In a typical ‘from think to buy’ customer journey, a customer goes through multiple touch points before zeroing in on the final product to buy. This is even more prominent in the case of e-commerce sales. It is relatively easier to track which are the different touch points the customer has encountered before making the final purchase.

TensorFlow is a very popular open-source library that is written in Python, C++ and CUDA. It’s uses span a range of tasks. Chief amongst them, is its use in machine learning applications for building neural networks. The TensorFlow library has seen many releases since 2015, and Google announced the latest update a couple of days back – TensorFlow 1.5. According to the team, they were monitoring “feedback about the programming style of TensorFlow, and how developers really wanted an imperative, define-by-run programming style”.

Learning of neural network takes place on the basis of a sample of the population under study. During the course of learning, compare the value delivered by output unit with actual value. After that adjust the weights of all units so to improve the prediction.

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.

R Function for Simulating Gaussian Processes

I’m happy to announce a new package that has recently appeared on CRAN, called “TSrepr” (version 1.0.0: https://…/package=TSrepr ). The TSrepr package contains methods of time series representations (dimensionality reduction, feature extraction or preprocessing) and several other useful helper methods and functions.