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Udacity Presents Siraj Raval’s Deep Learning Nanodegree Foundation Program

Link to Content:
Deep Learning Nanodegree

Created/Published/Taught by:
Siraj Raval
Mat Leonard
Brok Bucholtz

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Free? No

Cost: $0.00

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Difficulty Rating:
Early Beginner 0
Advanced Beginner 1
Intermediate 0
Advanced Intermediate 0
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From Udacity:

“Build your Deep Learning Foundations, and earn your Udacity credential!

Artificial Intelligence is transforming our world in dramatic and beneficial ways, and Deep Learning is powering the progress. Together with Siraj Raval, Udacity provides a dynamic introduction to this amazing field, using weekly videos, exclusive projects, and expert feedback and review to teach you the foundations of this future-shaping technology.”

Siraj’s bio: “I’m a Data Scientist, bestselling author, and YouTube star. I make videos that teach people how to use machine learning to create game bots, chatbots, self-driving cars, programs that create art and music, stock prediction models, and much more. I’m proud to be an exclusive Udacity partner, and excited to be your host for this amazing program.”

Syllabus by week:

  1. Introduction to Deep Learning
  2. Model Evaluation and Validation
  3. Graph computations
  4. Intro to TensorFlow
  5. Deep Neural Networks
  6. Convolutional Networks
  7. Recurrent Neural Networks
  8. Word Embeddings
  9. Using TensorBand
  10. Text Generation
  11. Sequence to Sequence Generation
  12. Transfer Learning
  13. Reinforcement Learning
  14. Autoencoders
  15. Generative Adversarial Networks (GAN)
  16. Image Generation
  17. One-shot Learning

Recommended Prerequisites: This program has been created specifically for students who are interested in machine learning, AI, and/or deep learning. Students who are interested in enrolling must have intermediate Python programming knowledge, experience with Numpy, experience using Anaconda and Jupyter Notebooks. Additionally, students must have the necessary math knowledge including: algebra and some calculus – specifically partial derivatives, and matrix multiplication (linear algebra) to be successful.

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