Sequence Models for Deep Learning

What's inside

5 lessons · 1 project

• 01
  Introduction to RNNs

  <p>Explore the fundamentals of Recurrent Neural Networks, examine their applications, and compare them to Convolutional Neural Networks, highlighting key differences and use cases.</p>

  51 min
• 02
  Basic RNN Architecture

  <p>Build and train a simple RNN for regression, evaluate performance using mean squared error, and optimize the model to improve its accuracy.</p>

  120 min
• 03
  Advanced RNN Architecture -- GRU and LSTM

  <p>Explore LSTM and GRU use cases, learn their differences, build a basic LSTM model, and compare its performance to a traditional RNN.</p>

  120 min
• 04
  Advanced RNN Architecture -- Convolutional Layers

  <p>Explore advanced RNN architectures by learning to combine convolutional layers with RNNs, integrating convolutional layers in LSTM models, and comparing the performance of various sequential models.</p>

  120 min
• 05
  Time Series Forecasting with RNNs

  <p>Master time series forecasting using RNNs by understanding their effectiveness, learning to forecast with basic RNN and LSTM models, and evaluating forecast performance for accurate predictions.</p>

  120 min
• 06
  Guided Project: Time-Series Forecasting on the S&P 500 Project

  For this project, you'll take on the role of a trader on the S&P 500 futures desk aiming to build a model to forecast the index's movement, enabling lucrative trading opportunities.

  120 min

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