O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Training, Evaluating, and Tuning Deep Neural Network Models with TensorFlow-Slim

Video Description

This course builds on the training in Marvin Bertin's "Introduction to TensorFlow-Slim", which covered the basic concepts and uses of the TensorFlow-Slim (TF-Slim) API. In a series of lessons designed for learners with basic machine learning knowledge and some previous TensorFlow experience, you'll explore many of TF-Slim's most advanced features; using them to build and train sophisticated deep learning models.

As you work through the examples, you'll come to appreciate TF-Slim's primary benefit: Its ability to enable the work of machine learning while avoiding code complexity, a significant problem in the world of increasingly deep neural networks.

  • Learn to construct and customize losses functions for regression, classification, and multi-task problems
  • Discover how to combine various metrics and use them to measure model performance
  • Understand how to automate training and evaluation routines
  • Learn how to train and evaluate a convolutional neural network model
  • See how you can improve model performance by using fine-tuning on pre-trained models
  • Gain experience using transfer learning for new predictive tasks
Marvin Bertin is a data scientist with Driver, a San Francisco based biotech startup. Before that, he worked as a deep learning researcher for the AI company Skymind. Marvin holds degrees in Data Science and Mechanical Engineering, has authored a number of courses on deep learning, and is a speaker at machine learning and deep learning conferences.