Course Description

  • What are the Key Learning Objectives of this course?

    Upon completion of this course, you will be able to:

    • Gain an understanding of Deep Learning
    • Understand Deep Learning models such as convolutional networks,
    • recurrent nets, Autoencoders, Recursive Neural Tensor Nets, and Deep
    • Learning Use Cases
    • Comprehend Deep Learning platforms and software libraries
       

Course Preview

    • Lesson 1 - Learning Objectives

      • Learning Objectives
    • Lesson 2 - Introduction to Deep Learning

      22:31
      • Learning Objectives
      • 1.1 Deep Learning: The Series Introduction
        03:46
      • 1.2 What is a Neural Network
        06:28
      • 1.3 Three Reasons to go Deep
        03:56
      • 1.4 Your choice of Deep Net
        02:58
      • 1.5 An Old Problem
        05:23
    • Lesson 3 - Deep Learning Models

      22:56
      • Learning Objectives
      • 2.1 Restricted Boltzmann Machines
        04:50
      • 2.2 Deep Belief Nets
        04:31
      • 2.3 Convolutional Nets
        08:15
      • 2.4 Recurrent Nets
        05:20
    • Lesson 4 - Additional Deep Learning Models

      19:13
      • Learning Objectives
      • 3.1 Autoencoders
        03:51
      • 3.2 Recursive Neural Tensor Nets
        05:49
      • 3.3 Use Cases
        09:33
    • Lesson 5 - Deep Learning Platforms & Libraries

      25:37
      • Learning Objectives
      • 4.1 What is a Deep Net Platform?
        03:41
      • 4.2 H2O.ai
        03:42
      • 4.3 Dato GraphLab
        03:32
      • 4.4 What is a Deep Learning Library?
        01:58
      • 4.5 Theano
        03:21
      • 4.6 Caffe
        02:48
      • 4.7 Tensorflow
        06:35
      • Unlocking IBM Certificate
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    • Disclaimer
    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.