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[FreeCoursesOnline.Me] [Packt] Mastering Deep Learning using Apache Spark [FCO]

[FreeCoursesOnline.Me] [Packt] Mastering Deep Learning using Apache Spark [FCO]

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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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File list
  • [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 MB
  • 1. CONVOLUTIONAL NEURAL NETWORKS FOR SPEECH RECOGNITION (NLP)/2. Analyzing Input Text Data That Will Need to Be Classified-111793.mp4 53.9 MB
  • 1. CONVOLUTIONAL NEURAL NETWORKS FOR SPEECH RECOGNITION (NLP)/3. Configuring Word Vectors That Will Be Used in Our Network-111794.mp4 14.3 MB
  • 1. CONVOLUTIONAL NEURAL NETWORKS FOR SPEECH RECOGNITION (NLP)/4. Adding Layers to Deep Neural Network-111795.mp4 14.7 MB
  • 1. CONVOLUTIONAL NEURAL NETWORKS FOR SPEECH RECOGNITION (NLP)/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
  • 2. PERFORMING VIDEO CLASSIFICATION USING RNN AND LSTMS/7. Creating a Neural Network for Video Classification-111799.mp4 18.3 MB
  • 2. PERFORMING VIDEO CLASSIFICATION USING RNN AND LSTMS/8. Adding RNN and LSTMs to Network to Perform a Task Better-111800.mp4 19 MB
  • 2. PERFORMING VIDEO CLASSIFICATION USING RNN AND LSTMS/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
  • 3. TRANSFER LEARNING AND PRE-TRAINED MODELS/11. Adding Labels to Non-Labelled Data-111804.mp4 17.6 MB
  • 3. TRANSFER LEARNING AND PRE-TRAINED MODELS/12. Finding Similarity between Vectors-111805.mp4 16.6 MB
  • 3. TRANSFER LEARNING AND PRE-TRAINED MODELS/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
  • 4. DEEP REINFORCEMENT LEARNING/15. Extracting Features from Input Data Using Multi-Layer Approach-111809.mp4 26.7 MB
  • 4. DEEP REINFORCEMENT LEARNING/16. Adding Layer That Finds an Actual Anomaly-111810.mp4 17 MB
  • 4. DEEP REINFORCEMENT LEARNING/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
  • 5. GENERATIVE ADVERSARIAL NETWORKS/19. Adding Discriminator for Our Data-111814.mp4 31 MB
  • 5. GENERATIVE ADVERSARIAL NETWORKS/20. Create Classifier for Generated Data-111815.mp4 24.3 MB
  • 5. GENERATIVE ADVERSARIAL NETWORKS/21. Performing Validation of Our Model-111816.mp4 16.5 MB
  • 6. DISTRIBUTED MODELS/22. Configuring Spark for High Data Distribution-111818.mp4 16 MB
  • 6. DISTRIBUTED MODELS/23. Fetching Input Set into Distributed Data Set Using Spark API-111819.mp4 14.2 MB
  • 6. DISTRIBUTED MODELS/24. Creating Training Master That Supervise Computations on the Workers-111820.mp4 13.6 MB
  • 6. DISTRIBUTED MODELS/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
  • 7. TROUBLESHOOTING/27. Speeding Up Computations by Employing Caching-111824.mp4 14.5 MB
  • 7. TROUBLESHOOTING/28. Partitioning Deep Learning Data into Several Workers-111825.mp4 64.8 MB
  • 7. TROUBLESHOOTING/29. Tweaking Spark Workers Configuration-111826.mp4 56.9 MB
  • Discuss.FTUForum.com.html 31.9 KB
  • FreeCoursesOnline.Me.html 108.3 KB
  • FTUForum.com.html 100.4 KB
  • How you can help Team-FTU.txt 235 B

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