English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 19m | 335 MB
Understand Deep Learning algorithms with the most famous Java Framework: DL4J
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.
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.
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
Table of Contents
Software Setup
1 The Course Overview
2 JDK
3 Eclipse IDE
4 Apache Maven
5 Sample Code
Artificial Neural Network in Deep Learning
6 Sample Code
7 Nervous System and Neural Network
8 Working with Perceptron
9 Simple Neural Network
10 Gradient Descent
11 Hyperparameters
12 Accuracy
Convolutional Neural Network in Deep Learning
13 Sample Code
14 Data Extraction
15 Understanding Max Pooling
16 Dropout layer
17 Softmax
Recurrent Neural Network
18 Sample Code
19 Architecture of RNN
20 Types of RNN
Understanding Word2Vec
21 Introduction to Word2Vec
22 Continuous Bag of Words Model
23 Skip – Gram Model
24 Negative Sampling
25 PseudoCode
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