Overview of this book
<p><span id="description" class="sugar_field">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. </span></p>
<p><span id="description" class="sugar_field">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. </span></p>
<p><span id="description" class="sugar_field">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).</span></p>
<p><span id="description" class="sugar_field">All the code and supporting files for this course are available on Github at <a style="font-weight: normal;" href="https://github.com/PacktPublishing/TensorFlow-1.X-Recipes-for-Supervised-and-Unsupervised-Learning" target="_new">https://github.com/PacktPublishing/TensorFlow-1.X-Recipes-for-Supervised-and-Unsupervised-Learning</a></span></p>
<h2>Style and Approach</h2>
<p>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.</p>
