Deep Learning Nanodegree is Now Live!
We are proud to announce that yesterday we launched the refresh of our Deep Learning Nanodegree Program as the latest addition to our School of Artificial Intelligence. It is now available to students in the classroom!
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Introduction to Deep Learning
Begin by learning the fundamentals of deep learning. Then examine the foundational algorithms underpinning modern deep learning: gradient descent and backpropagation. Once those foundations are established, explore design constructs of neural networks and the impact of these design decisions. Finally, the course explores how neural network training can be optimized for accuracy and robustness.
- Developing a Handwritten Digits Classifier with PyTorch
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Convolutional Neural Networks
This course introduces convolutional neural networks, the most widely used type of neural networks specialized in image processing. You will learn the main characteristics of CNNs that make them better than standard neural networks for image processing. Then you’ll examine the inner workings of CNNs and apply the architectures to custom datasets using transfer learning. Finally, you will learn how to use CNNs for object detection and semantic segmentation.
- Landmark Classification and Tagging for Social Media
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RNNs & Transformers
This course covers multiple RNN architectures and discusses design patterns for those models. Additionally, you’ll focus on the latest transformer architectures.
- LSTM Seq2Seq Chatbot
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Building Generative Adversarial Networks
Become familiar with generative adversarial networks (GANs) by learning how to build and train different GANs architectures to generate new images. Discover, build, and train architectures such as DCGAN, CycleGAN, ProGAN, and StyleGAN on diverse datasets including the MNIST dataset, Summer2Winter Yosemite dataset, or CelebA dataset.
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If a student with a current static access to the previous version of the deep learning nanodegree joins this updated version, does the previous version of the course get replaced by this version "in other words the student will lose access to the previous version" ?
And assuming the answer to the previous question is yes, I guess then the only way to have access to both versions is by creating a new Udacity account for this updated version, but the question is, does Udacity allow students to have multiple accounts ?
Thanks and looking forward to your reply
Mina
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This is a great question Mina Naguib Zekry Garss. I am going to forward this question to one of our specialists to make sure we get you the most accurate information.
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Chris this seems to be impossible to do. I tried enrolling, but it just takes me to the prior iteration with all my completed modules. I don't see any new content. Of course, it'd be great if we could get the refreshed content, as we've already enrolled and completed the course before :)
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Hey, Ashic Mahtab
Students who enroll and graduate from a subscription-based Nanodegree program, single paid course, or Executive Program will have indefinite static access to their programs post-graduation.
Static access will include classroom content that will not be updated over time and read-only access to Knowledge Q&A. Such access will not include access to unsubmitted projects, as well as certain services, such as community channels, project reviews, workspaces, labs, or quizzes.
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Chris thanks for the response. The issue I have is that I tried to enrol into the "new" deep learning nano-degree during the massive discount to access the new content, however when I clicked "Enroll", it just takes me back to the course I've already completed. It seems I can't even enrol in the new course as I graduated from the old.
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I see. Thanks for that info Ashic Mahtab. Let me send your issue to one of the support agents so we can solve this for you! : )
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