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
- Implementing a time series forecast model using a basic RNN
- Implementing a time series forecast model using LSTM and GRU
Course outline
Introduction to Deep Learning in TensorFlow [6 lessons]
Deep Learning Fundamentals 2h
Lesson Objectives- Identify the differences between a shallow and multi-layer (dense) neural network
- Implement forward propagation in Python
- Identify different types of activation functions in Python
- Implement an activation in Python
Introduction to TensorFlow Operations 2h
Lesson Objectives- Create tensors using TensorFlow
- Define data as constants and variables in TensorFlow
- Convert Tensors to NumPy arrays and vice versa
- Perform mathematical operations on tensors
Shallow Neural Network with the Sequential API 2h
Lesson Objectives- Perform exploratory analysis of the data
- Prepare data for machine learning
- Build and train a shallow neural network regression model
- Evaluate and make predictions on test data
- Visualize model results
Multi-layer (dense) Deep Learning Model 2h
Lesson Objectives- Build a multi-layer deep learning model using Sequential API
- Evaluate and make predictions on test data
- Visualize model results
- Compare the performance of a model using different activation functions
Deep Learning with the Functional API in Keras 2h
Lesson Objectives- Compare and contrast the Sequential and Functional API for model building
- Explore and prepare a dataset for classification modeling
- Visualize the main features of a dataset
- Build and summarize a binary classification model using Functional API
- Evaluate the performance of the classification model
Guided Project: Predicting Listing Gains in the Indian IPO Market Using TensorFlow 2h
Lesson Objectives- Perform exploratory analysis, visualization, and preprocessing on the dataset
- Build a hold-out validation approach to model evaluation
- Build and train the multi-layer classification deep learning model using the Sequential API in TensorFlow
Projects in this course
Guided Project: Predicting Listing Gains in the Indian IPO Market Using TensorFlow
Build a deep learning model to predict the listing gains of IPOs in the Indian market.
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We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.
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Build confidence with our in-depth projects, and show off your data skills.
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