[unable to retrieve full-text content]Curious about the future of Big Data and AI? Here’s what the trends have it in 2018 for innovations.
Original Post: Four Big Data Trends for 2018
[unable to retrieve full-text content]A series of stimulating conferences on AI and Sentiment Analysis in Hong Kong, Bangalore and London. Use code KDHK20 to receive 20% discount on any of these events.
Original Post: AI and Sentiment Analysis to help you move ahead of the competition
[unable to retrieve full-text content]A complete and unbiased comparison of the three most common Cloud Technologies for Machine Learning as a Service.
Original Post: Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI
[unable to retrieve full-text content]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!
Original Post: Visual Aesthetics: Judging photo quality using AI techniques
[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