by Marvin Bertin | Released April 2017 | ISBN: 9781491986073

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 tasksMarvin 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. Show and hide more Publisher resources Download Example Code

1. Introduction
   • Welcome To The Course 00:01:47
   • About The Author 00:02:01
2. Training Deep Neural Network Models
   • Loss Function Module In TensorFlow-Slim: Part - 1 00:06:43
   • Loss Function Module In TensorFlow-Slim: Part - 2 00:12:19
   • Training Routines In TensorFlow-Slim: Part - 1 00:06:31
   • Training Routines In TensorFlow-Slim: Part - 2 00:05:08
3. Evaluating Deep Neural Network Models
   • Evaluation Metrics Module In TensorFlow-Slim: Part - 1 00:05:04
   • Evaluation Metrics Module In TensorFlow-Slim: Part - 2 00:04:10
   • Evaluation Routines In TensorFlow-Slim: Part - 1 00:06:57
   • Evaluation Routines In TensorFlow-Slim: Part - 2 00:06:00
4. Tuning Deep Neural Network Models
   • Fine-Tuning Existing Models In TensorFlow-Slim: Part 1 00:03:26
   • Fine-Tuning Existing Models In TensorFlow-Slim: Part 2 00:05:03
   • Tensorboard - Visualize Neural Networks And Inspect Model Learning 00:05:46
5. Conclusion
   • Wrap Up And Thank You 00:01:39
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