Java Deep Learning Solutions [Video]

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Java Deep Learning Solutions [Video]

Shreenidhi Sudhakar
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Understand Deep Learning algorithms with the most famous Java Framework: DL4J
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Video Details

ISBN 139781788396059
Course Length2 hours and 19 minutes

Video Description

Deep Learning is part of a broader family of machine learning methods based on learning data representations. Deeplearning4j is a Deep Learning programming library written in Java and the Java Virtual Machine (JVM) and is a computing framework with wide support for Deep Learning algorithms.

In this course, you start by installing Deep Learning software for Java. You learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. The course will take you into Neural Networks, working with Perceptron, XOR, and Gradient Descent on code examples. It then covers Convolutional Networks with extraction, Max pooling, and Softmax. You'll get hands-on with working examples at the end of each section. It also covers Recurrent Neural Network, explaining all the features of its Architecture. Finally, you'll learn about Word2Vec's different models and how to work with them via practical examples.

By the end of this video course, you’ll be ready to tackle Deep Learning with Java.

All the code and supporting files for this course are available on Github at - https://github.com/PacktPublishing/Java-Deep-Learning-Solutions-

Style and Approach

This course is packed with step-by-step instructions, working examples, and helpful advice about Deep Learning using the Java Framework. This practical course is divided into clear byte-size chunks so you can learn at your own pace and focus on the areas of most interest to you.

Table of Contents

Software Setup
The Course Overview
JDK
Eclipse IDE
Apache Maven
Sample Code
Artificial Neural Network in Deep Learning
Nervous System and Neural Network
Working with Perceptron
Simple Neural Network
Gradient Descent
Hyperparameters
Accuracy
Sample Code
Convolutional Neural Network in Deep Learning
Data Extraction
Understanding Max Pooling
Dropout layer
Softmax
Sample Code
Recurrent Neural Network
Architecture of RNN
Types of RNN
Sample Code
Understanding Word2Vec
Introduction to Word2Vec
Continuous Bag of Words Model
Skip - Gram Model
Negative Sampling
PseudoCode

What You Will Learn

  • Install various Deep Learning applications
  • Work with Perceptron in Neural Network
  • Implement the XOR using Neural Network
  • Classify your data with Softmax
  • Use Recurrent Network and its Architecture
  • Work with the different models in Word2Vec
  • Devise strategies to use Deep Learning algorithms and libraries in the real world
  • Gain an insight into the Deep Learning library, DL4J, and its practical uses

Authors

Table of Contents

Software Setup
The Course Overview
JDK
Eclipse IDE
Apache Maven
Sample Code
Artificial Neural Network in Deep Learning
Nervous System and Neural Network
Working with Perceptron
Simple Neural Network
Gradient Descent
Hyperparameters
Accuracy
Sample Code
Convolutional Neural Network in Deep Learning
Data Extraction
Understanding Max Pooling
Dropout layer
Softmax
Sample Code
Recurrent Neural Network
Architecture of RNN
Types of RNN
Sample Code
Understanding Word2Vec
Introduction to Word2Vec
Continuous Bag of Words Model
Skip - Gram Model
Negative Sampling
PseudoCode

Video Details

ISBN 139781788396059
Course Length2 hours and 19 minutes
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