Facebook Scholarship Fuels a Passion for Deep Learning

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Last week we announced our latest collaboration with Facebook: Secure and Private AI Scholarship Challenge. This program builds on the previous momentum, aimed at a further expansion of student access to deep learning and AI tools. Just seven months ago we launched the PyTorch Scholarship Challenge from Facebook which saw 18,000 applications, 10,000 challenge scholars, and 300 full scholarship students from 149 countries starting the Deep Learning Nanodegree program in January.



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Announcing the Secure and Private AI Scholarship Challenge with Facebook

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Today, we are pleased to announce our newest offering to expand students’ deep learning and AI skills: the Secure and Private AI Challenge Scholarship from Facebook.

This new scholarship program, announced at F8, the Facebook Developer Conference in San Jose, will enable students to acquire skills in Federated Learning, Differential Privacy, and Encrypted Computation with the benefit of robust community support from Udacity. You will learn how to use the newest privacy-preserving technologies, such as OpenMined’s PySyft. PySyft extends PyTorch and other deep learning tools with the cryptographic and distributed technologies necessary to safely and securely train AI models on distributed private data while maintaining users’ privacy. Students will also have the opportunity to earn their way to a full scholarship to either the Deep Learning Nanodegree program or the Computer Vision Nanodegree program with Udacity.

The Secure and Private AI Scholarship Challenge from Facebook



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Bertelsmann Announces 50,000 New Udacity Scholarships in Areas of Cloud, Data and AI

UPDATE: The Bertelsmann Scholarship Program is now open.

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Today, we are excited to announce a new scholarship program with Bertelsmann. Over the next three years, Bertelsmann and Udacity will provide up to 50,000 scholarships in the areas of Cloud Engineering, Data Science and Artificial intelligence. This effort is an expansion of Udacity and Bertelmann’s partnership, as well as, their joint efforts to provide enhanced learning opportunities in emerging technologies.

The program is structured in two phases: In the first phase, 15,000 applicants, per subject area, will be selected to participate in a 3-month Scholarship Challenge phase. In the second phase, the top 5,000 performing Challenge phase students in each subject area will be awarded a full scholarship for a  Udacity Nanodegree program.



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Machine Learning Improves your Shopping Experience

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Machine learning is impacting countless industries, from the recent discovery of a black hole to improving healthcare, we are just scratching the surface. The retail industry is a prime example. Retailers and manufacturers are racing to figure out how they can employ machine learning to target specific consumers, monitor trends, and discover new pricing models.

While retailers and manufacturers are doubling down on new ways to target and sell to consumers, Jia Rui Ong, a two-time Nanodegree program graduate, and his team are employing machine learning to help you, the consumer, find the best price for the clothing you desire.

We recently had a chance to sit down with Jia Rui Ong and his team at Yux to discuss their product, as well as, our newly updated Machine Learning Nanodegree program.



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Amazon Web Services and Udacity collaborate to offer Machine Learning Nanodegree with Amazon SageMaker

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We’re working with Amazon Web Services (AWS) and their AWS Educate program to teach you how to deploy machine learning models using Amazon SageMaker.  

Over the past few years, the demand for machine learning specialists and engineers has soared, with machine learning engineers and specialists ranking amongst the top emerging jobs on LinkedIn. Recently, machine learning has been adopted by a wide range of industries, including medical diagnostic companies, finance firms, and more. Udacity’s Intro to Machine Learning Nanodegree program and Machine Learning Engineer Nanodegree program were built in response to this demand to provide access to this growing tech field.

We’ve seen advances in research and industry practices as more companies look to build machine learning products. Specifically, there is a growing demand for engineers who are able to deploy machine learning models to a global audience. Deployment means making a model available for use in a piece of hardware or web application, such as a voice assistant or recommendation engine. Knowing how to build machine learning models is a great starting point, but to truly make an impact at scale, a data scientist or programmer needs to know the techniques and tools to deploy that model so that it’s highly accessible.

To keep up with this advancement and bring the best educational experience to our students, we are updating the Machine Learning Engineer Nanodegree program to include the latest skills by adding two new projects focused on deployment skills.



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Meet the Three-Time Nanodegree Graduate Using Deep Learning to Explore an Ancient Turkish Art

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In a continuation of celebrating this year’s Women’s History Month, we would like to introduce Ayşin Taşdelen, an artificial intelligence professional and three-time Nanodegree program graduate. She has let her curiosity and desire for new skills lead her through three Nanodegree programs, new jobs, and side projects.

Women's History Artificial Intelligence Ayşin Taşdelen Udacity quote

We recently had a chance to speak with Ayşin to hear about her motivations and interest in pursuing cutting-edge technologies.

You studied mathematics during your university years and then became a programmer, what were some of your initial career goals?

I really enjoyed my university studies, so much so, that I initially looked into becoming a full-time researcher. Leaving academia was a tough decision, I loved learning but also knew that starting a traditional career would help me financially. I decided to go the career route and follow my interest in computer science. My initial career goal was to land a job and improve my programming skills.

As your career has developed, how have you satiated your desire to learn?

Over the years, I have tried to keep up with industry articles and books about the latest computer and tech trends. As the internet surged, I started using online library subscriptions and video learning paths. Reading and watching videos were great, but they only get you so far; I never felt like I was learning enough about a subject or concept, until, Udacity.

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Practical Machine Learning with TensorFlow 2.0 Alpha

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In 2016, Udacity released the very first free course on TensorFlow in collaboration with Google. Since then, over 400,000 students have enrolled in the course and joined the AI revolution. We’re excited to release an all-new version of this free course featuring the just-announced alpha release of TensorFlow 2.0: Intro to TensorFlow for Deep Learning. This update makes AI even more accessible to everyone, and we’ve again worked directly with the deep learning experts at Google to ensure you’re learning the very latest skills to utilize TensorFlow.

Google and Udacity Intro to TensorFlow for Deep Learning course

This free course is a practical approach to deep learning for software developers. Our goal is to get you building state-of-the-art AI applications as fast as possible, without requiring a background in math. 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.

Machine Learning for Everyone

The alpha release of TensorFlow 2.0 is a big milestone for the product. TensorFlow has matured into an entire end-to-end platform. In this alpha release, TensorFlow has been redesigned with a focus on simplicity, developer productivity, and ease of use. This release integrates Keras more tightly into the rest of the TensorFlow platform so that it’s easier for developers new to machine learning to get started with TensorFlow. Along with standardizing around Keras as the main API, other deprecated and redundant APIs have been removed to reduce complexity in the framework. A general release candidate will be available later in Q2 2019.

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