About this Course

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.

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Course Cost
Free
Timeline
Approx. 3 months
Skill Level
Udacity
Advanced
Included in Course
  • Icon cop 01 Rich Learning Content

  • Icon cop 02 Interactive Quizzes

  • Icon cop 03 Taught by Industry Pros

  • Icon cop 04 Self-Paced Learning

  • Icon cop 05 Student Support Community

Join the Path to Greatness

This free course is your first step towards a new career with the Deep Learning Nanodegree Foundation Nanodegree Program.

Free Course

Deep Learning

by Google

Enhance your skill set and boost your hirability through innovative, independent learning.

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Course Leads

  • Vincent Vanhoucke
    Vincent Vanhoucke

    Instructor

  • Arpan Chakraborty
    Arpan Chakraborty

    Course Developer

What You Will Learn

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”, and using gradient descent (and stochastic gradient descent).
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”, and using gradient descent (and stochastic gradient descent).
Lesson 2

Deep Neural Networks

  • Train a simple deep network: Relus, the chain rule, and backpropagation.
  • Effectively regularize a simple deep network. L2 regularization, and dropout.
  • Train a competitive deep network via model exploration and hyperparameter tuning.
Lesson 2

Deep Neural Networks

  • Train a simple deep network: Relus, the chain rule, and backpropagation.
  • Effectively regularize a simple deep network. L2 regularization, and dropout.
  • 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 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 using models like Word2Vec. Reduce the dimensionality of the space using tSNE.
  • Train a LSTM model, and regularize it.
Lesson 4

Deep Models for Text and Sequences

  • Train a text embedding model using models like Word2Vec. Reduce the dimensionality of the space using tSNE.
  • Train a LSTM model, and regularize it.

Prerequisites and 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.)

See the Technology Requirements for using Udacity.

Why Take This Course

Deep learning methods are becoming exponentially more important due to their demonstrated success at tackling complex learning problems. At the same time, increasing access to high-performance computing resources and state-of-the-art open-source libraries are making it more and more feasible for enterprises, small firms, and individuals to use these methods.

Mastering deep learning accordingly positions you at the very forefront of one of the most promising, innovative, and influential emergent technologies, and opens up tremendous new career opportunities. For Data Analysts, Data Scientists, Machine Learning Engineers, and students in a Machine Learning/Artificial Intelligence curriculum, this represents a rarefied opportunity to enhance your Machine Learning portfolio with an advanced, yet broadly applicable, collection of vital techniques.

What do I get?
  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals
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