[unable to retrieve full-text content]Also A Primer on Web Scraping in R; Elasticsearch for Dummies; Generative Adversarial Networks, an overview,
Original Post: KDnuggets™ News 18:n03, Jan 17: Top 10 TED Talks on Data Science, Machine Learning; How Docker Can Help You Become A More Effective Data Scientist
[unable to retrieve full-text content]This is the narrative of a typical AI Sunday, where I decided to look at building a sequence to sequence (seq2seq) model based chatbot using some already available sample code and data from the Cornell movie database.
Original Post: A Day in the Life of an AI Developer
[unable to retrieve full-text content]Edge-based inferencing will become a foundation of all AI-infused applications in the Internet of Things and People and the majority of new IoT&P application-development projects will involve building the AI-driven smarts for deployment to edge devices for various levels of local sensor-driven inferencing.
Original Post: Local AI Inferencing Will Become Standard In Edge Applications In 2018
by Le Zhang (Data Scientist, Microsoft) and Graham Williams (Director of Data Science, Microsoft) As an in-memory application, R is sometimes thought to be constrained in performance or scalability for enterprise-grade applications. But by deploying R in a high-performance cloud environment, and by leveraging the scale of parallel architectures and dedicated big-data technologies, you can build applications using R that provide the necessary computational efficiency, scale, and cost-effectiveness. We identify four application areas and associated applications and Azure services that you can use to deploy R in enterprise applications. They cover the tasks required to prototype, build, and operationalize an enterprise-level data science and AI solution. In each of the four, there are R packages and tools specifically for accelerating the development of desirable analytics. Below is a brief introduction of each. Cloud resource management and operation Cloud computing instances…
Original Post: Services and tools for building intelligent R applications in the cloud
The letters and numbers you entered did not match the image. Please try again. As a final step before posting your comment, enter the letters and numbers you see in the image below. This prevents automated programs from posting comments. Having trouble reading this image? View an alternate.
Original Post: How to implement neural networks in R
[unable to retrieve full-text content]This year, RE-WORK will be continuing the Global Healthcare Series, focusing on the AI and deep learning tools and techniques set to revolutionise healthcare applications, medicine & diagnostics. Save an additional 20% on already discounted passes with the code: KDNUGGETS
Original Post: AI and Deep Learning in Healthcare – save with code KDnuggets
[unable to retrieve full-text content]Democratization is defined as the action/development of making something accessible to everyone, to the “common masses.” AI | ML | DL technology stacks are complicated systems to tune and maintain, expertise is limited, and one minimal change of the stack can lead to failure.
Original Post: Democratizing Artificial Intelligence, Deep Learning, Machine Learning with Dell EMC Ready Solutions
[unable to retrieve full-text content]A comprehensive and diverse compilation of TED talks to understand the big picture of AI and Machine Learning.
Original Post: Top 10 TED Talks for Data Scientists and Machine Learning Engineers
[unable to retrieve full-text content]Quantum Machine Learning: An Overview; How to build a Successful Advanced Analytics Department; Top Data Science, Machine Learning Courses from Udemy; Supercharging Visualization with Apache Arrow; The Convergence of AI and Blockchain: What’s the deal?
Original Post: KDnuggets™ News 18:n02, Jan 10: Quantum Machine Learning; AI & Blockchain Convergence; Building a Successful Analytics Dept
[unable to retrieve full-text content]H2O.ai recently launched Driverless AI, which speeds up data science workflows by automating feature engineering, model tuning, ensembling, and model deployment.
Original Post: Driverless AI: Fast, Accurate, Interpretable AI