Otavio

“The learning paths on Dataquest are incredible. They give you a direction through the learning process – you don’t have to guess what to learn next.”

Otávio Silveira

Data Analyst @ Hortifruti

Course overview

First, you’ll explore the concepts and terms necessary for working with sequential models in TensorFlow. You’ll discover recurrent neural networks (RNN) and how they compare with convolutional neural networks (CNNs), as well as some of the most common RNN applications.

Then, you’ll learn how to build, train, evaluate, and improve a basic RNN to predict song popularity using regression. You’ll also learn to use Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) techniques to improve model performance. You’ll implement these to predict the sentiment of the review (good or bad) on a dataset of IMDB reviews.

Next, you’ll combine convolutional neural networks with sequential models to add a convolutional layer to your LSTM model and compare its predictive performance before and after on a dataset of IMDB reviews, predicting sentiment.

Finally, you’ll optimize the tools already used for time-series forecasting on a dataset of movie ticket sales to prepare you for the guided project.

Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll apply your new skills to a project to build a model to better forecast how the S&P 500 futures index will move based on its behavior over the past several years.

Key skills

  • Describing sequential neural network models
  • Determining when to use RNN, GRU, and LSTM
  • Implement a sequential model using a basic RNN
  • Implementing a time series forecast model using LSTM and GRU

Course outline

Sequence Models for Deep Learning [6 lessons]

Introduction to RNNs 2h

Lesson Objectives
  • Explain the basics and the different components of recurrent neural networks (RNN)
  • Summarize the applications of RNN
  • Compare and contrast RNN with CNN

Basic RNN Architecture 2h

Lesson Objectives
  • Build and train a basic RNN model for a regression problem
  • Evaluate model performance on a test set using mean squared error
  • Optimize a model by experimenting with parameters

Advanced RNN Architecture -- GRU and LSTM 2h

Lesson Objectives
  • Explain the use cases for LSTM and GRU
  • Differentiate between LSTM and GRU
  • Build a basic LSTM model
  • Compare the performance of an LSTM model with an RNN model

Advanced RNN Architecture -- Convolutional Layers 2h

Lesson Objectives
  • Describe the use case for combining convolutional layers with an RNN
  • Add a convolutional layer to an LSTM model
  • Compare the performance of different sequential models

Time Series Forecasting with RNNs 2h

Lesson Objectives
  • Describe why RNNs are effective for time series forecasting
  • Forecast time series data using a basic RNN model
  • Forecast time series data using an LSTM model
  • Evaluate the performance of a time series forecast

Guided Project: Time-Series Forecasting on the S&P 500 2h

Lesson Objectives
  • Work with a real-world dataset for the S&P 500 index
  • Build an LSTM model with a convolutional layer
  • Train and evaluate the model for stock price prediction

Projects in this course

Guided Project: Time-Series Forecasting on the S&P 500

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.

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Aaron

Aaron Melton

Business Analyst at Aditi Consulting

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