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Convolutional and Recurrent Neural Networks


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
Some tasks aren't suitable for traditional neural networks and require specialized neural networks. In this course you will learn about convolutional and recurrent neural networks and the types of problems they can solve.

Target Audience
Anyone interested in understanding machine learning and using it to solve problems

Prerequisites
None

Expected Duration (hours)
0.7

Lesson Objectives

Convolutional and Recurrent Neural Networks

  • start the course
  • describe convolutional neural networks, how they are different from regular neural networks, and how they are used
  • describe the high level architecture of convolutional neural networks
  • describe how convolution layers are set in convolutional neural networks
  • describe how pooling layers work in convolutional neural networks
  • describe some training considerations for convolutional neural networks and how training can differ from traditional neural networks
  • describe regularization and how it applies to convolutional neural networks
  • implement and train a convolutional neural network in TensorFlow
  • perform regularizing to a convolutional neural network in TensorFlow
  • describe recurrent neural networks, how they are different from regular neural networks, and how they are used
  • describe the architecture of a recurrent neural network
  • implement an LSTM network in TensorFlow
  • use RNNs to perform time-series analysis in TensorFlow
  • use TensorFlow to create a CNN that classifies images
  • Course Number:
    sd_exml_a05_it_enus

    Expertise Level
    Intermediate