- Deep learning doesn’t need to be a black box - Feb 5, 2021.
The cultural perception of AI is often suspect because of the described challenges in knowing why a deep neural network makes its predictions. So, researchers try to crack open this "black box" after a network is trained to correlate results with inputs. But, what if the goal of explainability could be designed into the network's architecture -- before the model is trained and without reducing its predictive power? Maybe the box could stay open from the beginning.
- Support Vector Machine for Hand Written Alphabet Recognition in R - Jan 27, 2021.
We attempt to break down a problem of hand written alphabet image recognition into a simple process rather than using heavy packages. This is an attempt to create the data and then build a model using Support Vector Machines for Classification.
- KDnuggets™ News 21:n04, Jan 27: The Ultimate Scikit-Learn Machine Learning Cheatsheet; Building a Deep Learning Based Reverse Image Search - Jan 27, 2021.
The Ultimate Scikit-Learn Machine Learning Cheatsheet; Building a Deep Learning Based Reverse Image Search; Data Engineering — the Cousin of Data Science, is Troublesome; Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning; Popular Machine Learning Interview Questions
- Building a Deep Learning Based Reverse Image Search - Jan 22, 2021.
Following the journey from unstructured data to content based image retrieval.
- Build Dog Breeds Classifier Step By Step with AWS Sagemaker - Jun 17, 2020.
This post takes you through the basic steps for creating a cloud-based deep learning dog classifier, with everything accomplished from the AWS Management Console.
- Deep Learning for Detecting Pneumonia from X-ray Images - Jun 5, 2020.
This article covers an end to end pipeline for pneumonia detection from X-ray images.
- 5 Machine Learning Papers on Face Recognition - May 28, 2020.
This article will highlight some of that research and introduce five machine learning papers on face recognition.
- Interactive Machine Learning Experiments - May 26, 2020.
Dive into experimenting with machine learning techniques using this open-source collection of interactive demos built on multilayer perceptrons, convolutional neural networks, and recurrent neural networks. Each package consists of ready-to-try web browser interfaces and fully-developed notebooks for you to fine tune the training for better performance.
- Satellite Image Analysis with fast.ai for Disaster Recovery - May 14, 2020.
We were asked to build ML models using the novel xBD dataset provided by the organizers to estimate damage to infrastructure with the goal of reducing the amount of human labour and time required to plan an appropriate response. This article will focus on the technical aspects of our solution and share our experiences.
- Google Open Sources SimCLR, A Framework for Self-Supervised and Semi-Supervised Image Training - Apr 27, 2020.
The new framework uses contrastive learning to improve image analysis in unlabeled datasets.
- Image Recognition For Building Your Perfect Store - Mar 3, 2020.
In this blog, we outline what a perfect store strategy is, and how to achieve it.
- Image Recognition and Object Detection in Retail - Feb 26, 2020.
“According to Gartner, by 2020, 85% of customer interactions in the retail industry will be managed by AI.”
- Easy Image Dataset Augmentation with TensorFlow - Feb 13, 2020.
What can we do when we don't have a substantial amount of varied training data? This is a quick intro to using data augmentation in TensorFlow to perform in-memory image transformations during model training to help overcome this data impediment.
- Pedestrian Detection Using Non Maximum Suppression Algorithm - Dec 17, 2019.
Read this overview of a complete pipeline for detecting pedestrians on the road.
- This Microsoft Neural Network can Answer Questions About Scenic Images with Minimum Training - Oct 21, 2019.
Recently, a group of AI experts from Microsoft Research published a paper proposing a method for scene understanding that combines two key tasks: image captioning and visual question answering (VQA).
- A Single Function to Streamline Image Classification with Keras - Sep 23, 2019.
We show, step-by-step, how to construct a single, generalized, utility function to pull images automatically from a directory and train a convolutional neural net model.
- A 2019 Guide to Human Pose Estimation - Aug 28, 2019.
Human pose estimation refers to the process of inferring poses in an image. Essentially, it entails predicting the positions of a person’s joints in an image or video. This problem is also sometimes referred to as the localization of human joints.
- A 2019 Guide to Semantic Segmentation - Aug 12, 2019.
Semantic segmentation refers to the process of linking each pixel in an image to a class label. These labels could include a person, car, flower, piece of furniture, etc., just to mention a few. We’ll now look at a number of research papers on covering state-of-the-art approaches to building semantic segmentation models.
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- Introduction to Image Segmentation with K-Means clustering - Aug 9, 2019.
Image segmentation is the classification of an image into different groups. Many kinds of research have been done in the area of image segmentation using clustering. In this article, we will explore using the K-Means clustering algorithm to read an image and cluster different regions of the image.
- A 2019 Guide to Object Detection - Aug 1, 2019.
Object detection has been applied widely in video surveillance, self-driving cars, and object/people tracking. In this piece, we’ll look at the basics of object detection and review some of the most commonly-used algorithms and a few brand new approaches, as well.
- An Overview of Human Pose Estimation with Deep Learning - Jun 28, 2019.
Human Pose Estimation is one of the main research areas in computer vision. The reason for its importance is the abundance of applications that can benefit from such a technology. Here's an introduction to the different techniques used in Human Pose Estimation based on Deep Learning.
- K-means Clustering with Dask: Image Filters for Cat Pictures - Jun 18, 2019.
How to recreate an original cat image with least possible colors. An interesting use case of Unsupervised Machine Learning with K Means Clustering in Python.
- Boost Your Image Classification Model - May 27, 2019.
Check out this collection of tricks to improve the accuracy of your classifier.
- Large-Scale Evolution of Image Classifiers - May 16, 2019.
Deep neural networks excel in many difficult tasks, given large amounts of training data and enough processing power. The neural network architecture is an important factor in achieving a highly accurate model... Techniques to automatically discover these neural network architectures are, therefore, very much desirable.
- Object Detection with Luminoth - Mar 13, 2019.
In this article you will learn about Luminoth, an open source computer vision library which sits atop Sonnet and TensorFlow and provides object detection for images and video.
- People Tracking using Deep Learning - Mar 12, 2019.
Read this overview of people tracking and how deep learning-powered computer vision has allowed for phenomenal performance.
- How to do Everything in Computer Vision - Feb 27, 2019.
The many standard tasks in computer vision all require special consideration: classification, detection, segmentation, pose estimation, enhancement and restoration, and action recognition. Let me show you how to do everything in Computer Vision with Deep Learning!
- Building an image search service from scratch - Jan 30, 2019.
By the end of this post, you should be able to build a quick semantic search model from scratch, no matter the size of your dataset.
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- Deep learning in Satellite imagery - Dec 26, 2018.
This article outlines possible sources of satellite imagery, what its properties are and how this data can be utilised using R.
- Implementing ResNet with MXNET Gluon and Comet.ml for Image Classification - Dec 14, 2018.
Whether MXNet is an entirely new framework for you or you have used the MXNet backend while training your Keras models, this tutorial illustrates how to build an image recognition model with an MXNet resnet_v1 model.
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- Solve any Image Classification Problem Quickly and Easily - Dec 13, 2018.
This article teaches you how to use transfer learning to solve image classification problems. A practical example using Keras and its pre-trained models is given for demonstration purposes.
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- Building an Image Classifier Running on Raspberry Pi - Oct 9, 2018.
The tutorial starts by building the Physical network connecting Raspberry Pi to the PC via a router. After preparing their IPv4 addresses, SSH session is created for remotely accessing of the Raspberry Pi. After uploading the classification project using FTP, clients can access it using web browsers for classifying images.
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- Semantic Segmentation: Wiki, Applications and Resources - Oct 4, 2018.
An extensive overview covering the features of Semantic Segmentation and possible uses for it, including GeoSensing, Autonomous Drive, Facial Recognition and more.
- Data Capture – the Deep Learning Way - Sep 21, 2018.
An overview of how an information extraction pipeline built from scratch on top of deep learning inspired by computer vision can shakeup the established field of OCR and data capture.
- Data Augmentation For Bounding Boxes: Rethinking image transforms for object detection - Sep 19, 2018.
Data Augmentation is one way to battle this shortage of data, by artificially augmenting our dataset. In fact, the technique has proven to be so successful that it's become a staple of deep learning systems.
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- Object Detection and Image Classification with YOLO - Sep 10, 2018.
We explain object detection, how YOLO algorithm can help with image classification, and introduce the open source neural network framework Darknet.
- How GOAT Taught a Machine to Love Sneakers - Aug 7, 2018.
Embeddings are a fantastic tool to create reusable value with inherent properties similar to how humans interpret objects. GOAT uses deep learning to generate these for their entire sneaker catalogue.
- KDnuggets™ News 18:n27, Jul 18: Data Scientist was the sexiest job until…; Text Mining on the Command Line; Does PCA Really Work? - Jul 18, 2018.
Also: What is Minimum Viable (Data) Product?; Beating the 4-Year Slump: Mid-Career Growth in Data Science; GDPR after 2 months - What does it mean for Machine Learning?; Basic Image Data Analysis Using Numpy and OpenCV; fast.ai Deep Learning Part 2 Complete Course Notes
- Analyze a Soccer (Football) Game Using Tensorflow Object Detection and OpenCV - Jul 10, 2018.
For the data scientist within you let's use this opportunity to do some analysis on soccer clips. With the use of deep learning and opencv we can extract interesting insights from video clips
- Inside the Mind of a Neural Network with Interactive Code in Tensorflow - Jun 29, 2018.
Understand the inner workings of neural network models as this post covers three related topics: histogram of weights, visualizing the activation of neurons, and interior / integral gradients.
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- Top KDnuggets tweets, May 16-22: Python eats away at R; Data Science Plan 2018 - May 23, 2018.
Also: AI is learning to see in the dark; Introducing state of the art text classification with universal language models; Top 100 Books for Data Scientists.
- Top Stories, May 14-20: Data Science vs Machine Learning vs Data Analytics vs Business Analytics; Implement a YOLO Object Detector from Scratch in PyTorch - May 21, 2018.
Also: An Introduction to Deep Learning for Tabular Data; 9 Must-have skills you need to become a Data Scientist, updated; GANs in TensorFlow from the Command Line: Creating Your First GitHub Project; Complete Guide to Build ConvNet HTTP-Based Application
- How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1 - May 17, 2018.
The best way to go about learning object detection is to implement the algorithms by yourself, from scratch. This is exactly what we'll do in this tutorial.
- How to Organize Data Labeling for Machine Learning: Approaches and Tools - May 16, 2018.
The main challenge for a data science team is to decide who will be responsible for labeling, estimate how much time it will take, and what tools are better to use.
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- 50+ Useful Machine Learning & Prediction APIs, 2018 Edition - May 1, 2018.
Extensive list of 50+ APIs in Face and Image Recognition ,Text Analysis, NLP, Sentiment Analysis, Language Translation, Machine Learning and prediction.
- Building Convolutional Neural Network using NumPy from Scratch - Apr 26, 2018.
In this article, CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), ReLU, and max pooling.
- Using Tensorflow Object Detection to do Pixel Wise Classification - Mar 29, 2018.
Tensorflow recently added new functionality and now we can extend the API to determine pixel by pixel location of objects of interest. So when would we need this extra granularity?
- Exploring DeepFakes - Mar 27, 2018.
In this post, I explore the capabilities of this tech, describe how it works, and discuss potential applications.
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- KDnuggets™ News 18:n10, Mar 7: Functional Programming in Python; Surviving Your Data Science Interview; Easy Image Recognition with Google Tensorflow - Mar 7, 2018.
- Is Google Tensorflow Object Detection API the Easiest Way to Implement Image Recognition? - Mar 1, 2018.
There are many different ways to do image recognition. Google recently released a new Tensorflow Object Detection API to give computer vision everywhere a boost.
- Building a Toy Detector with Tensorflow Object Detection API - Feb 13, 2018.
This project is second phase of my popular project - Is Google Tensorflow Object Detection API the easiest way to implement image recognition? Here I extend the API to train on a new object that is not part of the COCO dataset.
- Plot2txt for quantitative image analysis - Jan 19, 2018.
Plot2txt converts images into text and other representations, helping create semi-structured data from binary, using a combination of machine learning and other algorithms.
- Visual Aesthetics: Judging photo quality using AI techniques - Jan 18, 2018.
We built a deep learning system that can automatically analyze and score an image for aesthetic quality with high accuracy. Check the demo and see your photo measures up!
- The Value of Semi-Supervised Machine Learning - Jan 17, 2018.
This post shows you how to label hundreds of thousands of images in an afternoon. You can use the same approach whether you are labeling images or labeling traditional tabular data (e.g, identifying cyber security atacks or potential part failures).
- Generative Adversarial Networks, an overview - Jan 15, 2018.
In this article, we’ll explain GANs by applying them to the task of generating images. One of the few successful techniques in unsupervised machine learning, and are quickly revolutionizing our ability to perform generative tasks.
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- Oak Ridge National Laboratory: Postdoc, Imaging, Signals and Machine Learning - Nov 10, 2017.
The Imaging, Signals, and Machine Learning (ISML) group at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate with expertise in computer vision/image processing and data analytics.
- Real World Deep Learning: Neural Networks for Smart Crops - Nov 7, 2017.
The advances in image classification, object detection, and semantic segmentation using deep Convolutional Neural Networks, which spawned the availability of open source tools such as Caffe and TensorFlow (to name a couple) to easily manipulate neural network graphs... made a very strong case in favor of CNNs for our classifier.
- Deep Learning for Object Detection: A Comprehensive Review - Oct 6, 2017.
By the end of this post, we will hopefully have gained an understanding of how deep learning is applied to object detection, and how these object detection models both inspire and diverge from one another.
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- Object Detection: An Overview in the Age of Deep Learning - Sep 13, 2017.
Like many other computer vision problems, there still isn’t an obvious or even “best” way to approach the problem of object recognition, meaning there’s still much room for improvement.
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- Detecting Facial Features Using Deep Learning - Sep 4, 2017.
A challenging task in the past was detection of faces and their features like eyes, nose, mouth and even deriving emotions from their shapes. This task can be now “magically” solved by deep learning and any talented teenager can do it in a few hours.
- Using AI to Super Compress Images - Aug 21, 2017.
Neural Network algorithms are showing promising results for different complex problems. Here we discuss how these algorithms are used in image compression.
- A Guide to Instagramming with Python for Data Analysis - Aug 17, 2017.
I am writing this article to show you the basics of using Instagram in a programmatic way. You can benefit from this if you want to use it in a data analysis, computer vision, or any other cool project you can think of.
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- First Steps of Learning Deep Learning: Image Classification in Keras - Aug 16, 2017.
Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over, this post is for you!
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- KDnuggets™ News 17:n31, Aug 16: Data Science Primer: Basic Concepts; Python vs R vs rest - Aug 16, 2017.
Also: What Artificial Intelligence and Machine Learning Can Do-And What It Can't; How Convolutional Neural Networks Accomplish Image Recognition?; Making Predictive Models Robust: Holdout vs Cross-Validation; The Machine Learning Abstracts: Support Vector Machines
- How Convolutional Neural Networks Accomplish Image Recognition? - Aug 9, 2017.
Image recognition is very interesting and challenging field of study. Here we explain concepts, applications and techniques of image recognition using Convolutional Neural Networks.
- Machine Learning and Misinformation - Jul 27, 2017.
The creative aspects of machine learning are overshadowed by visions of an autonomous future, but machine learning is a powerful tool for communication. Most machine learning in today’s products is related to understanding.
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- Medical Image Analysis with Deep Learning , Part 4 - Jul 11, 2017.
This is the fourth installment of this series, and covers medical images and their components, medical image formats and their format conversions. The goal is to develop knowledge to help us with our ultimate goal — medical image analysis with deep learning.
- Medical Image Analysis with Deep Learning , Part 3 - Jun 15, 2017.
In this article we will focus — basic deep learning using Keras and Theano. We will do 2 examples one using keras for basic predictive analytics and other a simple example of image analysis using VGG.
- Data preprocessing for deep learning with nuts-ml - May 30, 2017.
Nuts-ml is a new data pre-processing library in Python for GPU-based deep learning in vision. It provides common pre-processing functions as independent, reusable units. These so called ‘nuts’ can be freely arranged to build data flows that are efficient, easy to read and modify.
- What Data You Analyzed – KDnuggets Poll Results and Trends - Apr 26, 2017.
Image/video data analysis is surging, JSON replacing XML, anonymized data usage is growing in US and Europe (but not in Asia), itemsets and Twitter analysis is declining - some of the highlights of KDnuggets Poll on data types used.
- KDnuggets™ News 17:n16, Apr 26: Awesome Deep Learning: Most Cited Deep Learning Papers; Data Science for the Layman - Apr 26, 2017.
Awesome Deep Learning: Most Cited Deep Learning Papers; Data Science for the Layman; Best Data Science Courses from Udemy; Negative Results on Negative Images: Major Flaw in Deep Learning?
- Negative Results on Negative Images: Major Flaw in Deep Learning? - Apr 20, 2017.
This is an overview of recent research outlining the limitations of the capabilities of image recognition using deep neural networks. But should this really be considered a "limitation?"
- Medical Image Analysis with Deep Learning , Part 2 - Apr 13, 2017.
In this article we will talk about basics of deep learning from the lens of Convolutional Neural Nets. We plan to use this knowledge to build CNNs in the next post and use Keras to develop a model to predict lung cancer.
- Medical Image Analysis with Deep Learning - Apr 6, 2017.
In this article, I start with basics of image processing, basics of medical image format data and visualize some medical data.
- New James Bond is a Data Scientist: Data Science Challenge sponsored by UK MI5 and MI6 - Apr 5, 2017.
Two Data Science challenges were launched by UK Government agencies, including MI5 and MI6. One challenge involves classifying vehicles from aerial images, and another analyzing crisis reports. Can you take part and be the next James Bond?
- 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.