Recurrent Neural Networks

بواسطة: 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

Recurrent Neural Networks
الذهاب الي الدورة

Recurrent Neural Networks

بواسطة: LinkedIn Learning

  • LinkedIn Learning
  • مدفوعة
  • الإنجليزية
  • متاح شهادة
  • متاح في أي وقت
  • الجميع
  • N/A