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Deep Learning with Apache Spark - MasterClass!

The Udemy Deep Learning with Apache Spark – MasterClass! free download also includes 5 hours on-demand video, 5 articles, 57 downloadable resources, Full lifetime access, Access on mobile and TV, Assignments, Certificate of Completion and much more.

What you'll learn
  • Explore deep learning neural networks such as RBM, RNN, and DBN using some of the most popular industrial deep learning frameworks.
  • Learn how to leverage big data to solve real-world problems using deep learning.
  • Understand how to formulate real-world prediction problems as machine learning tasks, how to choose the right neural net architecture for a problem, and how to train neural nets using DL4J.
  • Configure a Convolutional Neural Network (CNN) to extract value from images.
  • Create a deep network with multiple layers to perform computer vision.
  • Classify speech and audio data.
  • Get up-and-running and gain an insight into the deep learning library DL4J and its practical uses.
  • Train and test neural networks to fit your data model.


Deep learning has solved tons of interesting real-world problems in recent years. Apache Spark has emerged as the most important and promising Machine Learning tool and currently a stronger challenger of the Hadoop ecosystem. In this course, you’ll learn about the major branches of AI and get familiar with several core models of Deep Learning in its natural way.

This comprehensive 3-in-1 course is a fast-paced guide to implementing practical hands-on examples, streamlining Deep Learning with Apache Spark. You’ll begin by exploring Deep Learning Neural Networks using some of the most popular industrial Deep Learning frameworks. You’ll apply built-in Machine Learning libraries within Spark, also explore libraries that are compatible with TensorFlow and Keras. Next, you’ll create a deep network with multiple layers to perform computer vision and improve cybersecurity with Deep Reinforcement Learning. Finally, you’ll use a generative adversarial network for training and create highly distributed algorithms using Spark.

By the end of this course, you'll develop fast, efficient distributed Deep Learning models with Apache Spark.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Deep Learning with Apache Spark, covers deploying efficient deep learning models with Apache Spark. The tutorial begins by explaining the fundamentals of Apache Spark and deep learning. You will set up a Spark environment to perform deep learning and learn about the different types of neural net and the principles of distributed modeling (model- and data-parallelism, and more). You will then implement deep learning models (such as CNN, RNN, LTSMs) on Spark, acquire hands-on experience of what it takes, and get a general feeling for the complexity we are dealing with. You will also see how you can use libraries such as Deeplearning4j to perform deep learning on a distributed CPU and GPU setup. By the end of this course, you'll have gained experience by implementing models for applications such as object recognition, text analysis, and voice recognition. You will even have designed human expert games.

The second course, Apache Spark Deep Learning Recipes, covers over 35 recipes that streamline eep learning with Apache Spark. This video course starts offs by explaining the process of developing a neural network from scratch using deep learning libraries such as Tensorflow or Keras. It focuses on the pain points of convolution neural networks. We’ll predict fire department calls with Spark ML and Apple stock market cost with LSTM. We’ll walk you through the steps to classify chatbot conversation data for escalation. By the end of the video course, you'll have all the basic knowledge about apache spark.

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