Deep Learning with Apache Spark and BigDL Training

Apache Spark with Big DL specifies the developers for creating Deep Learning Application in Real-World Scenario

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Duration: 2 Day

Course fee:$399.00 (₹0)

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Product Description

Apache Spark and BigDL Deep Learning Training provides the developers to develop large datasets in very speedy way. The tackling of the program can be usually done with faster hardware (GPUs), Optimized codes and also some sort of parallelism. One of the foremost cloud-computing framework, Apache Spark, used by many of the organizations across the world for performing some advanced functionalities related to Cloud Applications. Big DL when used with Apache Spark creates Application that can be on Cloud using some functionalities that can lead to more secure. The training includes the introductory information about the Apache Spark and its related installation concepts. On this training, some of the Spark Example Repository related information are made available for creating an application for Deep Learning. On the completion of the training, the aspirants can gain expertise in creating Deep Learning Application using the Apache Spark and Big DL.

Objectives

  • Understanding the concept of training neural networks basically on spark cluster
  • Dealing with the Network related training for BigDL
  • Understanding the concept of using Spark local in conjunction with DL4J

Advantages

  • Completely secure and safe for the Blockchain developers
  • Synchronization and Serialization overhead concepts are most overwhelmed
  • Complete enhancement in Training Performance

 

 

Additional Information

Course Content

1. Basic Overview of Apache Spark

2. Understanding the features of Apache Spark

3. Core prerequisites for Apache Spark

4. Basic Configuration of TrainingMaster

5. Dealing with basic dependencies for Training

6. Understanding the concept of Spark Example Repository

7. Using the concept of GPU on Spark

– Dealing with YARN and GPUs
– Dealing with Mesos and GPUs

8. Memory Configuration for Spark on YARN

– Concept of DeepLearning Managing Memory
– Concept of YARN Handling Memory Management
– Memory Configuration Deeplearning spark training

9. Spark Locality Configuration for Improved Training Performance

10. Understanding the concept of Performance Debugging

11. Concept of Collecting Training Performance Information

12. Dealing with Caching/Persisting RDD and RDD

13. Working with Kyro Serialization

14 . Working with Intel MKL on Amazon Elastic MapReduce

15. Basic Overview of BigDL

16. Understanding the core concept of Distributed Deep Learning on Spark

17. Basic Features of BigDL

18. Reasons for Using BigDL

19. Working of BigDL

20. Practical Scenarios on BigDL

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