Google and Udacity launch free course to help you master machine learning


Google and online learning hub Udacity have launched a free course designed to make it simpler for software developers to grasp the fundamentals of machine learning. The "Intro to TensorFlow for Deep Learning" course is designed to be more accessible to developers than previous machine-learning courses offered by Udacity. "Our goal is to get you building state-of-the-art AI applications as fast as possible, without requiring a background in math," says Mat Leonard, head of the School of AI at Udacity. "If you can code, you can build AI with TensorFlow. You'll get hands-on experience using TensorFlow to implement state-of-the-art image classifiers and other deep learning models. You'll also learn how to deploy your models to various environments including browsers, phones, and the cloud."

First Steps of Learning Deep Learning: Image Classification in Keras


In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees. Do I need some Skynet to run it?

Free Resources for Beginners on Deep Learning and Neural Network


Machines have already started their march towards artificial intelligence. Deep Learning and Neural Networks are probably the hottest topics in machine learning research today. Companies like Google, Facebook and Baidu are heavily investing into this field of research. Researchers believe that machine learning will highly influence human life in near future. Human tasks will be automated using robots with negligible margin of error. I'm sure many of us would never have imagined such gigantic power of machine learning.

An AI Resident at work: Suhani Vora and her work on genomics


Suhani: During graduate school, I worked on engineering CRISPR/Cas9 systems, which enable a wide range of research on genomes. And though I was working with the most efficient tools available for genome editing, I knew we could make progress even faster.

To cripple AI, hackers are turning data against itself


A neural network looks at a picture of a turtle and sees a rifle. A self-driving car blows past a stop sign because a carefully crafted sticker bamboozled its computer vision. An eyeglass frame confuse facial recognition tech into thinking a random dude is actress Milla Jovovich. The hacking of artificial intelligence is an emerging security crisis. Pre-empting criminals attempting to hijack artificial intelligence by tampering with datasets or the physical environment, researchers have turned to adversarial machine learning.