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.
- 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
Sequence Models for Deep Learning [6 lessons]
- 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
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