Data Science News Digest – handpicked articles, news, and stories from Data Science world.
- CUDA 9 Features Revealed – At the GPU Technology Conference, NVIDIA announced CUDA 9, the latest version of CUDA’s powerful parallel computing platform and programming model.
- Explaining How End-to-End Deep LearninSteers a Self-Driving Car – As part of a complete software stack for autonomous driving, NVIDIA has created a deep-learning-based system, known as PilotNet, which learns to emulate the behavior of human drivers and can be deployed as a self-driving car controller.
- Microsoft Build 2017: Microsoft AI – Amplify human ingenuity – Thanks to the convergence of three major forces — increased computing power in the cloud, powerful algorithms that run on deep neural networks and access to massive amounts of data — we’re finally able to realize the dream of AI.
- AlphaGo’s next move – Chinese Go Grandmaster and world number one Ke Jie departed from his typical style of play and opened with a “3:3 point” strategy – a highly unusual approach aimed at quickly claiming corner territory at the start of the game.
- Integrate Your Amazon Lex Bot with Any Messaging Service – Is your Amazon Lex chatbot ready to talk to the world? When it is, chances are that you’ll want it to be able to interact with as many users as possible. Amazon Lex offers built-in integration with Facebook, Slack and Twilio. But what if you want to connect to a messaging service that isn’t supported? Well, there’s an API for that–the Amazon Lex API.
- How Our Company Learned to Make Better Predictions About Everything – In Silicon Valley, everyone makes bets. Founders bet years of their lives on finding product-market fit, investors bet billions on the future value of ambitious startups, and executives bet that their strategies will increase a company’s prospects. Here, predicting the future is not a theoretical superpower, it’s part of the job.
- Are Pop Lyrics Getting More Repetitive? – In 1977, the great computer scientist Donald Knuth published a paper called The Complexity of Songs, which is basically one long joke about the repetitive lyrics of newfangled music (example quote: “the advent of modern drugs has led to demands for still less memory, and the ultimate improvement of Theorem 1 has consequently just been announced”).
- Home advantages and wanderlust – When Burnley got beat 3-1 by Everton at Goodison Park on the 15th April, 33 games into their Premier League season, they’d gained only 4 points out of a possible 51 in their away fixtures. But during this time they’d also managed to accrue 32 points out of a possible 48 at Turf Moor; if the league table were based upon only home fixtures, they’d be in a highly impressive 6th place.
- Facebook Wants to Merge AI Systems for a Smarter Chatbot – It’s possible to have a computer use conversational tricks that provide the appearance of intelligence for a few minutes, but the complexity of language eventually destroys the illusion.
- MAGIC AI: THESE ARE THE OPTICAL ILLUSIONS THAT TRICK, FOOL, AND FLUMMOX COMPUTERS – To a human, a fooling image might look like a random tie-dye pattern or a burst of TV static, but show it to an AI image classifier and it’ll say with confidence: “Yep, that’s a gibbon,” or “My, what a shiny red motorbike.”
- The Simple, Economic Value of Artificial Intelligence – How does this framing now apply to our emerging AI revolution? After decades of promise and hype, AI seems to have finally arrived, – driven by the explosive growth of big data, inexpensive computing power and storage, and advanced algorithms like machine learning that enable us to analyze and extract insights from all that data.
Python for Data Science by UC San DiegoX – Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets.
High-Dimensional Data Analysis by HarvardX – A focus on several techniques that are widely used in the analysis of high-dimensional data.
A developer’s guide to the Internet of Things (IoT) – The Internet of Things (IoT) is an area of rapid growth and opportunity. Technical innovations in networks, sensors and applications, coupled with the advent of ‘smart machines’ have resulted in a huge diversity of devices generating all kinds of structured and unstructured data that needs to be processed somewhere.
Neural Networks for Machine Learning – Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We’ll emphasize both the basic algorithms and the practical tricks needed to get them to work well.
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