- 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!
Pages: 1 2
Big Data, Data Science, DataCamp, Machine Learning
- 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
Free ebook, Machine Learning, Neural Networks, Poll, Python, scikit-learn
- 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.
Bayesian, Data Science, Deep Learning, Free ebook, Machine Learning, Reinforcement Learning
- 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.
Pages: 1 2
Beginners, Machine Learning, Neural Networks, Python, scikit-learn
- 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.
Algorithms, Bellevue, Intellectual Ventures, Machine Learning, Software Engineer, WA
- 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.
Europe, Machine Intelligence, Machine Learning, Startups
- 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.
Classification, Database, Machine Learning, TensorFlow, Transfer Learning
- 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.
GitHub, Hadoop, Machine Learning, Reddit, Top tweets
- 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.
Advice, Analytics, Challenges, Data Science, Ethics, Machine Learning
- 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
Canada, Courses, Deep Learning, Generative Models, Geoff Hinton, Machine Learning, Reddit, xkcd
- 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.
Automated, Automated Data Science, Automated Machine Learning, Competition, Machine Learning, scikit-learn
- 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.
Pages: 1 2
API, Explained, Flink, Graph Mining, Machine Learning, Streaming Analytics
- 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.
Compression, Deep Learning, Lab41, Machine Learning, Neural Networks
- 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.
Gong.io, Machine Learning, Sales, Speech Recognition
- 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.
Data Science, Hackathon, Machine Learning, Predictive Analytics
- 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.
Algorithms, Claudia Perlich, Logistic Regression, Machine Learning
- 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.
Algorithms, Clustering, K-means, Machine Learning
- 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.
Convolutional Neural Networks, Deep Learning, Google, Image Recognition, Machine Learning, Neural Networks, NIPS
- 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.
Pages: 1 2 3
Apache Spark, Big Data, Hadoop, HDFS, Machine Learning, MapReduce
- 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.
Academics, Convolutional Neural Networks, Deep Learning, Image Recognition, Lab41, Machine Learning, Neural Networks
- 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.
Art, Convolutional Neural Networks, Deep Learning, Image Recognition, Machine Learning, Neural Networks
- 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.
Algorithms, Machine Learning, Support Vector Machines
- 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
Algorithms, Apache Spark, Career, Data Scientist, Decision Trees, Machine Learning, 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!
Algorithms, Clustering, Decision Trees, K-nearest neighbors, Machine Learning, PCA, Poll, random forests algorithm, Regression, Statistics, Text Mining, Time Series, Visualization
- 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.
Pages: 1 2
Advice, Beginners, Machine Learning
- 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.
GitHub, Image Recognition, Machine Learning, MNIST, Sequences
- 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.
Caltech, edX, Learning from Data, Machine Learning, MOOC, Yaser Abu-Mostafa
- 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.
Deep Learning, Interpretability, Machine Learning, Reinforcement Learning, Supervised Learning, Zachary Lipton
- 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.
Pages: 1 2
Deep Learning, Feature Extraction, Machine Learning, Neural Networks, TensorFlow
- 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.
Convolutional Neural Networks, Coursera, Deep Learning, Geoff Hinton, Machine Learning, Neural Networks, OpenAI
- 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
Andrew Ng, Deep Learning, Google, Machine Learning, Python, Reddit, Robots, TensorFlow
- 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.
Amsterdam, Booking.com, Data Scientist, Machine Learning, Netherlands
- 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.
CA, Hitachi, Machine Learning, Research Scientist, Santa Clara
- 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.
API, Applications, Boston, Claudia Perlich, MA, Machine Learning, Startups
- 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.
Cats, Deep Learning, DeepMind, Google, GPU, Healthcare, Machine Learning
- 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.
Algorithms, Applications, Clustering, Data Science, Machine Learning, Poll, Supervised Learning
- 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.
Pages: 1 2
AI, Artificial Intelligence, Deep Learning, Gregory Piatetsky, Hugo Larochelle, Machine Learning, Pedro Domingos, Xavier Amatriain
- 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.
Algorithms, Classifier, Explanation, Interpretability, LIME, Machine Learning, Prediction
- 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.
Databases, Jupyter, Machine Learning, TensorFlow, Top tweets
- KDnuggets™ News 16:n31, Aug 24: 10 Algo Machine Learning Engineers Need to Know; How to Become a Data Scientist; Gentle Tensorflow - Aug 24, 2016.
The 10 Algorithms Machine Learning Engineers Need to Know; How to Become a Data Scientist - Part 1; The Gentlest Introduction to Tensorflow - Part 1; Approaching (Almost) Any Machine Learning Problem.
Algorithms, Data Scientist, Machine Learning, TensorFlow
- The Gentlest Introduction to Tensorflow – Part 2 - Aug 19, 2016.
Check out the second and final part of this introductory tutorial to TensorFlow.
Pages: 1 2
Beginners, Deep Learning, Gradient Descent, Machine Learning, TensorFlow
- Top Machine Learning Projects for Julia - Aug 19, 2016.
Julia is gaining traction as a legitimate alternative programming language for analytics tasks. Learn more about these 5 machine learning related projects.
Deep Learning, Julia, Machine Learning, Open Source, scikit-learn
- The 10 Algorithms Machine Learning Engineers Need to Know - Aug 18, 2016.
Read this introductory list of contemporary machine learning algorithms of importance that every engineer should understand.
Pages: 1 2
Algorithms, Machine Learning, Supervised Learning, Unsupervised Learning
- Approaching (Almost) Any Machine Learning Problem - Aug 18, 2016.
If you're looking for an overview of how to approach (almost) any machine learning problem, this is a good place to start. Read on as a Kaggle competition veteran shares his pipelines and approach to problem-solving.
Pages: 1 2
Advice, Feature Selection, Kaggle, Machine Learning, Modeling
- The Gentlest Introduction to Tensorflow – Part 1 - Aug 17, 2016.
In this series of articles, we present the gentlest introduction to Tensorflow that starts off by showing how to do linear regression for a single feature problem, and expand from there.
Pages: 1 2
Beginners, Deep Learning, Gradient Descent, Linear Regression, Machine Learning, TensorFlow
- Tales from ICML: Insights and Takeaways - Aug 15, 2016.
The dust has settled from ICML 2016, having been held in June in NYC. Read some perspective on what was offered at the conference and relevant takeaways from a reflective attendee.
Deep Learning, Fei-Fei Li, ICML, Lab41, Machine Learning
- Stop Blaming Terminator for Bad AI Journalism - Aug 11, 2016.
Too often, we blame The Terminator for the public's misconceptions concerning machine learning. But do James Cameron and the Austrian Oak stand wrongfully accused?
Big Data Hype, Deep Learning, Machine Learning, Skynet, Zachary Lipton
- Contest 2nd Place: Automated Data Science and Machine Learning in Digital Advertising - Aug 4, 2016.
This post is an overview of an automated machine learning system in the digital advertising realm. It is an entrant and second-place recipient in the recent KDnuggets blog contest.
Advertising, Automated, Automated Data Science, Automated Machine Learning, Claudia Perlich, Machine Learning
- Contest 2nd Place: Automating Data Science - Aug 3, 2016.
This post discusses some considerations, options, and opportunities for automating aspects of data science and machine learning. It is the second place recipient (tied) in the recent KDnuggets blog contest.
Algorithms, Automated, Automated Data Science, Feature Selection, Machine Learning
- What Statistics Topics are Needed for Excelling at Data Science? - Aug 2, 2016.
Here is a list of skills and statistical concepts suggested for excelling at data science, roughly in order of increasing complexity.
Bayesian, Distribution, Machine Learning, Markov Chains, Probability, Regression, Statistics
- Top /r/MachineLearning Posts, July: Google Brain AMA, Geoff Hinton Awarded IEEE Medal, Hinton ANN Course Lives! - Aug 2, 2016.
Google Brain AMA; Geoff Hinton Awarded IEEE Medal; Geoff Hinton's ANN Course Lives; Google’s DeepMind Reduces Data Center Cooling Bill; Training an artificial neural network to play Diablo 2
DeepMind, Geoff Hinton, Google, Machine Learning, Reddit
- And the Winner is… Stepwise Regression - Aug 1, 2016.
This post evaluates several methods for automating the feature selection process in large-scale linear regression models and show that for marketing applications the winner is Stepwise regression.
Automated Data Science, Feature Selection, Linear Regression, Machine Learning, Predictive Analytics
- Dataiku DSS 3.1 – Now with 5 ML Backends & Scala! - Aug 1, 2016.
Introducing Dataiku DSS 3.1, with new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface.
Data Science, Dataiku, Machine Learning, Scala
- The steps in the machine learning workflow - Jul 28, 2016.
We outline preprocessing steps for finding, removing, and cleaning data to prepare it for machine learning and how tools like MATLAB can help with data exploration, identification of key traits, and communicating the findings.
Machine Learning, MathWorks, MATLAB, Workflow
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 3 - Jul 27, 2016.
This is the final part of a 3 part introductory series on machine learning in Python, using the Titanic dataset.
Pages: 1 2
Kaggle, Machine Learning, Python, Titanic
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 2 - Jul 26, 2016.
This is part 2 of a 3 part introductory series on machine learning in Python, using the Titanic dataset.
Pages: 1 2
Machine Learning, Python, Titanic
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 1 - Jul 25, 2016.
Check out the first of a 3 part introductory series on machine learning in Python, fueled by the Titanic dataset. This is a great place to start for a machine learning newcomer.
Machine Learning, Python, scikit-learn, Titanic
- Building a Data Science Portfolio: Machine Learning Project Part 3 - Jul 22, 2016.
The final installment of this comprehensive overview on building an end-to-end data science portfolio project focuses on bringing it all together, and concludes the project quite nicely.
Pages: 1 2
Advice, Career, Data Science, Data Scientist, Dataquest, Machine Learning, Python
- Machine Learning: Separating Hype From Reality - Jul 22, 2016.
When it comes to business value and ROI, does machine learning live up tot he claims? We’ll explore a pure machine learning approach through the lens of a typical enterprise use case.
Hype, Machine Learning
- Building a Data Science Portfolio: Machine Learning Project Part 2 - Jul 21, 2016.
The second part of this comprehensive overview on building an end-to-end data science portfolio project concentrates on data exploration and preparation.
Pages: 1 2
Advice, Career, Data Science, Data Scientist, Dataquest, Machine Learning, Portfolio, Python
- Building a Data Science Portfolio: Machine Learning Project Part 1 - Jul 20, 2016.
Dataquest's founder has put together a fantastic resource on building a data science portfolio. This first of three parts lays the groundwork, with subsequent posts over the following 2 days. Very comprehensive!
Pages: 1 2
Advice, Career, Data Science, Data Scientist, Dataquest, Machine Learning, Portfolio, Project, Python
- Top KDnuggets tweets, Jul 13 – Jul 19: Bayesian #MachineLearning, Explained; Introducing JupyterLab - Jul 20, 2016.
Bayesian #MachineLearning, Explained; JupyterLab: the next generation of the #Jupyter Notebook; On the importance of democratizing #ArtificialIntelligence
Bayesian, Jupyter, Machine Learning, Python, Top tweets
- Multi-Task Learning in Tensorflow: Part 1 - Jul 20, 2016.
A discussion and step-by-step tutorial on how to use Tensorflow graphs for multi-task learning.
Pages: 1 2
Machine Learning, Neural Networks, TensorFlow
- KDnuggets™ News 16:n26, Jul 20: Bayesian Machine Learning, Explained; Start Learning Deep Learning; Big Data is in Trouble - Jul 20, 2016.
Bayesian Machine Learning, Explained; How to Start Learning Deep Learning; Why Big Data is in Trouble: They Forgot About Applied Statistics; Data Mining/Data Science "Nobel Prize": 2016 SIGKDD Innovation Award to Philip S. Yu
Applied Statistics, Bayesian, Big Data, Deep Learning, Machine Learning
- Bayesian Machine Learning, Explained - Jul 13, 2016.
Want to know about Bayesian machine learning? Sure you do! Get a great introductory explanation here, as well as suggestions where to go for further study.
Bayesian, Explained, LDA, Machine Learning
- KDnuggets™ News 16:n25, Jul 13: Top Machine Learning MOOCs; 5 Deep Learning Projects; Support Vector Machines Overview - Jul 13, 2016.
Top Machine Learning MOOCs and Online Lectures: A Comprehensive Overview; Support Vector Machines: A Simple Explanation; 5 Deep Learning Projects You Can No Longer Overlook; Why You Should Attend the Data Science Summit 2016 and 9 Talks To Be Excited About
Deep Learning, Machine Learning, MOOC, Overlook, Support Vector Machines
- Semi-supervised Feature Transfer: The Practical Benefit of Deep Learning Today? - Jul 12, 2016.
This post evaluates four different strategies for solving a problem with machine learning, where customized models built from semi-supervised "deep" features using transfer learning outperform models built from scratch, and rival state-of-the-art methods.
Pages: 1 2 3
API, Deep Learning, indico, Machine Learning, scikit-learn, Sentiment Analysis
- 5 Deep Learning Projects You Can No Longer Overlook - Jul 12, 2016.
There are a number of "mainstream" deep learning projects out there, but many more niche projects flying under the radar. Have a look at 5 such projects worth checking out.
- The Hard Problems AI Can’t (Yet) Touch - Jul 11, 2016.
It's tempting to consider the progress of AI as though it were a single monolithic entity,
advancing towards human intelligence on all fronts. But today's machine learning only addresses problems with simple, easily quantified objectives
AI, Machine Learning, Optimization, Reinforcement Learning, Supervised Learning
- Top Machine Learning MOOCs and Online Lectures: A Comprehensive Survey - Jul 11, 2016.
This post reviews Machine Learning MOOCs and online lectures for both the novice and expert audience.
Andrew Ng, Coursera, Deep Learning, edX, Machine Learning, MOOC, Nando de Freitas, Tom Mitchell, Udacity
- Glimpses & Impressions: Strata Silicon Valley AI + ML Review – Part Two - Jul 8, 2016.
Read some impressions from onsite visits to 2 companies during Strata Silicon Valley in March: Novumind and Numenta.
Artificial Intelligence, Machine Learning, Strata
- AI for Fun & Profit: Using the new Genie Cognitive Computing Platform for P2P Lending - Jul 8, 2016.
This tutorials uses the recently-released Genie (an acronym for General Evolving Networked Intelligence Engine) platform to learn from P2P (peer-to-peer) loan data. Experts and non-experts alike can leverage Genie to analyze Big Data, recognize objects, events, and patterns, and more.
Pages: 1 2 3
Artificial Intelligence, Cognitive Computing, Finance, Machine Learning
- Support Vector Machines: A Simple Explanation - Jul 7, 2016.
A no-nonsense, 30,000 foot overview of Support Vector Machines, concisely explained with some great diagrams.
Aylien, Explanation, Machine Learning, Support Vector Machines
- Glimpses & Impressions: Strata Silicon Valley AI + ML Review – Part One - Jul 7, 2016.
Read some impressions from a visit to Strata Silicon Valley in March. The focus is on integration of data science and machine learning tools, as well as the simplification of related processes.
Pages: 1 2
Artificial Intelligence, Data Science Tools, DataRobot, Domino, Machine Learning, Strata
- Top /r/MachineLearning Posts, June: Microsoft Videos, Machine Learning Training Pathway, Free Books! - Jul 5, 2016.
Microsoft Research Machine Learning Videos; Free Machine Learning Training Pathway; Andrew Ng's New Book; Coursera Removing Free Online Courses; Free Books!
Andrew Ng, Coursera, Free ebook, Machine Learning, Microsoft Research, Reddit
- What is Softmax Regression and How is it Related to Logistic Regression? - Jul 1, 2016.
An informative exploration of softmax regression and its relationship with logistic regression, and situations in which each would be applicable.
Logistic Regression, Machine Learning, Regression
- Three Impactful Machine Learning Topics at ICML 2016 - Jul 1, 2016.
This post discusses 3 particular tutorial sessions of impact from the recent ICML 2016 conference held in New York. Check out some innovative ideas on Deep Residual Networks, Memory Networks for Language Understanding, and Non-Convex Optimization.
Pages: 1 2
Deep Learning, ICML, Machine Learning, Natural Language Processing, Neural Networks, Optimization
- 5 More Machine Learning Projects You Can No Longer Overlook - Jun 28, 2016.
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.
- Regularization in Logistic Regression: Better Fit and Better Generalization? - Jun 24, 2016.
A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.
Cost Function, Logistic Regression, Machine Learning, Regression, Regularization
- Doing Data Science: A Kaggle Walkthrough Part 6 – Creating a Model - Jun 24, 2016.
In the final part of this 6 part series on the process of data science, and applying it to a Kaggle competition, building the predictive models is covered, and multiple algorithms are discussed.
Pages: 1 2
Kaggle, Machine Learning, Python
- Machine Learning Trends and the Future of Artificial Intelligence - Jun 22, 2016.
The confluence of data flywheels, the algorithm economy, and cloud-hosted intelligence means every company can now be a data company, every company can now access algorithmic intelligence, and every app can now be an intelligent app.
Algorithmia, Algorithms, Artificial Intelligence, Cloud, Machine Intelligence, Machine Learning
- New Andrew Ng Machine Learning Book Under Construction, Free Draft Chapters - Jun 20, 2016.
Check out the details on Andrew Ng's new book on building machine learning systems, and find out how to get your free copy of draft chapters as they are written.
Andrew Ng, Book, Free ebook, Machine Learning
- A Visual Explanation of the Back Propagation Algorithm for Neural Networks - Jun 17, 2016.
A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.
Algorithms, Backpropagation, Explanation, Machine Learning, Neural Networks
- Figuring Out the Algorithms of Intelligence - Jun 15, 2016.
Marvin Minsky, the father of AI, passed away this year. One of his inventions was the confocal microscope, which we used to take this high-resolution picture of a live brain circuit. Something in these cells allows them to automatically identify useful connections and establish useful networks out of information.
Algorithms, Artificial Intelligence, Deep Learning, Machine Learning, Neural Networks
- Machine Learning Classic: Parsimonious Binary Classification Trees - Jun 14, 2016.
Get your hands on a classic technical report outlining a three-step method of construction binary decision trees for multiple classification problems.
Decision Trees, Leo Breiman, Machine Learning, Statistics
- How to Select Support Vector Machine Kernels - Jun 13, 2016.
Support Vector Machine kernel selection can be tricky, and is dataset dependent. Here is some advice on how to proceed in the kernel selection process.
Machine Learning, Support Vector Machines
- AIG & Zurich on Machine Learning in Insurance - Jun 10, 2016.
Where and how can machine learning be practically applied by insurers? And is it worth it? Read the white paper from insurance experts at AIG and Zurich.
AIG, Insurance, Machine Learning, White Paper
- Where are the Opportunities for Machine Learning Startups? - Jun 8, 2016.
Machine learning has permeated data-driven businesses, which means almost all businesses. Here are a few areas where it’s possible that big corporations haven’t already eaten everybody’s lunch.
Machine Learning, Startup
- Open Source Machine Learning Degree - Jun 6, 2016.
A set of free resources for learning machine learning, inspired by similar open source degree resources. Find links to books and book-length lecture notes for study.
Free, Machine Learning, Mathematics, Open Source
- Ethics in Machine Learning – Summary - Jun 6, 2016.
Still worried about the AI apocalypse? Here we are discussion about the constraints and ethics for the machine learning algorithms to prevent it.
AI, Ethics, Machine Learning, MLconf, Seattle, WA
- 5 Reasons Machine Learning Applications Need a Better Lambda Architecture - Jun 2, 2016.
The Lambda Architecture enables a continuous processing of real-time data. It is a painful process that gets the job done, but at a great cost. Here is a simplified solution called as Lambda-R (ƛ-R) for the Relational Lambda.
Applications, Lambda Architecture, Machine Learning, Monte Zweben, Splice Machine
- Udacity Nanodegree Programs: Machine Learning, Data Analyst, and more - Jun 1, 2016.
Develop new skills. Be in demand. Accelerate your career with the credential that fast-tracks you to career success.
Machine Learning, Online Education, Udacity
- Top /r/MachineLearning Posts, May: TensorFlow Tricks; Machine Learning Tutorials; Google TPUs - Jun 1, 2016.
May on /r/MachineLearning was all about tutorials, TensorFlow, Google hardware, Deep Learning machine installations, and some laughs.
Google, Machine Learning, Reddit, TensorFlow, TPU, Tutorials
- Top 10 Open Dataset Resources on Github - May 31, 2016.
The top open dataset repositories on Github include a variety of data, freely available for use by researchers, practitioners, and students alike.
Datasets, GitHub, Machine Learning, Open Data
- Interacting with Machine Learning – Here is Why You Should Care - May 30, 2016.
The issue of designing new interactive interfaces with machine learning systems that best serve our needs and help us build and maintain trust is a central issue in AI. Read one researcher's take on this topic.
Machine Learning, Siri
- A Concise Overview of Standard Model-fitting Methods - May 27, 2016.
A very concise overview of 4 standard model-fitting methods, focusing on their differences: closed-form equations, gradient descent, stochastic gradient descent, and mini-batch learning.
Pages: 1 2
Cost Function, Gradient Descent, Machine Learning, Sebastian Raschka
- Top KDnuggets tweets, May 18-24: Google supercharges #MachineLearning, #DeepLearning tasks with TPU (Tensor Processing Unit) - May 25, 2016.
Stanford Crowd Course Initiative: #MachineLearning with #Python course; Practical Guide to Matrix Calculus for #DeepLearning; Build your own #DeepLearning Box < $1.5K
Google, Machine Learning, Python, Top tweets, TPU
- KDnuggets™ News 16:n19, May 25: Explain Machine Learning to Software Engineer; 5 Can’t Miss Machine Learning Projects - May 25, 2016.
How to Explain Machine Learning to a Software Engineer; 5 Machine Learning Projects You Can No Longer Overlook; Doing Data Science: A Kaggle Walkthrough Part 1 - Introduction; The Amazing Power of Word Vectors
Advice, Data Science, Kaggle, Machine Learning
- How to Explain Machine Learning to a Software Engineer - May 20, 2016.
How do you explain what machine learning is to the uninitiated software engineer? Read on for one perspective on doing so.
Automating, Machine Learning, Software Engineer
- Doing Data Science: A Kaggle Walkthrough Part 1 – Introduction - May 19, 2016.
This is the first post in a fantastic 6 part series covering the process of data science, and the application of the process to a Kaggle competition. Very thorough, and very insightful.
Kaggle, Machine Learning, Python, Titanic
- 5 Machine Learning Projects You Can No Longer Overlook - May 19, 2016.
We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.
Data Cleaning, Deep Learning, Machine Learning, Open Source, Overlook, Pandas, Python, scikit-learn, Theano
- An Introduction to Semi-supervised Reinforcement Learning - May 17, 2016.
A great overview of semi-supervised reinforcement learning, including general discussion and implementation information.
Pages: 1 2
Machine Learning, Reinforcement Learning
- The Good, The Bad, and The Deep Algorithms… at MLconf Seattle, May 20 - May 13, 2016.
MLconf in Seattle is a week away and we are getting a glimpse. Ethics in machine learning is the hottest conversation right now. Hear how a quantum molecular dynamic model made Uber service more reliable, get practical advice on next revolution in text search, and learn about multi-classification evaluation and ensemble learning.
Machine Learning, MLconf, Seattle, WA
- TPOT: A Python Tool for Automating Data Science - May 13, 2016.
TPOT is an open-source Python data science automation tool, which operates by optimizing a series of feature preprocessors and models, in order to maximize cross-validation accuracy on data sets.
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Automated Data Science, Automated Machine Learning, Hyperparameter, Machine Learning, Python, scikit-learn
- How Bing Predicts is forecasting everything from sports to political outcomes - May 13, 2016.
Bing Predicts is an innovative feature which now regularly makes headlines for its ability to analyze massive amounts of Web activity to forecast the outcomes of elections, voting-based reality TV shows, sports matchups and more.
Bing, Machine Learning, Microsoft
- Top KDnuggets tweets, May 4-10: Understanding the Bias-Variance Tradeoff; Python, MachineLearning, & Dueling Languages - May 11, 2016.
Understanding the Bias-Variance Tradeoff; Python, MachineLearning, & Dueling Languages; Why AI development is going to get even faster; Why Implement #MachineLearning Algorithms From Scratch?
AI, Bias, Failure, Machine Learning, Top tweets, Variance
- Why Implement Machine Learning Algorithms From Scratch? - May 6, 2016.
Even with machine learning libraries covering almost any algorithm implementation you could imagine, there are often still good reasons to write your own. Read on to find out what these reasons are.
Algorithms, Machine Learning
- From Insight-as-a-Service to Insightful Applications - May 5, 2016.
Applications that combine machine learning, AI, and domain knowledge have strong potential for industry and investors.
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Artificial Intelligence, Domain Knowledge, Evangelos Simoudis, Insights, Machine Learning
- Top /r/MachineLearning Posts, April: New Google Machine Learning Videos, Deep Learning Book, TensorFlow Playground - May 2, 2016.
Check out the most popular topics on Reddit's Machine Learning subreddit from April, including TensorFlow, deep learning, tutorials, self-reflection, and free books.
API, Book, Deep Learning, Machine Learning, Reddit, TensorFlow, xkcd
- Positioning a Machine Learning Company - Apr 28, 2016.
The classic guide for entrepreneurs preparing a pitch is Sequoia’s Business Plan Template. This post aims to be a mere addendum to that in the age of machine learning.
Business Strategy, Machine Learning, Startup, Startups, VC
- Machine Learning for Artists – Video lectures and notes - Apr 28, 2016.
Art has always been deep for those who appreciate it... but now, more than ever, deep learning is making a real impact on the art world. Check out this graduate course, and its freely-available resources, focusing on this very topic.
Art, Convolutional Neural Networks, Deep Learning, Machine Learning, Recurrent Neural Networks
- Top Data Science Courses on Udemy - Apr 27, 2016.
An overview of the very best that Udemy has to offer in data science education. Includes courses covering machine learning, Python, Hadoop, visualization, and more.
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Apache Spark, Brendan Martin, Data Science, Hadoop, Machine Learning, Python, Udemy
- Microsoft is Becoming M(ai)crosoft - Apr 25, 2016.
This post is an overview and discussion of Microsoft's increasing investment in, and approach to, artificial intelligence, and how their philosophy differs from their competitors.
AI, Artificial Intelligence, Computer Vision, Cortana, Machine Learning, Microsoft, Natural Language Processing, Speech Recognition
- How machine learning is making bots more human - Apr 25, 2016.
See how machine learning is making bots more human than ever - read the interview with a 17-year-old Chinese girl named XiaoIce who is actually a artificially intelligent chatbot.
Bots, Machine Learning, Microsoft
- Top 10 IPython Notebook Tutorials for Data Science and Machine Learning - Apr 22, 2016.
A list of 10 useful Github repositories made up of IPython (Jupyter) notebooks, focused on teaching data science and machine learning. Python is the clear target here, but general principles are transferable.
Data Science, Deep Learning, GitHub, IPython, Machine Learning, Python, Sebastian Raschka, TensorFlow
- Black Box Challenge Machine Learning Competition - Apr 21, 2016.
Take part in an unusual machine learning competition — program an agent (in Python) that can play a game with unknown rules.
Challenge, Competition, Machine Learning, Python
- Best Data Science, Machine Learning Blogs from Companies and Startups - Apr 21, 2016.
A collection of company data science blogs to follow and read. Top blogs have links to, and excerpts from, recent quality posts of particular interest.
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Advice, Blogs, Data Mining, Data Science, Machine Learning, Startup
- KDnuggets™ News 16:n14, Apr 20: Top 15 Frameworks for Machine Learning Experts; How to Grow Your Own Data Scientists - Apr 20, 2016.
Top 15 Frameworks for Machine Learning Experts; How to Grow Your Own Data Scientists; Association Rules and the Apriori Algorithm: A Tutorial; Automated Machine Learning: Changing the Game.
Automated, Data Science Platform, Machine Learning, Recommender Systems
- Top 15 Frameworks for Machine Learning Experts - Apr 19, 2016.
Either you are a researcher, start-up or big organization who wants to use machine learning, you will need the right tools to make it happen. Here is a list of the most popular frameworks for machine learning.
Data Science Tools, Deep Learning, Devendra Desale, Machine Learning, MLlib
- The MBA Data Science Toolkit: 8 resources to go from the spreadsheet to the command line - Apr 18, 2016.
A great guide for the MBA, or any relatively non-technical convert, for getting comfortable with the command line and other technical skills required to excel in data science.
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GitHub, Haskell, Machine Learning, Python, R, SQL
- Using Big Data Analytics To Prevent Crimes The “Minority Report” Way - Apr 18, 2016.
The idea of using artificial intelligence for the crime prevention has been around for more than a decade. In this post, we present four examples, including how using analytics, we can prevent a criminal from re-offending.
Big Data Analytics, Crime, Machine Learning, Surveillance
- What Developers Actually Need to Know About Machine Learning - Apr 14, 2016.
Some guidance on what, exactly, it is that developers need to know to get up to speed with machine learning.
Advice, Developers, Machine Learning
- Automated Machine Learning: Changing the Game - Apr 14, 2016.
Making sense of the mountains of data collected on a daily basis requires specialized data science skills that are hard to come by, and hard to keep. Augmented or even eliminated some of these specialized tasks with machine learning.
Automated, Data Science, DataRobot, Machine Learning
- Stochastic Depth Networks Accelerate Deep Network Training - Apr 7, 2016.
Read about the presentation and overview of a new deep neural network architectural method, and the response to some strong reaction that it brought about.
Architecture, Deep Learning, Deep Neural Network, Machine Learning
- KDnuggets™ News 16:n12, Apr 6: Top 10 Essential Books; Perfect Data Science Interview - Apr 6, 2016.
Top 10 Essential Books for the Data Enthusiast; How to Compute the Statistical Significance of Two Classifiers Performance Difference; The Secret to a Perfect Data Science Interview; If Hollywood Made Movies About Machine Learning Algorithms.
Books, Classifier, Complexity, Machine Learning
- Top /r/MachineLearning Posts, March: Hugs, Deep Learning Navigation, 3D Face Capture, AlphaGo! - Apr 4, 2016.
What's huggable, adversarial images for deep learning, overview of real-time 3D face capture and reenactment, deep learning quadcopter navigation, and a whole lot of AlphaGo!
Adversarial, AlphaGo, Computer Vision, Deep Learning, Go, Machine Learning, Reddit
- If Hollywood Made Movies About Machine Learning Algorithms - Apr 1, 2016.
A lighthearted take on the kind of movie Hollywood would produce if it took on machine learning algorithms.
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Algorithms, Humor, Machine Learning, Movies
- Avoiding Complexity of Machine Learning Problems - Mar 31, 2016.
Sometimes machine learning is the perfect tool for a task. Sometimes it is unnecessary overkill. Here are important lessons learned from the Quora engineering team.
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Complexity, Machine Learning, Quora, Xavier Amatriain
- Pattern Curators of the Cognitive Era - Mar 31, 2016.
Machine learning has a critical dependency on human learning. But not just on Data Scientists, but on legions of people who legions of individuals who prepare training data to guide algorithms.
Curation, Data Curation, IBM Watson, Machine Learning