Summary

Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you’ll get an overview of what deep learning is all about.

Partnering with Vincent Vanhoucke, Principal Scientist at Google, and technical lead in the Google Brain team, we’ll teach you how deep learning builds on machine learning. Then you’ll get a chance to learn more about deep neural networks and advanced architectures such as convolutional networks and recurrent networks.

And if you’d like to dive even deeper into this cutting-edge field, we recommend that you continue your studies with our full-fledged Deep Learning Nanodegree program to get more hands-on experience.

Expected Learning

You've heard so much about deep learning - from DeepMind's victory over the world's Go champion to skin cancer detection algorithms as good as the world's best doctors. Now get an overview of how deep learning actually works.

And once you have a better sense of the field, consider enrolling in our Deep Learning Nanodegree program to get hands-on with mastering this cutting-edge toolset.

Syllabus

Lesson 1: From Machine Learning to Deep Learning

• Understand the historical context and motivation for Deep Learning.
• Set up a basic supervised classification task and train a black box classifier on it.
• Train a logistic classifier "by hand"Optimize a logistic classifier using gradient descent, SGD, Momentum and AdaGrad.

Lesson 2: Deep Neural Networks

• Train a simple deep network.
• Effectively regularize a simple deep network.
• Train a competitive deep network via model exploration and hyperparameter tuning.

Lesson 3: Convolutional Neural Networks

• Train a simple convolutional neural net.
• Explore the design space for convolutional nets.

Lesson 4: Deep Models for Text and Sequences

• Train a text embedding model.
• Train a LSTM model.

Required Knowledge

Prior to taking this course, you should possess the following experience and skills:

• Minimum 2 years of programming experience (preferably in Python)
• Git and GitHub experience (assignment code is in a GitHub repo)
• Basic machine learning knowledge (especially supervised learning)
• Basic statistics knowledge (mean, variance, standard deviation, etc.)
• Linear algebra (vectors, matrices, etc.)
• Calculus (differentiation, integration, partial derivatives, etc.)