TensorFlow 1.X Recipes for Supervised and Unsupervised Learning [Video]
Deep Learning models often perform significantly better than traditional machine learning algorithms in many tasks. This course consists of hands-on recipes to use deep learning in the context of supervised and unsupervised learning tasks.
After covering the basics of working with TensorFlow, it shows you how to perform the traditional machine learning tasks in supervised learning: regression and classification. This course also covers how to perform unsupervised learning using cutting-edge techniques from Deep Learning.
To address many different use cases, this product presents recipes for both the low-level API (TensorFlow core) as well as the high-level APIs (tf.contrib.lean and Keras).
All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/TensorFlow-1.X-Recipes-for-Supervised-and-Unsupervised-Learning
Style and Approach
The course takes a recipe-based approach and will show you how to perform traditional machine learning tasks in supervised learning and also covers how to perform unsupervised learning using cutting-edge techniques from Deep Learning.
|Course Length||3 hours 19 minutes|
|Date Of Publication||21 Mar 2018|
|Set Up and Installing TensorFlow|
|Defining and Running a Computational Graph|
|Visualizing a Computational Graph With TensorBoard|
|How to Read Data From Files|
|The Hello World of Deep Learning – Your First Deep Neural Network|