by Ahmed Sherif, Amrith Ravindra | Released October 2018 | ISBN: 9781789955309

Implement practical hands-on examples with Apache SparkAbout This VideoDiscover practical recipes for distributed deep learning with Apache SparkPredict real estate value using XGBoostCreate and visualize Word Vectors using Word2VecEvaluate the recommendation engine's accuracyIn DetailIn 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.The code bundle for this video course is available at https://github.com/PacktPublishing/Advanced-Apache-spark-Deep-learning-recipesDownloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you. Show and hide more

1. Chapter 1 : Using LSTMs in Generative Networks
   • The Course overview 00:03:00
   • Downloading Novels/Books that will be used as Input Text 00:05:39
   • Preparing and Cleansing Data 00:03:04
   • Tokenizing Sentences 00:03:53
   • Training and Saving the LSTM Model 00:05:04
   • Generating Similar Text using the Model 00:03:27
2. Chapter 2 : Real Estate Value Prediction Using XGBoost
   • Downloading the King County House Sales Dataset 00:03:41
   • Performing Exploratory Analysis and Visualization 00:05:40
   • Plotting Correlation Between Price and Other Features 00:04:24
   • Predicting the Price of a House 00:05:16
3. Chapter 3 : Face Recognition Using Deep Convolutional Networks
   • Downloading and Loading the MIT-CBCL Dataset into the Memory 00:03:25
   • Plotting and Visualizing Images from the Directory 00:03:27
   • Preprocessing Images 00:04:30
4. Chapter 4 : Creating and Visualizing Word Vectors Using Word2Vec
   • Acquiring Data 00:04:09
   • Importing the Necessary Libraries 00:04:24
   • Preparing the Data 00:02:49
   • Building and Training the Model 00:02:42
   • Visualizing Further 00:04:16
   • Analyzing Further 00:02:30
5. Chapter 5 : Creating a Movie Recommendation Engine with Keras
   • Downloading MovieLens Datasets 00:05:20
   • Manipulating and Merging the MovieLens Datasets 00:03:53
   • Exploring the MovieLens Datasets 00:02:24
   • Preparing Dataset for the Deep Learning Pipeline 00:02:49
   • Applying the Deep Learning Model with Keras 00:04:18
   • Evaluating the recommendation engine's accuracy 00:01:36
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