Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

LinkedIn Learning

Recurrent Neural Networks

via LinkedIn Learning

Overview

Learn the basics of recurrent neural networks to get up and running with RNN quickly.

Syllabus

Introduction
  • Getting started with RNNs
  • Scope and prerequisites for the course
  • Setting up exercise files
1. Introduction to RNNs
  • A review of deep learning
  • Why sequence models?
  • A recurrent neural network
  • Types of RNNs
  • Applications of RNNs
2. RNN Concepts
  • Training RNN models
  • Forward propagation with RNN
  • Computing RNN loss
  • Backward propagation with RNN
  • Predictions with RNN
3. An RNN Example
  • A simple RNN example: Predicting stock prices
  • Data preprocessing for RNN
  • Preparing time series data with lookback
  • Creating an RNN model
  • Testing and predictions with RNN
4. RNN Architectures
  • The vanishing gradient problem
  • The gated recurrent unit
  • Long short-term memory
  • Bidirectional RNNs
5. An LSTM Example
  • Forecasting service loads with LSTM
  • Time series patterns
  • Preparing time series data for LSTM
  • Creating an LSTM model
  • Testing the LSTM model
  • Forecasting service loads: Predictions
6. Word Embeddings
  • Text based models: Challenges
  • Intro to word embeddings
  • Pretrained word embeddings
  • Text preprocessing for RNN
  • Creating an embedding matrix
7. Spam Detection with Word Embeddings
  • Spam detection example for embeddings
  • Preparing spam data for training
  • Building the embedding matrix
  • Creating a spam classification model
  • Predicting spam with LSTM and word embeddings
Conclusion
  • Next steps

Taught by

Kumaran Ponnambalam

Reviews

4.7 rating at LinkedIn Learning based on 143 ratings

Start your review of Recurrent Neural Networks

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.