Experiments Website; Google in Montreal - Dec 5, 2016.
DeepMind and Blizzard to release StarCraft II as an AI research environment; Google AI Experiments Website; Google opens new Montreal-based AI research lab; Lip Reading Sentences in the Wild; Clean implementations of machine learning algorithms
- Academic/Research positions in Business Analytics, Data Science, Machine Learning in November 2016 - Dec 2, 2016.
Faculty/Postdoc positions in Data Science/Machine Learning at DePaul, UCSB, Virginia Tech, Barcelona U, Aarhus U, Georgia State U, U. of Innsbruck, Drexel, CMU, Oregon State U, Iowa State, and more.
- Interviews with Data Scientists: Claudia Perlich - Dec 2, 2016.
In this wide-ranging interview, Roberto Zicari talks to a leading Data Scientist Claudia Perlich about what they must know about Machine Learning and evaluation, domain knowledge, data blending, and more.
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- Top KDnuggets tweets, Nov 23-29: The Entire #Python Language in a Single Image ; Great list of Data Science, Machine Learning, AI Resources - Nov 30, 2016.
The Entire #Python Language in a Single Image; Cartoon: Thanksgiving, #BigData, and Turkey #DataScience; 50% of Data Scientists have under 10 GB databases, not #BigData; Machine Learning Algorithms: A Concise Technical Overview
- Measuring Topic Interpretability with Crowdsourcing - Nov 30, 2016.
Topic modelling is an important statistical modelling technique to discover abstract topics in collection of documents. This article talks about a new measure for assessing the semantic properties of statistical topics and how to use it.
- The Data Science Delusion - Nov 30, 2016.
Gleanings from observed technical misunderstandings between business leaders and data scientists (and among data scientists themselves) so dramatic that one could start wondering whether there is something wrong with data science as it is being practiced.
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- Top 10 Amazon Books in Artificial Intelligence & Machine Learning, 2016 Edition - Nov 30, 2016.
Given the ongoing explosion in interest for all things Data Science, Artificial Intelligence, Machine Learning, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the AI & Machine Learning category.
- KDnuggets™ News 16:n42, Nov 30: Python Machine Learning Open Source Projects; Facebook Groups for Big Data & Data Science - Nov 30, 2016.
Python Machine Learning Open Source Projects; Facebook Groups for Big Data & Data Science; Combining Different Methods to Create Advanced Time Series Prediction; Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed; Continuous improvement for IoT through AI / Continuous learning
- Machine Learning vs Statistics - Nov 29, 2016.
Machine learning is all about predictions, supervised learning, and unsupervised learning, while statistics is about sample, population, and hypotheses. But are they actually that different?
- Introduction to Machine Learning for Developers - Nov 28, 2016.
Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning.
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- arXiv Paper Spotlight: Stealing Machine Learning Models via Prediction APIs - Nov 28, 2016.
Despite their confidentiality, machine learning models which have public-facing APIs are vulnerable to model extraction attacks, which attempt to "steal the ingredients" and duplicate functionality. The paper at hand investigates.
- Continuous improvement for IoT through AI / Continuous learning - Nov 25, 2016.
In reality, especially for IoT, it is not like once an analytics model is built, it will give the results with same accuracy till the end of time. Data pattern changes over the time which makes it absolutely important to learn from new data and improve/recalibrate the models to get correct result. Below article explain this phenomenon of continuous improvement in analytics for IoT.
- Deep Learning Research Review: Reinforcement Learning - Nov 25, 2016.
This edition of Deep Learning Research Review explains recent research papers in Reinforcement Learning (RL). If you don't have the time to read the top papers yourself, or need an overview of RL in general, this post has you covered.
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- Top KDnuggets tweets, Nov 16-22: Top 20 #Python #MachineLearning #OpenSource Projects; Shortcomings of #DeepLearning - Nov 23, 2016.
Top 20 #Python #MachineLearning #OpenSource Projects; Shortcomings of #DeepLearning; What is the Difference Between #DeepLearning and Regular #MachineLearning?; Questions To Ask When Moving #MachineLearning From Practice to Production; How to Choose the Right #Database System
- Vencore: Software Engineer, Machine Learning - Nov 23, 2016.
Seeking a Software Developer/Engineer professional with an experienced background in software/algorithm development and integration. This opportunity requires the ability to develop scalable and maintainable software solutions for our healthcare predictive analytics platform.
- Top 10 Facebook Groups for Big Data, Data Science, and Machine Learning - Nov 23, 2016.
Social media now not only shares friendship connections or photos of “selfies” but also spreads from political media to science information. Social network members are tending to more eagerly learn about big data, data science and machine learning through groups. We review the ten largest Facebook groups in this area.
- Top 20 Python Machine Learning Open Source Projects, updated - Nov 21, 2016.
Open Source is the heart of innovation and rapid evolution of technologies, these days. This article presents you Top 20 Python Machine Learning Open Source Projects of 2016 along with very interesting insights and trends found during the analysis.
- Questions To Ask When Moving Machine Learning From Practice to Production - Nov 18, 2016.
An overview of applying machine learning techniques to solve problems in production. This articles covers some of the varied questions to ponder when incorporating machine learning into teams and processes.
- The Foundations of Algorithmic Bias - Nov 16, 2016.
We might hope that algorithmic decision making would be free of biases. But increasingly, the public is starting to realize that machine learning systems can exhibit these same biases and more. In this post, we look at precisely how that happens.
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- CMU: Teaching Faculty, Machine Learning - Nov 16, 2016.
CMU has the world’s only academic Machine Learning Department, and is seeking a Teaching Faculty member to lead efforts in modernizing the teaching of machine learning inside the university and to remote locations.
- Combining Different Methods to Create Advanced Time Series Prediction - Nov 16, 2016.
The results from combining methods for time series prediction have been quite promising. However, the degree of error for long-term predictions is still quite high. Sounds like a challenge, so some new experiments are forthcoming!
- 13 Forecasts on Artificial Intelligence - Nov 15, 2016.
Once upon a time, Artificial Intelligence (AI) was the future. But today, human wants to see even beyond this future. This article try to explain how everyone is thinking about the future of AI in next five years, based on today’s emerging trends and developments in IoT, robotics, nanotech and machine learning.
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- An Intuitive Explanation of Convolutional Neural Networks - Nov 11, 2016.
This article provides a easy to understand introduction to what convolutional neural networks are and how they work.
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- A Quick Introduction to Neural Networks - Nov 9, 2016.
This article provides a beginner level introduction to multilayer perceptron and backpropagation.
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- How to Rank 10% in Your First Kaggle Competition - Nov 9, 2016.
This post presents a pathway to achieving success in Kaggle competitions as a beginner. The path generalizes beyond competitions, however. Read on for insight into succeeding while approaching any data science project.
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- Agilience Top Artificial Intelligence, Machine Learning Authorities - Nov 7, 2016.
Agilience developed a new way to find authorities in social media across many fields of interest. In previous post we reviewed the top authorities in Data Mining and Data science; in this post we review top authorities in Artificial Intelligence and Machine Learning which includes Vineet Vashishta, Kirk D. Borne, KDnuggets, James Kobielus, Kaggle and more.
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- Top /r/MachineLearning Posts, October: NSFW Image Recognition, Differentiable Neural Computers, Hinton on Coursera - Nov 4, 2016.
NSFW Image Recognition, Differentiable Neural Computers, Hinton's Neural Networks for Machine Learning Coursera course; Introducing the AI Open Network; Making a Self-driving RC Car
- Booking.com: Data Scientist – Machine Learning - Nov 3, 2016.
Booking.com is looking for rock star Data Scientists to add to join their highly successful Personalization Team, crunching data and providing customers with the most relevant personalized recommendations.
- KDnuggets™ News 16:n39, Nov 2: Machine Learning: A Complete and Detailed Overview; Learn Data Science in 8 (Easy) Steps - Nov 2, 2016.
Machine Learning: A Complete and Detailed Overview; Cartoon: Scary Big Data; Learn Data Science in 8 (Easy) Steps; Is Your Code Good Enough to Call Yourself a Data Scientist?; Using Machine Learning to Detect Malicious URLs; Frequent Pattern Mining and the Apriori Algorithm
- How Can Lean Six Sigma Help Machine Learning? - Nov 1, 2016.
The data cleansing phase alone is not sufficient to ensure the accuracy of the machine learning, when noise / bias exists in input data. The lean six sigma variance reduction can improve the accuracy of machine learning results.
- Machine Learning: A Complete and Detailed Overview - Oct 28, 2016.
This is an overview (with links) to a 5-part series on introductory machine learning. The set of tutorials is comprehensive, yet succinct, covering many important topics in the field (and beyond).
- Automated Machine Learning: An Interview with Randy Olson, TPOT Lead Developer - Oct 28, 2016.
Read an insightful interview with Randy Olson, Senior Data Scientist at University of Pennsylvania Institute for Biomedical Informatics, and lead developer of TPOT, an open source Python tool that intelligently automates the entire machine learning process.
- Learn Data Science in 8 (Easy) Steps - Oct 27, 2016.
Want to learn data science? Check out these 8 (easy) steps to set out in the right direction!
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- KDnuggets™ News 16:n38, Oct 26: Free Machine Learning EBooks; Neural Networks in Python with Scikit-learn - Oct 26, 2016.
5 EBooks to Read Before Getting into A Machine Learning Career; A Beginner's Guide to Neural Networks with Python and Scikit-learn 0.18!; New Poll: What was the largest dataset you analyzed / data mined?; Jupyter Notebook Best Practices for Data Science
- 5 EBooks to Read Before Getting into A Machine Learning Career - Oct 21, 2016.
A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.
- A Beginner’s Guide to Neural Networks with Python and SciKit Learn 0.18! - Oct 20, 2016.
This post outlines setting up a neural network in Python using Scikit-learn, the latest version of which now has built in support for Neural Network models.
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- Intellectual Ventures Lab: Sr. Machine Learning Algorithm Development Software Engineer - Oct 18, 2016.
Seeking a Senior Machine-Learning Algorithm Development Software Engineer to provide technical leadership to fast-paced machine-learning development projects.
- European Machine Intelligence Landscape - Oct 18, 2016.
This post outlines the European machine intelligence landscape, which, until recently, has been under-appreciated in its contribution to the innovation and commercialisation of machine intelligence technologies.
- MLDB: The Machine Learning Database - Oct 17, 2016.
MLDB is an opensource database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.
- Top KDnuggets tweets, Oct 05-11: Most Active #DataScientists on #Github; Why Not So Hadoop? - Oct 12, 2016.
Most Active #DataScientists, Free Books, Notebooks & Tutorials on #Github; Why Not So Hadoop?; Free #MachineLearning text PDF, from theory to algorithms; Top @reddit #MachineLearning Posts September.
- Humans & Machines Ethics Framework: Assessing Machine Learning Influence - Oct 11, 2016.
Humans & Machines Ethics Canvas’ main goal is to be a guide for critical thinking throughout the ethical decision-making process. It acts as a value system and an ethics framework to assess the influence of machine learning and software development while developing a system for individuals, teams, and organisations.
- Top /r/MachineLearning Posts, September: Open Images Dataset; Whopping Deep Learning Grant; Advanced ML Courseware - Oct 7, 2016.
Google Research announces the Open Images dataset; Canadian Government Deep Learning Research grant; DeepMind: WaveNet - A Generative Model for Raw Audio; Machine Learning in a Year - From total noob to using it at work; Phd-level machine learning courses; xkcd: Linear Regression
- Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team - Oct 4, 2016.
This is an interview with the authors of the recent winning KDnuggets Automated Data Science and Machine Learning blog contest entry, which provided an overview of the Auto-sklearn project. Learn more about the authors, the project, and automated data science.
- Beginner’s Guide to Apache Flink – 12 Key Terms, Explained - Oct 4, 2016.
We review 12 core Apache Flink concepts, to better understand what it does and how it works, including streaming engine terminology.
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- Deep Learning Reading Group: SqueezeNet - Sep 29, 2016.
This paper introduces a small CNN architecture called “SqueezeNet” that achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters.
- Data Science of Sales Calls: The Surprising Words That Signal Trouble or Success - Sep 29, 2016.
While not as profound a problem as uncovering the secrets of the universe, how to conduct a successful sales conversation is an age-old problem, impacting millions of people every day.
- Brainwaves hackathon on Machine Learning - Sep 28, 2016.
This hackathon aims at attracting top developers for a 30-hour build session focused on Machine Learning. The first qualifying event will be held online in October.
- Top Data Scientist Claudia Perlich’s Favorite Machine Learning Algorithm - Sep 27, 2016.
Interested in the reasons why a top data scientist is partial to one particular algorithm over others? Read on to find out.
- Comparing Clustering Techniques: A Concise Technical Overview - Sep 26, 2016.
A wide array of clustering techniques are in use today. Given the widespread use of clustering in everyday data mining, this post provides a concise technical overview of 2 such exemplar techniques.
- Up to Speed on Deep Learning: August Update, Part 2 - Sep 23, 2016.
This is the second part of an overview of deep learning stories that made news in August. Look to see if you have missed anything.
- Spark for Scale: Machine Learning for Big Data - Sep 23, 2016.
This post discusses the fundamental concepts for working with big data using distributed computing, and introduces the tools you need to build machine learning models.
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- Deep Learning Reading Group: Deep Residual Learning for Image Recognition - Sep 22, 2016.
Published in 2015, today's paper offers a new architecture for Convolution Networks, one which has since become a staple in neural network implementation. Read all about it here.
- Up to Speed on Deep Learning: August Update - Sep 21, 2016.
Check out this thorough roundup of deep learning stories that made news in August, and see if there are any items of note that you missed.
- Support Vector Machines: A Concise Technical Overview - Sep 21, 2016.
Support Vector Machines remain a popular and time-tested classification algorithm. This post provides a high-level concise technical overview of their functionality.
- KDnuggets™ News 16:n34, Sep 21: The Great Algorithm Tutorial Roundup; 7 Steps to Mastering Apache Spark 2.0 - Sep 21, 2016.
The Great Algorithm Tutorial Roundup; 7 Steps to Mastering Apache Spark 2.0; Machine Learning in a Year: From Total Noob to Effective Practitioner; Learning From Data (Introductory Machine Learning) Caltech MOOC
- The Great Algorithm Tutorial Roundup - Sep 20, 2016.
This is a collection of tutorials relating to the results of the recent KDnuggets algorithms poll. If you are interested in learning or brushing up on the most used algorithms, as per our readers, look here for suggestions on doing so!
- Machine Learning in a Year: From Total Noob to Effective Practitioner - Sep 19, 2016.
Read how the author went from self-described total machine learning noob to being able to effectively use machine learning effectively on real world projects at work... all within a year.
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- New sequence learning data set - Sep 17, 2016.
A new data set for the study of sequence learning algorithms is available as of today. The data set consists of pen stroke sequences that represent handwritten digits, and was created based on the MNIST handwritten digit data set.
- Learning From Data (Introductory Machine Learning) Caltech course starts on edX Sep 18 - Sep 17, 2016.
This introductory Machine Learning course taught by top Caltech professor Abu-Mostafa covers theory, algorithms and applications, with focus on real understanding. Starts Sep 18, 2016 on edX.
- The Deception of Supervised Learning - Sep 13, 2016.
Do models or offline datasets ever really tell us what to do? Most application of supervised learning is predicated on this deception.
- Urban Sound Classification with Neural Networks in Tensorflow - Sep 12, 2016.
This post discuss techniques of feature extraction from sound in Python using open source library Librosa and implements a Neural Network in Tensorflow to categories urban sounds, including car horns, children playing, dogs bark, and more.
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- Up to Speed on Deep Learning: July Update, Part 2 - Sep 7, 2016.
Check out this second installation of deep learning stories that made news in July. See if there are any items of note you missed.
- Top /r/MachineLearning Posts, August: Google Brain AMA, Image Completion with TensorFlow, Japanese Cucumber Farming - Sep 5, 2016.
Google Brain AMA; Image Completion with Deep Learning in TensorFlow; Japanese Cucumber Farming; Andrew Ng's machine learning class in Python; Google Brain datasets for robotics research
- Booking: Data Scientist – Machine Learning - Aug 31, 2016.
Booking.com is looking for rock star Data Scientists to add to join their highly successful Personalization Team, crunching data and providing customers with the most relevant personalized recommendations.
- Hitachi: Research Scientist, Machine Learning - Aug 30, 2016.
Hitachi is seeking a Research Scientist in the Big Data Laboratory located in Silicon Valley, with a mission of helping create new and innovative solutions in big data and advanced analytics.
- PAPIs 16 Conference on Predictive Applications & APIs, Oct 10-12, Boston - Aug 30, 2016.
PAPIs is the premier forum for the presentation of new machine learning APIs, techniques, architectures and tools to build intelligent applications. It also hosts the world’s 1st startup competition where the jury is an AI.
- Up to Speed on Deep Learning: July Update - Aug 29, 2016.
Check out this thorough roundup of deep learning stories that made news in July. See if there are any items of note you missed.
- New Poll: Which methods/algorithms you used for a Data Science or Machine Learning application? - Aug 26, 2016.
Which methods/approaches you used in the past 12 months for an actual Data Science-related application? Please vote and we will analyze and publish the results.
- Is “Artificial Intelligence” Dead? Long Live Deep Learning?!? - Aug 26, 2016.
Has Deep Learning become synonymous with Artificial Intelligence? Read a discussion on the topic fuelled by the opinions of 7 participating experts, and gain some additional insight into the future of research and technology.
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- Introduction to Local Interpretable Model-Agnostic Explanations (LIME) - Aug 25, 2016.
Learn about LIME, a technique to explain the predictions of any machine learning classifier.
- Top KDnuggets tweets, Aug 17-23: Approaching (Almost) Any #MachineLearning Problem; #Database Nirvana – can one query language rule them all? - Aug 24, 2016.
In Search of #Database Nirvana - can one query language rule them all? Google Cloud Datalab: #Jupyter meets #TensorFlow, #cloud meets local deployment; Approaching (Almost) Any #MachineLearning Problem; The Gentlest Introduction to Tensorflow Part 1.