Large Scale Machine Learning for Payment Fraud Prevention Recorded at:

How advanced #machinelearning algorithms are applied at @PayPal for #fraud prevention. 

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  • Venkatesh Ramanathan is a senior data scientist at PayPal where he is working on building state-of-the-art tools for payment fraud detection.
  • Venkatesh has worked on various problems in the areas of Anti-Spam, Phishing Detection, and Face Recognition.
  • Data Science is an emerging field that allows businesses to effectively mine historical data and better understand consumer behavior.
  • This type of scientific data management approach is critical for any business to successfully launch its products and better serve its existing markets.

Venkatesh Ramanathan presents how advanced machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention.
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Try Deep Learning in Python now with a fully pre-configured VM

Try #DeepLearning in #Python now with a fully pre-configured VM

  • Try Deep Learning in Python now with a fully pre-configured VMI love to write about face recognition, image recognition and all the other cool things you can build with machine learning.
  • If you aren’t a long-time Linux user, it can be really hard to figure out how to get a system fully configured with all the required machine learning libraries and tools like TensorFlow, Theano, Keras, OpenCV, and dlib.
  • To make it simple for anyone to play around with machine learning, I’ve put together a simple virtual machine image that you can download and run without any complicated installation steps.The virtual machine image has Ubuntu Linux Desktop 16.04 LTS 64-bit pre-installed with the following machine learning tools:Python 3.5OpenCV 3.2 with Python 3 bindingsdlib 19.4 with Python 3 bindingsTensorFlow 1.0 for Python 3Keras 2.0 for Python 3Theanoface_recognition for Python 3 (for playing around with face recognition)PyCharm Community Edition already set up and ready to go for all these librariesConvenient code examples ready to run, right on the desktop!Even the webcam is preconfigured to work inside the Linux VM for OpenCV / face_recognition examples (as long as you set up your webcam to be accessible in the VMware settings).
  • So don’t the VirtualBox version unless you don’t have any other choice.You need VMware to run this virtual machine image.
  • Right-click on the code window and choose “Run” to run the current file in PyCharm.If you configure your webcam in VMware settings, you can access your webcam from inside the Linux virtual machine!

I love to write about face recognition, image recognition and all the other cool things you can build with machine learning. Whenever possible, I try to include code examples or even write libraries…
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OpenFace

Open source face recognition w/ deep neural networks in python  #DataScience #MachineLearning

  • Unlike other face representations, this embedding has the nice property that a larger distance between two face embeddings means that the faces are likely not of the same person.
  • Use a deep neural network to represent (or embed) the face on a 128-dimensional unit hypersphere.
  • Transform the face for the neural network.
  • The embedding is a generic representation for anybody’s face.
  • [Oct 15, 2015] TheNextWeb: Watch this open-source program recognize faces in real time

Read the full article, click here.


@gcosma1: “Open source face recognition w/ deep neural networks in python #DataScience #MachineLearning”


OpenFace is a Python and Torch implementation of
face recognition with deep neural networks and is based on
the CVPR 2015 paper
FaceNet: A Unified Embedding for Face Recognition and Clustering
by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google.
Torch allows the network to be executed on a CPU or with CUDA.


OpenFace