Packt | Mastering Deep Learning using Apache Spark [FCO]

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[FreeCoursesOnline.Me] [Packt] Mastering Deep Learning using Apache Spark [FCO] 1. CONVOLUTIONAL NEURAL NETWORKS FOR SPEECH RECOGNITION (NLP)
  • 1. The Course Overview-111792.mp4 (17.0 MB)
  • 2. Analyzing Input Text Data That Will Need to Be Classified-111793.mp4 (53.9 MB)
  • 3. Configuring Word Vectors That Will Be Used in Our Network-111794.mp4 (14.3 MB)
  • 4. Adding Layers to Deep Neural Network-111795.mp4 (14.7 MB)
  • 5. Asserting Classification of Input Sentences-111796.mp4 (16.1 MB)
2. PERFORMING VIDEO CLASSIFICATION USING RNN AND LSTMS
  • 6. Generating Input Video Data-111798.mp4 (22.1 MB)
  • 7. Creating a Neural Network for Video Classification-111799.mp4 (18.3 MB)
  • 8. Adding RNN and LSTMs to Network to Perform a Task Better-111800.mp4 (19.0 MB)
  • 9. Testing and Validating Deep Learning Model-111801.mp4 (24.4 MB)
3. TRANSFER LEARNING AND PRE-TRAINED MODELS
  • 10. Creating Paragraph Vectors-111803.mp4 (9.4 MB)
  • 11. Adding Labels to Non-Labelled Data-111804.mp4 (17.6 MB)
  • 12. Finding Similarity between Vectors-111805.mp4 (16.6 MB)
  • 13. Creating a Model That Can Guess the Meaning of The Word-111806.mp4 (14.4 MB)
4. DEEP REINFORCEMENT LEARNING
  • 14. Anomaly Detection Problem Explained-111808.mp4 (27.3 MB)
  • 15. Extracting Features from Input Data Using Multi-Layer Approach-111809.mp4 (26.7 MB)
  • 16. Adding Layer That Finds an Actual Anomaly-111810.mp4 (17.0 MB)
  • 17. Testing and Validating Results from Our Deep Learning Model-111811.mp4 (17.4 MB)
5. GENERATIVE ADVERSARIAL NETWORKS
  • 18. Creating Data Generator for GAN-111813.mp4 (19.4 MB)
  • 19. Adding Discriminator for Our Data-111814.mp4 (31.0 MB)
  • 20. Create Classifier for Generated Data-111815.mp4 (24.3 MB)
  • 21. Performing Validation of Our Model-111816.mp4 (16.5 MB)
6. DISTRIBUTED MODELS
  • 22. Configuring Spark for High Data Distribution-111818.mp4 (16.0 MB)
  • 23. Fetching Input Set into Distributed Data Set Using Spark API-111819.mp4 (14.2 MB)
  • 24. Creating Training Master That Supervise Computations on the Workers-111820.mp4 (13.6 MB)
  • 25. Evaluating Speed of Distributed Training Using Spark-111821.mp4 (9.9 MB)
7. TROUBLESHOOTING
  • 26. Monitoring of Models Using Spark UI-111823.mp4 (11.8 MB)
  • 27. Speeding Up Computations by Employing Caching-111824.mp4 (14.5 MB)
  • 28. Partitioning Deep Learning Data into Several Workers-111825.mp4 (64.8 MB)
  • 29. Tweaking Spark Workers Configuration-111826.mp4 (56.9 MB)
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Description



By : Tomasz Lelek
Released : Tuesday, April 16, 2019 [New Release!]
Torrent Contains : 34 Files, 7 Folders
Course Source : https://www.packtpub.com/big-data-and-business-intelligence/mastering-deep-learning-using-apache-spark-video

Develop industrial solutions based on deep learning models with Apache Spark

Video Details

ISBN 9781788292511
Course Length 2 hour 3 minutes

Table of Contents

• CONVOLUTIONAL NEURAL NETWORKS FOR SPEECH RECOGNITION (NLP)
• PERFORMING VIDEO CLASSIFICATION USING RNN AND LSTMS
• TRANSFER LEARNING AND PRE-TRAINED MODELS
• DEEP REINFORCEMENT LEARNING
• GENERATIVE ADVERSARIAL NETWORKS
• DISTRIBUTED MODELS
• TROUBLESHOOTING

Video Description

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.

You’ll begin with building deep learning networks to deal with speech data and explore tricks to solve NLP problems and classify video frames using RNN and LSTMs. You’ll also learn to implement the anomaly detection model that leverages reinforcement learning techniques to improve cyber security.

Moving on, you’ll explore some more advanced topics by performing prediction classification on image data using the GAN encoder and decoder. Then you’ll configure Spark to use multiple workers and CPUs to distribute your Neural Network training. Finally, you’ll track progress, solve the most common problems in your neural network, and debug your models that run within the distributed Spark engine.

Style and Approach

This course takes a practical approach to networking and will get you familiar with several core models. It will help you implement deep learning models like CNN, RNN, LTSMs on Spark and get hands-on experience of what it takes and a general feeling of the complexity we are dealing with.

What You Will Learn

• 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
• Leverage RNN and LSTMs for video classification for hospital data
• Improve cybersecurity with deep reinforcement learning
• Use a generative adversarial network for training
• Create highly distributed algorithms using Spark

Authors

Tomasz Lelek

Tomasz Lelek is a Software Engineer who programs mostly in Java and Scala. He has worked with Spark API and the ML API for the past five years and has production experience in processing petabytes of data.

He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before solving a problem. Recently, he was a speaker at conferences in Poland, Confitura and JDD (Java Developers Day), and the Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.

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Packt | Mastering Deep Learning using Apache Spark [FCO]


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