- 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
- 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
- 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
- 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.
Computer Vision, Data Preparation, Data Preprocessing, Javascript, Machine Learning, Natural Language Processing, NLP, Overlook, Python
- 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
- Top Machine Learning Libraries for Javascript - Jun 24, 2016.
Javascript may not be the conventional choice for machine learning, but there is no reason it cannot be used for such tasks. Here are the top libraries to facilitate machine learning in Javascript.
Andrej Karpathy, Convolutional Neural Networks, Deep Learning, Javascript, Machine Learning, Neural Networks
- 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
- 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
- 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 (Æ›-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 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 100 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning - Mar 29, 2016.
Stay on top of your data science skills game! Here’s a list of about 100 most active and interesting blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.
Pages: 1 2
Big Data, Blogs, Data Science, Deep Learning, Hadoop, Machine Learning
- Don’t Buy Machine Learning - Mar 28, 2016.
In many projects, the amount of effort spent on R&D on Machine Learning is usually a small fraction of the total effort, or it’s not even there because we plan it for a future phase after building the application first.
Advice, Industry, Machine Learning
- The Data Science Puzzle, Explained - Mar 10, 2016.
The puzzle of data science is examined through the relationship between several key concepts in the data science realm. As we will see, far from being concrete concepts etched in stone, divergent opinions are inevitable; this is but another opinion to consider.
Pages: 1 2
Artificial Intelligence, Data Mining, Data Science, Deep Learning, Explained, Machine Learning
- AI and Machine Learning: Top Influencers and Brands - Mar 8, 2016.
Onalytica gives us a new list of the top 100 Artifical Intelligence and Machine Learning influencers and brands, and provides some insight into the relationships between them.
About Gregory Piatetsky, AI, Influencers, Kirk D. Borne, Machine Learning, Onalytica, Top list
- scikit-feature: Open-Source Feature Selection Repository in Python - Mar 3, 2016.
scikit-feature is an open-source feature selection repository in python, with around 40 popular algorithms in feature selection research. It is developed by Data Mining and Machine Learning Lab at Arizona State University.
Data Mining, Data Science, Feature Extraction, Feature Selection, Machine Learning, Python
- Amazon Machine Learning: Nice and Easy or Overly Simple? - Feb 17, 2016.
Amazon Machine Learning is a predictive analytics service with binary/multiclass classification and linear regression features. The service is fast, offers a simple workflow but lacks model selection features and has slow execution times.
Amazon, Classification, Machine Learning, MLaaS
- Does Machine Learning allow opposites to attract? - Feb 11, 2016.
Most online dating sites use 'Netflix-style' recommendations which match people based on their shared interests and likes. What about those matches that work so well because people are so different - here is my example.
Love, Machine Learning, Online Dating, Recommendations
- Yahoo Releases the Largest-ever Machine Learning Dataset for Researchers - Jan 18, 2016.
Are you interested in massive amounts of data for research? Yahoo has just released the largest-ever machine learning dataset to the research community.
Anonymized, Dataset, Machine Learning, Yahoo
- 20 Questions to Detect Fake Data Scientists - Jan 1, 2016.
Hiring Data Scientists is no easy job, particularly when there are plenty of fake posers. Here is a handy list of questions to help separate the wheat from the chaff.
Data Scientist, Data Visualization, import.io, Kirk D. Borne, Machine Learning, Outliers
- What questions can data science answer? - Jan 1, 2016.
There are only five questions machine learning can answer: Is this A or B? Is this weird? How much/how many? How is it organized? What should I do next? We examine these questions in detail and what it implies for data science.
Pages: 1 2
Classification, Clustering, Machine Learning, Regression
- Tour of Real-World Machine Learning Problems - Dec 26, 2015.
The tour lists 20 interesting real-world machine learning problems for data science enthusiasts to learn by solving.
Datasets, Kaggle, Learning from Data, Machine Learning, Research, UCI
- Top 10 Machine Learning Projects on Github - Dec 14, 2015.
The top 10 machine learning projects on Github include a number of libraries, frameworks, and education resources. Have a look at the tools others are using, and the resources they are learning from.
Pages: 1 2
GitHub, Machine Learning, Matthew Mayo, Open Source, scikit-learn, Top 10
- Beyond One-Hot: an exploration of categorical variables - Dec 8, 2015.
Coding categorical variables into numbers, by assign an integer to each category ordinal coding of the machine learning algorithms. Here, we explore different ways of converting a categorical variable and their effects on the dimensionality of data.
Data Exploration, Machine Learning, Python, Will McGinnis
- 50 Useful Machine Learning & Prediction APIs - Dec 7, 2015.
We present a list of 50 APIs selected from areas like machine learning, prediction, text analytics & classification, face recognition, language translation etc. Start consuming APIs!
Pages: 1 2
API, Data Science, Face Recognition, IBM Watson, Image Recognition, Machine Learning, NLP, Sentiment Analysis
- 5 Tribes of Machine Learning – Questions and Answers - Nov 27, 2015.
Leading researcher Pedro Domingos answers questions on 5 tribes of Machine Learning, Master Algorithm, No Free Lunch Theorem, Unsupervised Learning, Ensemble methods, 360-degree recommender, and more.
Ensemble Methods, Machine Learning, Pedro Domingos, Recommender Systems
- Detecting In-App Purchase Fraud with Machine Learning - Nov 25, 2015.
Hacking applications allow users to make in-app purchases for free. With help from a few big games in the GROW data network we were able to build a model that classifies each purchase as real or fraud, with a very high level of accuracy.
Fraud Detection, Machine Learning, Online Games
- 7 Steps to Mastering Machine Learning With Python - Nov 19, 2015.
There are many Python machine learning resources freely available online. Where to begin? How to proceed? Go from zero to Python machine learning hero in 7 steps!
Pages: 1 2
7 Steps, Anaconda, Caffe, Deep Learning, Machine Learning, Matthew Mayo, Python, scikit-learn, Theano
- What No One Tells You About Real-Time Machine Learning - Nov 9, 2015.
Real-time machine learning has access to a continuous flow of transactional data, but what it really needs in order to be effective is a continuous flow of labeled transactional data, and accurate labeling introduces latency.
Dmitry Petrov, Machine Learning, Real-time
- 5 Best Machine Learning APIs for Data Science - Nov 5, 2015.
Machine Learning APIs make it easy for developers to develop predictive applications. Here we review 5 important Machine Learning APIs: IBM Watson, Microsoft Azure Machine Learning, Google Prediction API, Amazon Machine Learning API, and BigML.
Pages: 1 2
Amazon, API, Azure ML, BigML, DeZyre, Google, IBM Watson, Machine Learning
- The Best Advice From Quora on ‘How to Learn Machine Learning’ - Oct 15, 2015.
Top machine learning writers on Quora give their advice on learning machine learning, including specific resources, quotes, and personal insights, along with some extra nuggets of information.
Pages: 1 2
Books, Machine Learning, Matthew Mayo, MOOC, Quora, Sean McClure, Xavier Amatriain
- Does Deep Learning Come from the Devil? - Oct 9, 2015.
Deep learning has revolutionized computer vision and natural language processing. Yet the mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.
Berlin, Deep Learning, Machine Learning, Support Vector Machines, SVM, Vladimir Vapnik, Yandex, Zachary Lipton
- Topological Analysis and Machine Learning: Friends or Enemies? - Sep 29, 2015.
What is the interaction between Topological Data Analysis and Machine Learning ? A case study shows how TDA decomposition of the data space provides useful features for improving Machine Learning results.
Ayasdi, Machine Learning, random forests algorithm, Topological Data Analysis
- The Master Algorithm – new book by top Machine Learning researcher Pedro Domingos - Sep 25, 2015.
Wonderfully erudite, humorous, and easy to read, the Master Algorithm by top Machine Learning researcher Pedro Domingos takes you on a journey to visit the 5 tribes of Machine Learning experts and helps you understand what the Master Algorithm can be.
Algorithms, Book, Machine Learning, Pedro Domingos
- Top 10 Quora Machine Learning Writers and Their Best Advice - Sep 18, 2015.
Top Quora machine learning writers give their advice on pursuing a career in the field, academic research, and selecting and using appropriate technologies.
Machine Learning, Quora, random forests algorithm, Top 10, Xavier Amatriain, Yoshua Bengio
- 60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more - Sep 4, 2015.
Here is a great collection of eBooks written on the topics of Data Science, Business Analytics, Data Mining, Big Data, Machine Learning, Algorithms, Data Science Tools, and Programming Languages for Data Science.
Book, Brendan Martin, Data Mining, Data Science, Free ebook, Machine Learning, Python, R, SQL
- Gartner 2015 Hype Cycle: Big Data is Out, Machine Learning is in - Aug 28, 2015.
Which are the most hyped technologies today? Check out Gartner's latest 2015 Hype Cycle Report. Autonomous cars & IoT stay at the peak while big data is losing its prominence. Smart Dust is a new cool technology for the next decade!
Big Data, Citizen Data Scientist, Gartner, Machine Learning
- Recycling Deep Learning Models with Transfer Learning - Aug 14, 2015.
Deep learning exploits gigantic datasets to produce powerful models. But what can we do when our datasets are comparatively small? Transfer learning by fine-tuning deep nets offers a way to leverage existing datasets to perform well on new tasks.
Deep Learning, Image Recognition, ImageNet, Machine Learning, Neural Networks, Transfer Learning, Zachary Lipton
- Three Essential Components of a Successful Data Science Team - Aug 10, 2015.
A Data Science team, carefully constructed with the right set of dedicated professionals, can prove to be an asset to any organization,
Business Analyst, Data Engineer, Data Science Team, Machine Learning, Team
- 50+ Data Science and Machine Learning Cheat Sheets - Jul 14, 2015.
Gear up to speed and have Data Science & Data Mining concepts and commands handy with these cheatsheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark and Machine learning algorithms.
Cheat Sheet, Data Science, Django, Hadoop, Machine Learning, Python, R
- Can deep learning help find the perfect date? - Jul 10, 2015.
When a Machine Learning PhD student at University of Montreal starts using Tinder, he soon realises that something is missing in the dating app - the ability to predict to which girls he is attracted. Harm de Vries applies Deep Learning to assist in the pursuit of the perfect match.
Deep Learning, ICML, Love, Machine Learning, Online Dating, Predictive Analytics
- Top 20 R Machine Learning and Data Science packages - Jun 24, 2015.
We list out the top 20 popular Machine Learning R packages by analysing the most downloaded R packages from Jan-May 2015.
CRAN, Data Science, Machine Learning, R, R Packages, Top list
- Top 10 Machine Learning Videos on YouTube - Jun 23, 2015.
The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.
Andrew Ng, Computer Vision, Deep Learning, Geoff Hinton, Google, Grant Marshall, Machine Learning, Neural Networks, Robots, Video Games, Youtube
- In Machine Learning, What is Better: More Data or better Algorithms - Jun 17, 2015.
Gross over-generalization of “more data gives better results” is misguiding. Here we explain, in which scenario more data or more features are helpful and which are not. Also, how the choice of the algorithm affects the end result.
Big Data Hype, Data Quality, IMDb, Machine Learning, Quora, Xavier Amatriain
- Top 20 Python Machine Learning Open Source Projects - Jun 1, 2015.
We examine top Python Machine learning open source projects on Github, both in terms of contributors and commits, and identify most popular and most active ones.
GitHub, Machine Learning, Open Source, Python, scikit-learn
- Machine Learning Wars: Amazon vs Google vs BigML vs PredicSis - May 12, 2015.
Comparing 4 Machine Learning APIs: Amazon Machine Learning, BigML, Google Prediction API and PredicSis on a real data from Kaggle, we find the most accurate, the fastest, the best tradeoff, and a surprise last place.
Pages: 1 2
Amazon, BigML, Google, Louis Dorard, Machine Learning, PredicSis
- Awesome Public Datasets on GitHub - Apr 6, 2015.
A long, categorized list of large datasets (available for public use) to try your analytics skills on. Which one would you pick?
Pages: 1 2
Datasets, Finance, GitHub, Government, Machine Learning, NLP, Open Data, Time series data
- More Free Data Mining, Data Science Books and Resources - Mar 25, 2015.
More free resources and online books by leading authors about data mining, data science, machine learning, predictive analytics and statistics.
Book, Data Mining, Data Science, Free ebook, Machine Learning
- Interview: Vince Darley, King.com on the Serious Analytics behind Casual Gaming - Mar 18, 2015.
We discuss key characteristics of social gaming data, ML use cases at King, infrastructure challenges, major problems with A-B testing and recommendations to resolve them.
A/B Testing, Analytics, Gaming, Infrastructure, King.com, Machine Learning, Predictive Analysis, Vince Darley
- Machine Learning Table of Elements Decoded - Mar 11, 2015.
Machine learning packages for Python, Java, Big Data, Lua/JS/Clojure, Scala, C/C++, CV/NLP, and R/Julia are represented using a cute but ill-fitting metaphor of a periodic table. We extract the useful links.
Big Data Software, Java, Julia, Machine Learning, NLP, Python, R, Scala, scikit-learn, Weka
- 7 common mistakes when doing Machine Learning - Mar 7, 2015.
In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data. For Big Data, it pays off to analyze the data upfront and then design the modeling pipeline accordingly.
Pages: 1 2
Machine Learning, Mistakes, Overfitting, Regression, SVM
- Automatic Statistician and the Profoundly Desired Automation for Data Science - Feb 17, 2015.
The Automatic Statistician project by Univ. of Cambridge and MIT is pushing ahead the frontiers of automation for the selection and evaluation of machine learning models. In general, what does automation mean to Data Science?
Automation, Cambridge, Data Cleaning, Data Science, Machine Learning, MIT, Modeling, Statistician
- (Deep Learning’s Deep Flaws)’s Deep Flaws - Jan 26, 2015.
Recent press has challenged the hype surrounding deep learning, trumpeting several findings which expose shortcomings of current algorithms. However, many of deep learning's reported flaws are universal, affecting nearly all machine learning algorithms.
convnet, Deep Learning, Ian Goodfellow, Machine Learning, Neural Networks, Yoshua Bengio, Zachary Lipton
- The High Cost of Maintaining Machine Learning Systems - Jan 21, 2015.
Google researchers warn of the massive ongoing costs for maintaining machine learning systems. We examine how to minimize the technical debt.
Google, Machine Learning, Software Engineering, Technical Debt, Zachary Lipton
- Interview: Arno Candel, H2O.ai on the Basics of Deep Learning to Get You Started - Jan 20, 2015.
We discuss how Deep Learning is different from the other methods of Machine Learning, unique characteristics and benefits of Deep Learning, and the key components of H2O architecture.
Apache Spark, Arno Candel, Deep Learning, H2O, Machine Learning
- Why Azure ML is the Next Big Thing for Machine Learning? - Nov 17, 2014.
With advanced capabilities, free access, strong support for R, cloud hosting benefits, drag-and-drop development and many more features, Azure ML is ready to take the consumerization of ML to the next level.
Azure ML, Cloud Computing, Hadoop, Machine Learning, Marketplace, Microsoft Azure, Nate Silver, Predictive Analytics, Strata
- R and Hadoop make Machine Learning Possible for Everyone - Nov 16, 2014.
R and Hadoop make machine learning approachable enough for inexperienced users to begin analyzing and visualizing interesting data to start down the path in this lucrative field.
Data Science Skills, Hadoop, Hadoop 2.0, Joel Horwitz, LinkedIn, Machine Learning, R
- Will Deep Learning take over Machine Learning, make other algorithms obsolete? - Oct 27, 2014.
Will deep learning will take over machine learning and make other algorithms obsolete, or is it too complex to use on simpler problems? We look at both sides of this discussion.
Deep Learning, Machine Learning, Quora
- Most Viewed Machine Learning Talks at Videolectures - Sep 11, 2014.
Discover lectures from a variety of summer schools and conference tutorials on machine learning in this list of the top lectures on the subject from videolectures.net.
Machine Learning, Summer School, Tutorials, Videolectures
- Deep Learning – important resources for learning and understanding - Aug 21, 2014.
New and fundamental resources for learning about Deep Learning - the hottest machine learning method, which is approaching human performance level.
Deep Learning, Image Recognition, Machine Learning, Yann LeCun, Yoshua Bengio
- Sibyl: Google’s system for Large Scale Machine Learning - Aug 20, 2014.
A review of 2014 keynote talk about Sibyl, Google system for large scale machine learning. Parallel Boosting algorithm and several design principles are introduced.
Algorithms, Boosting, Google, Machine Learning, Sibyl
- Interview: Pedro Domingos: the Master Algorithm, new type of Deep Learning, great advice for young researchers - Aug 19, 2014.
Top researcher Pedro Domingos on useful maxims for Data Mining, Machine Learning as the Master Algorithm, new type of Deep Learning called sum-product networks, Big Data and startups, and great advice to young researchers.
Advice, Deep Learning, KDD-2014, Machine Learning, Pedro Domingos, Startups
- OpenML: Share, Discover and Do Machine Learning - Aug 11, 2014.
OpenML is designed to share, organize and reuse data, code and experiments, so that scientists can make discoveries more efficiently. It is an interesting idea to build a network of machine learning.
Kaggle, Machine Learning, OpenML, Ran Bi, Weka
- When Watson Meets Machine Learning - Jul 2, 2014.
Our report on a recent Cognitive Systems meetup co-sponsored by IBM Watson and NYU Center for Data Science, IBM Watson Ecosystem, and machine learning applications, from healthcare to cognitive toys. You will want Fang!
App, Cognitive Computing, IBM, Machine Learning, Ran Bi, Watson
- DLib: Library for Machine Learning - Jun 10, 2014.
DLib is an open source C++ library implementing a variety of machine learning algorithms, including classification, regression, clustering, data transformation, and structured prediction.
C++, DLib, Machine Learning, Open Source, Tools
- Vowpal Wabbit: Fast Learning on Big Data - May 26, 2014.
Vowpal Wabbit is a fast out-of-core machine learning system, which can learn from huge, terascale datasets faster than any other current algorithm. We also explain the cute name.
Fast Learning, John Langford, Machine Learning, Microsoft, Vowpal Wabbit
- Where to Learn Deep Learning – Courses, Tutorials, Software - May 26, 2014.
Deep Learning is a very hot Machine Learning techniques which has been achieving remarkable results recently. We give a list of free resources for learning and using Deep Learning.
Andrew Ng, Deep Learning, Geoff Hinton, Machine Learning, Yann LeCun
- Stacking the Deck: The Next Wave of Opportunity in Big Data - May 20, 2014.
A leading venture capitalist explains why Big Data infrastructure market is mostly mature and where lies the next big area of opportunities related to Big Data.
Chip Hazard, Full Stack Analytics, Machine Learning, Network Effects, Startups, VC
- Exclusive: Tamr at the New Frontier of Big Data Curation - May 19, 2014.
Our exclusive profile of Tamr (former Data Tamer), the latest startup from legendary Michael Stonebraker, which emerged from stealth mode to address the new field of Big Data Curation.
Andy Palmer, Data Curation, Machine Learning, Michael Brodie, Michael Stonebraker, Startups, Tamr
- Machine Learning in 7 Pictures - Mar 18, 2014.
Basic machine learning concepts of Bias vs Variance Tradeoff, Avoiding overfitting, Bayesian inference and Occam razor, Feature combination, Non-linear basis functions, and more - explained via pictures.
Basis functions, Bayesian, Concepts, Machine Learning, Pictures, Variance