[UDACITY] Deep Learning Nanodegree Program


[UDACITY] Deep Learning Nanodegree Program


Deep learning is driving advances in artificial intelligence that are changing our world. Enroll now to build and apply your own deep neural networks to produce amazing solutions to important challenges.

Course Syllabus


• Get your first taste of deep learning by applying style transfer to your own images, and gain experience using development tools such as Anaconda and Jupyter notebooks.

Neural Networks

• Learn neural networks basics, and build your first network with Python and NumPy. Use the modern deep learning framework PyTorch to build multi-layer neural networks, and analyze real data.

Convolutional Neural Networks

• Learn how to build convolutional networks and use them to classify images (faces, melanomas, etc.) based on patterns and objects that appear in them. Use these networks to learn data compression and image denoising.

Recurrent Neural Networks

• Build your own recurrent networks and long short-term memory networks with PyTorch; perform sentiment analysis and use recurrent networks to generate new text from TV scripts.

Generative Adversarial Networks

• Learn to understand and implement the DCGAN model to simulate realistic images, with Ian Goodfellow, the inventor of GANS (generative adversarial networks).

Deploying a Sentiment Analysis Model

• Use deep neural networks to design agents that can learn to take actions in a simulated environment. Apply reinforcement learning to complex control tasks like video games and robotics.

Project 1

• Predicting Bike-Sharing Patterns

Project 2

• Dog-Breed Classifier

Project 3

• Generate TV scripts

Project 4

• Generate Faces

Project 5

• Deploying a Sentiment Analysis Model.

Why Take This Nanodegree Program?

In this program, you’ll cover Convolutional and Recurrent Neural Networks, Generative Adversarial Networks, Deployment, and more. You’ll use PyTorch, and have access to GPUs to train models faster. You’ll learn from authorities like Sebastian Thrun, Ian Goodfellow, Jun-Yan Zhu, and Andrew Trask. This is the ideal point-of-entry into the field of AI.

Size: 3.33 GB

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