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Build & Train RNNs: Implementing Recurrent Neural Networks

Build & Train RNNs: Implementing Recurrent Neural Networks


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Examine the concepts of perception, perception layers, and backpropagation and discover how to implement recurrent neural network using Python, TensorFlow, and Caffe2.



Expected Duration (hours)
0.8

Lesson Objectives

Build & Train RNNs: Implementing Recurrent Neural Networks

  • identify the essential features and processes of implementing perception and backpropagation
  • compare single and multilayer perception and describe the need for layer management
  • describe the steps involved in building recurrent neural network models
  • implement recurrent neural network using Python and TensorFlow
  • implement long short-term memory using TensorFlow
  • recognize the capabilities provided by Caffe2 for implementing neural networks
  • implement recurrent neural network using Caffe2
  • build deep learning language models using Keras
  • implement RNN using TensorFlow and Caffe2 and build deep learning language models using Keras
  • Course Number:
    it_mlbtrndj_02_enus

    Expertise Level
    Intermediate