- 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.
- 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
- arXiv Paper Spotlight: Sampled Image Tagging and Retrieval Methods on User Generated Content - Jan 9, 2017.
Image tagging with user generated content in the wild, without the use of curated image datasets? Read more about this paper and its promising research.
- Artificial Neural Networks (ANN) Introduction, Part 1 - Dec 8, 2016.
This intro to ANNs will look at how we can train an algorithm to recognize images of handwritten digits. We will be using the images from the famous MNIST (Mixed National Institute of Standards and Technology) database.
- 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.
- 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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- 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
- 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.
- 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.
- 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.
- How Convolutional Neural Networks Work - Aug 31, 2016.
Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective.
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- Tricking Deep Learning - Apr 8, 2016.
Deep neural networks have had remarkable success with many tasks including image recognition. Read this overview regarding deep learning trickery, and why you should be cognizant.
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- How Shutterstock used Deep Learning to change the language of search - Apr 1, 2016.
How Shutterstock created computer-vision and Deep Learning technology that understands their 70 million-plus images and takes away the need for customers to type in descriptions and unreliable keywording. The technology relies on pixel data as its language of choice.
- Training a Computer to Recognize Your Handwriting - Mar 24, 2016.
The remarkable system of neurons is the inspiration behind a widely used machine learning technique called Artificial Neural Networks (ANN), used for image recognition. Learn how you can use this to recognize handwriting.
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- What Dog Breed is That? Let AI “fetch” it for you! - Feb 25, 2016.
Recently released AI app identifies dog breed information from pictures and mixes some fun too.
- Camelyon16 – Machine Learning Challenge in cancer detection - Jan 18, 2016.
Camelyon16 challenge in conjugation with IEEE International Symposium on Biomedical Imaging is here! You have to design and develop a system which can detect and localize metastatic regions in whole slide microscopic images.
- Update: Google TensorFlow Deep Learning Is Improving - Dec 17, 2015.
The recent open sourcing of Google's TensorFlow was a significant event for machine learning. While the original release was lacking in some ways, development continues and improvements are already being made.
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- 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!
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- MetaMind Mastermind Richard Socher: Uncut Interview - Oct 20, 2015.
In a wide-ranging interview, Richard Socher opens up about MetaMind, deep learning, the nature of corporate research, and the future of 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.
- Big Data Lessons from Microsoft “how-old” Experiment - May 19, 2015.
Salil Mehta examines Microsoft’s viral “How old do I look?” site, the limits of its age recognition, possible algorithms, and implications for Big Data analysis.
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- Deep Learning with Structure – a preview - May 6, 2015.
A big problem with Deep Learning networks is that their internal representation lacks interpretability. At the upcoming #DeepLearning Summit, Charlie Tang, a student of Geoff Hinton, will present an approach to address this concern - here is a preview.
- Inside Deep Learning: Computer Vision With Convolutional Neural Networks - Apr 9, 2015.
Deep Learning-powered image recognition is now performing better than human vision on many tasks. We examine how human and computer vision extracts features from raw pixels, and explain how deep convolutional neural networks work so well.
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- Watson Developer Cloud-Visual Recognition - Apr 3, 2015.
IBM Bluemix is a cloud platform which offers both Platform as a Service and Mobile Backend as a Service. Its services include Speech to Text, Text to Speech, Visual Recognition, Concept Insights, and Tradeoff Analytics.
- Deep Learning, The Curse of Dimensionality, and Autoencoders - Mar 12, 2015.
Autoencoders are an extremely exciting new approach to unsupervised learning and for many machine learning tasks they have already surpassed the decades of progress made by researchers handpicking features.
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- Top KDnuggets tweets, Feb 18-19: New Face Detection Algorithm to revolutionize search; How to transition from Excel to R - Feb 20, 2015.
Practical #DataScience in #Python #MachineLearning - nice intro; New Face Detection Algorithm to revolutionize search; Well written: How to Transition from Excel to R; Microsoft launches #Azure #MachineLearning Platform for #BigData, adds Python.
- Top KDnuggets tweets, Feb 9-15: Why limit yourself to “50 Shades of Grey?” R has 102 shades; Why Electric Cars Dont Have Better Batteries - Feb 16, 2015.
Why limit yourself to "50 Shades of Grey?" R has 102; Why Electric Cars Don't Have Better Batteries - a sad story of Envia; More evidence that #sports is a goldmine for #MachineLearning; Wedding with 200+ guests is 92% less likely to lead to divorce.
- Tinderbox: Automating Romance with Tinder and Eigenfaces - Feb 15, 2015.
Tinderbox is a software uses machine learning and image recognition to automate Tinder, a popular app for single meetings. The author describes his experience and feedback until it started to work too well.
- Top KDnuggets tweets, Feb 4-5: Clarifai Machine Learning software can understand what is in your videos - Feb 6, 2015.
Clarifai #MachineLearning software can understand what is in your videos; #BigData Lessons From @Netflix: comparing House of Cards and Macbeth insights; 2014 was the biggest year for #AI startups; Top Data Scientist @DPatil joined the #WhiteHouse as a data scientist-in-residence.
- Top KDnuggets tweets, Jan 19-20: 15 programming languages you need to know in 2015; R Programming fun: writing a Twitter bot - Jan 21, 2015.
15 #programming languages you need to know in 2015; #Facebook open sources its cutting-edge #DeepLearning tools; Simple Pictures that State-of-the-Art #AI Can't Recognize (yet); R Programming fun: writing a Twitter bot.
- Deep Learning can be easily fooled - Jan 14, 2015.
It is almost impossible for human eyes to label the images below to be anything but abstract arts. However, researchers found that Deep Neural Network will label them to be familiar objects with 99.99% confidence. The generality of DNN is questioned again.
- Top KDnuggets tweets, Dec 29 – Jan 04: A brilliant way to tell causation from correlation; Machine Learning Experts You Need to Know. - Jan 5, 2015.
SAS is n1 among major BI vendors whose users plan to discontinue use; How #MachineLearning, #BigData, and image recognition could revolutionize search; A brilliant way to tell causation from correlation; Machine Learning Experts You Need to Know: Geoff Hinton, Michael Jordan, Andrew Ng.
- National Data Science Bowl: Predict Ocean Health - Dec 16, 2014.
Enter the 1st ever National Data Science bowl, with 175K in prizes and build an algorithm to automate the plankton image identification across 100+ classes. Plankton are critically important to ecosystem, but traditional methods for measuring their populations are time consuming and cannot scale for large-scale studies.
- Top KDnuggets tweets, Dec 10-11: Which one is the bunny? Google new CAPTCHA trains AI; Big Data in 2015: Security, #IoT, data markets - Dec 12, 2014.
Which one is the bunny? Google new CAPTCHA trains #AI; Data Scientist Salary/Tools Survey finds #BigData scientists earn more; Microsoft brings the power of #MachineLearning to Office Online; Visual Sentiment Analysis: Researchers train #NeuralNets to rate images for #Happiness.
- Top KDnuggets tweets last week, Dec 1-7: Hilarious ! If programming languages were vehicles; Big Data Scientists have the highest salaries - Dec 8, 2014.
Hilarious! If programming languages were vehicles; Data Scientists who know #BigData tools have the highest salaries; How Google "Translates" Pictures Into Words Using #DeepLearning; Scary! Change in temperature in Netherlands over the last century.
- Top KDnuggets tweets, Dec 1-2: Hilarious: If programming languages were vehicles; How Google Translates Pictures Into Words - Dec 3, 2014.
Hilarious: If programming languages were vehicles; How Google "Translates" Pictures Into Words Using #DeepLearning, #BigData; Change in temperature in Netherlands over the last century; Forbes 50 Most Innovative Companies 3x more likely to use #BigData Analytics.
- Top KDnuggets tweets last week, Nov 17-23: Keep this #Python Cheat Sheet handy; Is #BigData The Most Hyped Technology? - Nov 24, 2014.
Keep this #Python Cheat Sheet handy when learning to code; Is #BigData The Most Hyped Technology Ever?; Huge advance by Stanford and Google: #AI software recognizes images, writes captions; 20 Insane Things That Correlate W/ Each Other.
- 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.
- New Beginnings in Facial Recognition - Jun 28, 2014.
Developments in neural networks and deep learning are bringing great improvements in facial recognition, which could have exciting (and scary) applications on platforms like Google Glass.
- Does Deep Learning Have Deep Flaws? - Jun 19, 2014.
A recent study of neural networks found that for every correctly classified image, one can generate an "adversarial", visually indistinguishable image that will be misclassified. This suggests potential deep flaws in all neural networks, including possibly a human brain.