Deep Learning Fundamentals

Get a crash course on the what there is to learn and how to go about learning more. Deep Learning presents a simplified explanation of some of the hottest topics in data science today:

• What is Deep Learning?
• What convolutional neural networks?
• Why is deep learning so powerful and what can it be used for?
• Be part of a rapidly growing field in data science; there's no better time than now to get started with neural networks.

COURSE SYLLABUS

• Module 1 - Introduction to Deep Learning
  1. Why Deep Learning?
  2. What is a neural network?
  3. Three reasons to go Deep
  4. Your choice of Deep Net
  5. An old problem: The Vanishing Gradient

• Module 2 - Deep Learning Models
  1. Restricted Boltzmann Machines
  2. Deep Belief Nets
  3. Convolutional Networks
  4. Recurrent Nets

• Module 3 - Additional Deep Learning Models
  1. Autoencoders
  2. Recursive Neural Tensor Nets
  3. Deep Learning Use Cases

• Module 4 - Deep Learning Platforms and Software Libraries
  1. What is a Deep Learning Platform?
  2. H2O.ai
  3. Dato GraphLab
  4. What is a Deep Learning Library?
  5. Theano
  6. Caffe
  7. TensorFlow

GENERAL INFORMATION

• This course is free.
• It is self-paced.
• It can be taken at any time.
• It can be audited as many times as you wish.

RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE

• None

REQUIREMENTS