MP4 | Video: AVC 1920×1080 | Audio: AAC 48KHz 2ch | Duration: 1 hour 35 minutes | English | 624 MBVideo DescriptionIn this video course, you’ll work through specific recipes to generate outcomes for deep learning algorithms—without getting bogged down in theory.

From using LSTMs in generative networks to creating a movie recommendation engine, this course tackles both common and not so common problems so you can perform deep learning in a distributed environment.

In addition, you’ll get access to deep learning code within Spark that you can reuse to answer similar problems or tweak to answer slightly different problems.

You’ll learn how to predict real estate value using XGBoost.

You’ll also explore how to create a movie recommendation engine using popular libraries such as TensorFlow and Keras.

By the end of the course, you’ll have the expertise to train and deploy efficient deep learning models on Apache Spark.

Style and ApproachThis course includes practical, easy-to-understand solutions on how you can implement the popular deep learning libraries such as TensorFlow and Keras to train your deep learning models on Apache Spark.

Table of ContentsUSING LSTMS IN GENERATIVE NETWORKSREAL ESTATE VALUE PREDICTION USING XGBOOSTFACE RECOGNITION USING DEEP CONVOLUTIONAL NETWORKSCREATING AND VISUALIZING WORD VECTORS USING WORD2VECCREATING A MOVIE RECOMMENDATION ENGINE WITH KERAS