Tag: Classification

Feed: Planet big data. Author: David Smith. T-Mobile, the global telecommunication company, is using R in production to automatically classify text messages to customer service  and route them to an agent that can help. The AI@T-mobile team used the keras library in R to build a natural language processing engine with Tensorflow, and deployed it to production as a docker container. The MRAN Time Machine ensures the container gets fixed R package versions for reproducibility.  While you may think first of Python for building production AI models, the keras library in R has already proven to be a first-class interface for building ... Read More

Feed: What's New. Amazon SageMaker now supports incremental learning for its built-in visual recognition algorithms – Image Classification and Object Detection. With incremental learning, you can initialize your model with knowledge learned from prior training. This enables the model to preserve the knowledge gained previously and extend it by training the model on new data. It becomes useful when your training data comes in batches over time. With this capability, you do not need to retrain your model from scratch every time you get new data, saving you time and compute resources ... Read More

Feed: R-bloggers. Author: R Views. Moving predictive machine learning algorithms into large-scale production environments can present many challenges. For example, problems arise when attempting to calculate prediction probabilities (“scores”) for many thousands of subjects using many thousands of features located on remote databases. xgboost (docs), a popular algorithm for classification and regression, and the model of choice in many winning Kaggle competitions, is no exception. However, to run xgboost, the subject-features matrix must be loaded into memory, a cumbersome and expensive process. Available solutions require using expensive high-memory machines, or implementing external memory across distributed machines (expensive and in beta) ... Read More

Feed: All - O'Reilly Media. Author: O'Reilly Media. Computer and paper on a desk (source: Pexels via Pixabay) Learn new topics and refine your skills with 140 live online training courses we opened up for November, December, and January on our learning platform. Artificial intelligence and machine learning Artificial Intelligence for Big Data, November 28-29 Essential Machine Learning and Exploratory Data Analysis with Python and Jupyter Notebook , December 3 Deep Learning for Machine Vision, December 4 Beginning Machine Learning with Scikit-Learn, December 5 Managed Machine Learning Systems and Internet of Things, December 5-6 Natural Language Processing (NLP) from Scratch, ... Read More

Feed: AWS Partner Network (APN) Blog. Author: AWS Admin. By Kris Skrinak, APN Machine Learning Segment Lead at AWSDo you want to become more familiar with how your company can use artificial intelligence (AI) and machine learning (ML) but feel a bit lost amongst the buzzwords and hype? Driving business outcomes with AI doesn’t need to be overwhelming. It’s all about exploring which business problems you want to solve, how good predictions can help you achieve those outcomes, and then taking practical steps to get there while implementing an organization-wide AI strategy. At Amazon Web Services (AWS), we’re seeing customers ... Read More

Feed: R-bloggers. Author: rOpenSci - open tools for open science. Last week Google and friends released the new major version of their OCR system: Tesseract 4. This release builds upon 2+ years of hard work and has completely overhauled the internal OCR engine. From the tesseract wiki: Tesseract 4.0 includes a new neural network-based recognition engine that delivers significantly higher accuracy (on document images) than the previous versions, in return for a significant increase in required compute power. On complex languages however, it may actually be faster than base Tesseract. We have now also updated the R package tesseract to ... Read More

Feed: R-bloggers. Author: Hugo Bowne-Anderson. Hugo Bowne-Anderson, the host of DataFramed, the DataCamp podcast, recently interviewed Peter Bull, a data scientist for social good and co-founder of Driven Data. Here is the podcast link. Hugo: Hi there, Peter, and welcome to Data Framed. Peter: Thanks, Hugo. I’m happy to be here. Hugo: I’m happy to have you here. I’m really excited to be talking about human centered design and data science, the role of design in data science, and what data science can tell us about human centered design as well. Before we get into this, I want to find ... Read More

Feed: R-bloggers. Author: Jurriaan Nagelkerke. Why ROC curves are a bad idea to explain your model to business people Summary In this blog we explain four most valuable evaluation plots to assess the business value of a predictive model. These plots are the cumulative gains, cumulative lift, response and cumulative response. Since these visualisations are not included in most popular model building packages or modules in R and Python, we show how you can easily create these plots for your own predictive models with our modelplotr r package and our modelplotpy python module (Prefer python? Read all about modelplotpy here!) ... Read More

Feed: SAS Blogs. Author: Suneel Grover. As machine learning takes its place in numerous advances within the marketing ecosystem, the interpretability of these modernized algorithmic approaches grows in importance. According to my SAS peer Ilknur Kaynar Kabul: We are surrounded with applications powered by machine learning, and we’re personally affected by the decisions made by machines more and more every day. Her writing has inspired me to consider how machine learning is used for customer analytics and personalized marketing. Emerging machine learning applications for business-to-consumer (B2C) use cases range from: These uses should sound familiar to any data-driven marketer. However, ... Read More

Feed: Liquid Web. Author: Andrej Walilko; More and more web application managers are considering adding graphics cards into their servers. And the numbers don’t lie; CPU has a high cost per core, and GPU has a low cost per core. For about the same investment, you could have a dozen or two additional CPU cores or a few thousand GPU cores. That’s the power of GPUs, or Graphics Processing Units.In fact, since June 2018, more of the new processing power from the top 500 supercomputers around the world comes from GPUs instead of CPUs. And companies like Microsoft, Facebook, Google, ... Read More