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:
- 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.
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.
- 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
- Conditions and loops
- Functions in R
- Objects and Classes
- Module 4 - Deep Learning Platforms and Software Libraries
- What is a Deep Learning Platform?
- Dato GraphLab
- What is a Deep Learning Library?
- It is self-paced.
- It can be taken at any time.
Recommended skills prior to taking this course
Note: This course is an introductory course and does not have any hands-on lab. After completing this course, it is recommended to take "Deep learning with TensorFlow" course.
Note: Though this course does not have any hands-on lab, still you can try PowerAI to understand different deep learning libraries better. PowerAI speeds up deep learning and AI. Built on IBM's Power Systems, PowerAI is a scalable software platform that accelerates deep learning and AI with blazing performance for individual users or enterprises. The PowerAI platform supports popular machine learning libraries and dependencies including Tensorflow, Caffe, Torch, and Theano. You can download a free version of PowerAI here.
AT A GLANCE
- Starting from $49 USD