Database-as-a-service offers multiple potential benefits, including lower database licensing and infrastructure costs, faster time to application development, and reduced administration overheads. These benefits are most likely to be experienced by database administrators and architects, although senior decision-makers and business users also stand to gain from having on-demand access to database services, rather than waiting for databases to be configured and deployed on dedicated physical or virtual server infrastructure. While 451 Research anticipates growing adoption of database-as-a-service (DBaaS), adoption is currently nascent compared with other cloud services, as enterprises look to make the most of their investments in on-premises database deployments, and also to identify the most appropriate workloads for transition or migration to DBaaS. This webinar explores the factors shaping those adoption trends, including the potential benefits and challenges to DBaaS adoption, the economics of the cloud as they relate to database workloads, and adoption lifecycles.

I am planning on implementing an LSTM RNN on an FPGA as part of my research with a professor. I do not have formal ML training (but I have taken the Stanford-run Coursera course). It is my understanding that RNNs are used when dealing with sequences (such as text and audio), where history (past items) may provide some relevant context, and CNNs are generally used for image recognition. Is this a valid generalization, or are there other limitations as well? Are there examples of using RNNs to process images or use CNNs for some sequence?

I currently work as a Data Scientist for Informatica and I thought I'd share my process for learning new things. Recently I've been wanting to explore more into Deep Learning, especially Machine Vision and Natural Language Processing. I've been procrastinating a lot, mostly because it's been summer, but now that it's fall and starting to cool down and get dark early, I'm going to be spending more time learning when it's dark out. And the thing that deeply interests me is Deep Learning and Artificial Intelligence, partly out of intellectual curiosity and partly out of greed, as most businesses and products will incorporate Deep Learning/ML in some way. I started doing research and realized that an understanding and knowledge of Deep Learning was within my reach, but I also realized that I still have a lot to learn, more than I initially thought.

Slate and Future Tense are discussing Mr. Robot and the technological world it portrays throughout the show's second season. You can follow this conversation on Future Tense, and Slate Plus members can also listen to Hacking Mr. Robot, a members-only podcast series featuring Lily Newman and Fred Kaplan. In this episode of Hacking Mr. Robot, Fred and Lily discuss Episode 11. Fred Kaplan is the author of Dark Territory: The Secret History of Cyber War.

Two things happen like clockwork every September: Apple releases a new operating system to go on its new phones. And then everyone complaints that their old phones feel slow. The phenomenon – which can be seen in the huge surge in people searching for "iPhone slow" every year – is the result of a combination of factors. New software, the advertising of much faster new phones and heightened expectations that aren't always met combine to make the slab of glass and metal sitting on your phone feel like it's slowed down. But this year that seems to be even worse. And there seems to be something in it, after a huge number of complaints that iOS 10 slows down people's phones.

Machine Learning is a great way to use your existing data to identify trends going forward. In this webinar, Gregory Deckler will show us step-by-step how to get started using this very exciting Azure Service, using Power BI and R script. Based on your requests, this webinar will show you how to bring together Azure ML, Power BI, and R in a single solution! Power BI MVP Gregory Deckler will demonstrate how to use R to bridge the gap between Azure ML and Power BI in order to build predictive analytics directly into your data model and reports. Greg is a Director at Fusion Alliance and the Solution Director of Cloud Services.

Leveraging data to predict customer satisfaction is more important than ever––it can help your business engage with customers proactively, improve operations, reduce customer churn, and improve customer relationships over the long-term. Think better support experiences for everyone. Join us for a unique opportunity to learn from guest speaker Ken Landoline, Principal Analyst at Ovum, a market-leading research and consulting business. Adrian McDermott, Senior Vice President of Product Development at Zendesk, will join Landoline to discuss how your business can use machine learning to provide a better customer experience. This webinar is complimentary, so feel free to spread the word and share with colleagues who you think might benefit from this live, interactive session.

This tutorial will explain some practical tips about how to train a neural machine translation system. It is partly based around examples using the lamtram toolkit. Note that this will not cover the theory behind NMT in detail, nor is it a survey meant to cover all the work on neural MT, but it will show you how to use lamtram, and also demonstrate some things that you have to do in order to make a system that actually works well (focusing on ones that are implemented in my toolkit). This tutorial will assume that you have already installed lamtram (and the cnn backend library that it depends on) on Linux or Mac. Then, use git to pull this tutorial and the corresponding data. The data in the data/ directory is Japanese-English data that I have prepared doing some language-specific preprocessing (tokenization, lowercasing, etc.). Machine translation is a method for translating from a source sequence F with words f_1, ..., f_J to a target sequence E with words e_1, ..., e_I. This usually means that we translate between a sentence in a source language (e.g.

It's a struggle because deep learning is taught by academics, for academics. The way practitioners learn new technologies is by developing prototypes that deliver value quickly. This is a top-down approach to learning, but it is not the way that deep learning is taught. A way that works for top-down practitioners like you. You will believe that being successful with applied deep learning is possible.

After learning so much from Kaggle's collaborative community over the past eight months since I first joined, I wanted to share some of my favorite data science resources including suggestions from my fellow Kagglers. Like many others who have a seemingly endless queue of languages and techniques we hope to learn, I had tried MOOCs like Udacity and coding platforms like HackerRank. Right before joining Kaggle earlier this year, I was working through Andrew Ng's famed machine learning Coursera. Following the blogs, newsletters, and podcasts I'm sharing here is another way I try to stay (or become) savvy about topics in machine learning, data visualization, and industry trends. This list is far from exhaustive, so if you have any favs that are tragically missing, please add them to the comments!