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The promise of AI in audio processing – Towards Data Science
We have seen a rise of AI technologies for image and video processing. Even though things tend to take a little while longer making it to the world of audio, here we have also seen impressive technological advances. In this article, I will summarize some of these advances, outline further potentials of AI in audio processing as well as describe some of the possible pitfalls and challenges we might encounter in pursuing this cause. The kicker for my interest in AI use cases for audio processing was the publication of Google Deepmind's "WaveNet" -- A deep learning model for generating audio recordings [1] which was released during the end of 2016. Using an adapted network architecture, a dilated convolutional neural network, Deepmind researchers succeeded in generating very convincing text-to-speech and some interesting music-like recordings trained from classical piano recordings.
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There are more than 8,000 online courses out there. These are some of the best. More than 50 million students signed up for one this year. When scientists announce they've made a breakthrough, they usually promise we'll see the full effects of those discoveries--anything from a better understanding of how the universe works to a drug ready for use in patients--in about five years. TULSA -- Tom Coomer has retired twice: once when he was 65, and then several years ago.
WeDoSky, Drone Data Science, solutions raises funds from Mumbai Angels Network
New Delhi [India], Dec 14 (ANI): Higher in resolution than Google Earth/Maps and updated as often as daily, WeDoSky's Drone Data Science solutions help answer the'What' and the'Where' for a geographical area that needs to be monitored. WeDoSky is a data science company focused on generating actionable business insights using drone imagery, to assist various industries like mining, construction, energy and solar. The data extraction and analysis is done using their proprietary software'visualAI' . This is also offered as a standalone SaaS platform for Drone-enabled businesses across the globe. WeDoSky counts titans like Adani Enterprises, Tata Power, Azure Power, and ACC Cement amongst its many clients.
The top business news stories of 2017
The Japan Times newsroom selected these economy and technology stories as the most important of 2017. But the struggling Japanese conglomerate later agreed on the ¥2 trillion sale of its chip unit to a Japan-U.S.-South Korea consortium. It also ended a months-long legal battle with its partner in the venture, Western Digital. Earlier this month, Japan agreed with the EU on a giant free trade deal, eliminating tariffs on 94 percent of imports from the EU. The bloc will eliminate tariffs on 99 percent of its imports from Japan.
Adam - momentum y (aka. cost) terms. • r/MachineLearning
It was the Newton-Raphson method for finding roots of an equation. I thought this method mostly applies for minimization in machine learning as cost is always defined as a positive real valued function. But it was pointed out to me that the update equation of newton-raphson method, which is x x - y / dy_dx, is unstable at local minimas (where dy_dx 0) since it makes the update burst to infinity. Eventually, I landed on this update equation, x x - ((y * dy_dx) / (y dy_dx2)); dy_dx derivative of y wrt. To relate this update equation with the title: if we consider the update portion of the equation - g(x, y) (y * x) / (y x2); y 0 It is quite similar to adam since there is a square gradient term in the denominator and the gradient term in the numerator.