Mastering Deep Learning Using Apache Spark

BY
Simpliv Learning

Lavel

Expert

Mode

Online

Fees

$ 149 749

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course and certificate fees

Fees information
$ 149  $749
certificate availability

Yes

certificate providing authority

Simpliv Learning

The syllabus

Convolutional Neural Networks for Speech Recognization

  •  The Course Overview
  •  Analyzing Input Text Data That Will Need to Be Classified
  •  Configuring Word Vectors That Will Be Used in Our Network
  •  Adding Layers to Deep Neural Network
  •  Asserting Classification of Input Sentences
  •  Convolutional Neural Networks for Speech Recognition (NLP) - Quiz

Performing Video Classification Using RNN and LST

  •  Generating Input Video Data
  •  Creating a Neural Network for Video Classification
  •  Adding RNN and LSTMs to Network to Perform a Task Better
  •  Testing and Validating Deep Learning Model
  •  Performing Video Classification Using RNN and LSTMs - Quiz

Transfer Learning and Pre-Trained Models

  •  Creating Paragraph Vectors
  •  Adding Labels to Non-Labelled Data
  •  Finding Similarity between Vectors
  •  Creating a Model That Can Guess the Meaning of The Word
  •  Transfer Learning and Pre-Trained Models - Quiz

Deep Reinforcement Learning

  •  Anomaly Detection Problem Explained
  •  Extracting Features from Input Data Using Multi-Layer Approach
  •  Adding Layer That Finds an Actual Anomaly
  •  Testing and Validating Results from Our Deep Learning Model
  •  Deep Reinforncement Learning - Quiz

Generative Adversarial Networks

  •  Creating Data Generator for GAN
  •  Adding Discriminator for Our Data
  •  Create Classifier for Generated Data
  •  Performing Validation of Our Model
  •  Generative Adversarial Networks - Quiz

Distributed Models

  •  Configuring Spark for High Data Distribution
  •  Fetching Input Set into Distributed Data Set Using Spark API
  •  Creating Training Master That Supervise Computations on the Workers
  •  Evaluating Speed of Distributed Training Using Spark

Troubleshooting

  •  Monitoring of Models Using Spark UI
  •  Speeding Up Computations by Employing Caching
  •  Partitioning Deep Learning Data into Several Workers
  •  Tweaking Spark Workers Configuration

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