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."
Learn to use functions and apply Codes. This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning and also the basics of Machine learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand and its application . Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.
With a new $1.5 million grant, the growing field of transfer learning has come to the Ming Hsieh Department of Electrical and Computer Engineering at the USC Viterbi School of Engineering. The grant was awarded to three professors -- Salman Avestimehr, Antonio Ortega and Mahdi Soltanolkotabi -- who will work with Ilias Diakonikolas at the University of Wisconsin, Madison, to address the theoretical foundations of this field. Modern machine learning models are breaking new ground in data science, achieving unprecedented performance on tasks like classifying images in one thousand different image categories. This is achieved by training gigantic neural networks. "Neural networks work really well because they can be trained on huge amounts of pre-existing data that has previously been tagged and collected," said Avestimehr, the primary investigator of the project.