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
Table of contents
- Training Deep Neural Network Models
- Evaluating Deep Neural Network Models
- Tuning Deep Neural Network Models
- Wrap Up And Thank You 00:01:39
- Title: Training, Evaluating, and Tuning Deep Neural Network Models with TensorFlow-Slim
- Release date: April 2017
- Publisher(s): Infinite Skills
- ISBN: 9781491986073
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