Any time, Self-paced
Tell Your Friends
About This Course
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:
Please note that version 2.0 of this course was released on August 23, 2017. Please refer to the Change Log section in the course for a detailed description of the changes and updates.
- What is Deep Learning?
- What are are 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.
- Module 1 - Introduction to Deep Learning
- Why Deep Learning?
- What is a neural network?
- Three reasons to go Deep
- Your choice of Deep Net
- An old problem: The Vanishing Gradient
- Module 2 - Deep Learning Models
- Restricted Boltzmann Machines
- Deep Belief Nets
- Convolutional Networks
- Recurrent Nets
- Module 3 - Additional Deep Learning Models
- Recursive Neural Tensor Nets
- Deep Learning Use Cases
- Module 4 - Deep Learning Platforms and Software Libraries
- What is a Deep Learning Platform?
- Dato GraphLab
- What is a Deep Learning Library?
- 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