Deep Learning

by

Google
Free Course

Deep Learning

Brief overview of content only without mentorship, project reviews, certification

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About the course

Deep Learning

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 develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets.

We’ll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

We have developed this course with Vincent Vanhoucke, Principal Scientist at Google, and technical lead in the Google Brain team.

**Note: This is an intermediate to advanced level course offered as part of the Machine Learning Engineer Nanodegree program. It assumes you have taken a first course in machine learning, and that you are at least familiar with supervised learning methods.

Course Leads
Vincent Vanhoucke

Vincent Vanhoucke

Instructor

Arpan Chakraborty

Arpan Chakraborty

Course Developer

Course Cost
Free
Timeline
Approx. 3 months
Skill Level
intermediate
Pre-Requisites & Requirements

This is an intermediate to advanced level course. Prior to taking this course, and in addition to the prerequisites and requirements outlined for the Machine Learning Engineer Nanodegree program, 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.)

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