PAC | Deep Learning With Java (2019 EN)

Company: Packt Publishing
Author: Tomasz Lelek
Full Title: Deep Learning With Java
Year: 2019
Language: English
Genre: Educational: Data
Skill Level: -
Price: €124.99
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Files: MP4 (+ Code Files)
Time: 01:53:52
Video: AVC, 1920 x 1080 (1.778) at 30.000 fps, 400 kbps
Audio: AAC at 121 Kbps, 2 channels, 48.0 KHz



Build sophisticated algorithms that are fundamental to deep learning and AI with Java 12.

Deep learning (DL) is used across a broad range of industries as the fundamental driver of AI. Being able to apply deep learning with Java will be a vital and valuable skill, not only within the tech world but also the wider global economy, which depends upon solving problems with higher accuracy and much more predictability than other AI techniques could provide.

This step-by-step, practical tutorial teaches you how to implement key concepts and adopts a hands-on approach to key algorithms to help you develop a greater understanding of deep learning. You will learn how to use the DL4J library and apply deep learning to a range of real-world use cases. This course will also help you solve challenging problems in image processing, speech recognition, and natural language modeling; it will make you rethink what you can do with Java, showing you how to use it for truly cutting-edge predictive insights.

By the end of this course, you'll be ready to tackle deep learning with Java. Whether you come from a data science background or are a Java developer, you will become part of the deep learning revolution!


Learn:
✓ Extract features from unstructured data using ND4J
✓ Use DL4J to perform fast and efficient deep learning training
✓ Perform automatic speech recognition with DL
✓ Use RNN with DL to achieve more precise results based on previous history
✓ Process image data using multiple layers with DL4J
✓ Use Word2Vect to perform feature extraction on text data
✓ Predict using classification with a multilayered approach

Features:
✓ Learn key algorithms needed to enhance your understanding of deep learning
✓ Use Java and deep neural networks to solve problems with the help of image processing, speech recognition, and natural language modeling
✓ Use the DL4J library and apply deep learning concepts to real-world use cases


Lessons:
1. Leveraging Ecosystem with Java 12
01. The Course Overview
02. Starting with Deep Learning Java API
03. Using DL4J API
04. Using ND4J for Feature Vectors
05. Creating Multi-Dimensional Features with ND4J
06. Performing Vector Operations Using ND4J
2. Human Speech Recognition Using Classification
07. Preparing Input Speech Data
08. Leveraging Word Vectors Construct to Map Sentences to DL Domain
09. Creating Layers Responsible for Feature Extraction
10. Focusing on Features - Finding out the Most Important Input Data for Dl
11. Performing Classification of Speech Data
3. Image Processing Using RNN DL Techniques
12. Analyzing Input Video Data and Data Pre-Generation
13. Achieving Image Processing Classification with Neural Network
14. Leveraging State of Previously Processed Data with RNN
15. Cross-Validation of the DL Model
16. Tweaking Performance of Image Processing Model
4. Deep Learning for Natural Language Modeling
17. Analyzing Input Text Data Used for NLP Modeling
18. Leveraging Word2Vec with DL4J
19. Feeding Features from Text into DL Network
20. Leveraging Text Iterator API from DL4J
21. Starting Training and Cross-Validating Results
5. Classification Prediction Using DL
22. Analyzing Input Data about Persons
23. Choosing a Feature to Extract and Use in Model
24. Creating MultiLayered Model Using DL4J
25. Transforming Features to Input DL Vectors
26. Predicting the Classification for Persons

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