06-02, 11:00–12:30 (Europe/London), Warwick
This 90-minute tutorial provides an introduction to using TensorFlow for building random forest models. The tutorial will begin with an overview of the random forest algorithm and its advantages in the context of machine learning. Next, participants will learn how to implement a random forest model using TensorFlow's high-level API, Keras. The tutorial will cover important concepts such as model architecture, hyperparameter tuning, and training and evaluation techniques. Additionally, participants will learn how to use TensorFlow's TensorBoard to visualize and monitor their models during training. The tutorial will conclude with a discussion of best practices and tips for improving the performance of random forest models. By the end of the tutorial, participants will have gained a solid understanding of how to use TensorFlow to build powerful and accurate random forest models.
This 90 minute tutorial is designed to introduce people to using TensorFlow through a very accessible - and potentially familar model - Random Forest. This allows introduction to some of the key concepts of Tensorflow without also having to learn more advanced areas.
Requirements
- Attendees should be familar with base Python & working with Tabular data in Python (Pandas)
- Attendees should have TensorFlow installed or use Google Collab (Links and material will be provided)
- Material will be delivered via a github repo (Attendees should be familar with getting files from a repo)
Previous knowledge expected
Lisa is the lead data science instructor at Digital Futures, with responsibility for the design of our Data Science programme and delivery of a world-class learning experience for our engineers. Lisa has over 10 years experience in the data industry.