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- 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
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- Mastering Deep Learning Using Apache Spark
Mastering Deep Learning Using Apache Spark
Quick Facts
| particular | details | |||
|---|---|---|---|---|
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
$ 149 $749
certificate availability
Yes
certificate providing authority
Simpliv Learning
The syllabus
Convolutional Neural Networks for Speech Recognization
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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