[Packt] Machine Learning Projects with Java [FCO] GloDLS
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Torrent File Contents
[Packt] Machine Learning Projects with Java [FCO] GloDLS
1. FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA/1. The Course Overview.MP4
1. FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA/2. Performing Feature Engineering.MP4
1. FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA/3. Leveraging ND4J Library Input Vectors and Matrices.MP4
1. FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA/4. Extracting INDArray Features.MP4
1. FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA/5. Applying Scalar Transformations to Features Vectors.MP4
2. ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY/1. Project Set Up Using Weka Library.MP4
2. ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY/2. Data Mining of Input Data Set.MP4
2. ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY/3. Building Classifier in Weka Library.MP4
2. ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY/4. Performing Cross-Validation of the Model.MP4
2. ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY/5. Making Predictions Based on the Classification.MP4
3. BUILDING REGRESSION MODEL FOR HOUSING MARKET/1. Extracting Feature Vector for Housing Data.MP4
3. BUILDING REGRESSION MODEL FOR HOUSING MARKET/2. Performing Normalization of Data.MP4
3. BUILDING REGRESSION MODEL FOR HOUSING MARKET/3. Building Regression Model.MP4
3. BUILDING REGRESSION MODEL FOR HOUSING MARKET/4. Leveraging Regression Model for Predicting Price of House.MP4
3. BUILDING REGRESSION MODEL FOR HOUSING MARKET/5. Saving Model for Further Re-Usage.MP4
4. DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME/1. Feeding DL4J Model with Gender Labeled Data.MP4
4. DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME/2. Creating a .java File for Automatic Feature Extraction.MP4
4. DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME/3. Creating Neural Network with Multiple Layers.MP4
4. DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME/4. Training of Deep Learning Model.MP4
4. DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME/5. Performing Validation of a Model.MP4
5. FINDING SIMILARITY OF WORDS IN A BOOK USING NLP WITH DEEP LEARNING/1. Extracting Feature Vector from Text Data.MP4
5. FINDING SIMILARITY OF WORDS IN A BOOK USING NLP WITH DEEP LEARNING/2. Loading Raw Data That will be an Input for NLP Training.MP4
5. FINDING SIMILARITY OF WORDS IN A BOOK USING NLP WITH DEEP LEARNING/3. Leveraging NLP Construct from DL4J.MP4
5. FINDING SIMILARITY OF WORDS IN A BOOK USING NLP WITH DEEP LEARNING/4. Finding Words Based on the Similarity.MP4
Discuss.FTUForum.com.html
FreeCoursesOnline.Me.html
FTUForum.com.html
How you can help Team-FTU.txt
[TGx]Downloaded from torrentgalaxy.org.txt
Torrent Downloaded From GloDls.buzz.txt
1. FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA/1. The Course Overview.MP4
- 24.41 MB
1. FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA/2. Performing Feature Engineering.MP4
- 40.86 MB
1. FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA/3. Leveraging ND4J Library Input Vectors and Matrices.MP4
- 18.75 MB
1. FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA/4. Extracting INDArray Features.MP4
- 22.63 MB
1. FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA/5. Applying Scalar Transformations to Features Vectors.MP4
- 26.88 MB
2. ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY/1. Project Set Up Using Weka Library.MP4
- 26.19 MB
2. ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY/2. Data Mining of Input Data Set.MP4
- 15.58 MB
2. ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY/3. Building Classifier in Weka Library.MP4
- 19.5 MB
2. ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY/4. Performing Cross-Validation of the Model.MP4
- 13.93 MB
2. ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY/5. Making Predictions Based on the Classification.MP4
- 22.04 MB
3. BUILDING REGRESSION MODEL FOR HOUSING MARKET/1. Extracting Feature Vector for Housing Data.MP4
- 19.66 MB
3. BUILDING REGRESSION MODEL FOR HOUSING MARKET/2. Performing Normalization of Data.MP4
- 20.01 MB
3. BUILDING REGRESSION MODEL FOR HOUSING MARKET/3. Building Regression Model.MP4
- 17.1 MB
3. BUILDING REGRESSION MODEL FOR HOUSING MARKET/4. Leveraging Regression Model for Predicting Price of House.MP4
- 22.7 MB
3. BUILDING REGRESSION MODEL FOR HOUSING MARKET/5. Saving Model for Further Re-Usage.MP4
- 16.47 MB
4. DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME/1. Feeding DL4J Model with Gender Labeled Data.MP4
- 16.89 MB
4. DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME/2. Creating a .java File for Automatic Feature Extraction.MP4
- 21.41 MB
4. DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME/3. Creating Neural Network with Multiple Layers.MP4
- 21.92 MB
4. DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME/4. Training of Deep Learning Model.MP4
- 22.05 MB
4. DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME/5. Performing Validation of a Model.MP4
- 29.88 MB
5. FINDING SIMILARITY OF WORDS IN A BOOK USING NLP WITH DEEP LEARNING/1. Extracting Feature Vector from Text Data.MP4
- 25.11 MB
5. FINDING SIMILARITY OF WORDS IN A BOOK USING NLP WITH DEEP LEARNING/2. Loading Raw Data That will be an Input for NLP Training.MP4
- 17.76 MB
5. FINDING SIMILARITY OF WORDS IN A BOOK USING NLP WITH DEEP LEARNING/3. Leveraging NLP Construct from DL4J.MP4
- 69.08 MB
5. FINDING SIMILARITY OF WORDS IN A BOOK USING NLP WITH DEEP LEARNING/4. Finding Words Based on the Similarity.MP4
- 79.03 MB
Discuss.FTUForum.com.html
- 31.89 KB
FreeCoursesOnline.Me.html
- 108.3 KB
FTUForum.com.html
- 100.44 KB
How you can help Team-FTU.txt
- 235 Bytes
[TGx]Downloaded from torrentgalaxy.org.txt
- 524 Bytes
Torrent Downloaded From GloDls.buzz.txt
- 84 Bytes
Torrent Description
Description:
By: Tomasz Lelek
Released: Friday, March 29, 2019 [New Release!]
Torrent Contains: 30 Files, 5 Folders
Course Source: https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-projects-java-video
Learn how to leverage well-proven ML algorithms to solve day-to-day ML problems
Video Details
ISBN 9781789612455
Course Length 2 hour 12 minutes
Table of Contents
• FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA
• ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY
• BUILDING REGRESSION MODEL FOR HOUSING MARKET
• DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME
• FINDING SIMILARITY OF WORDS IN A BOOK USING NLP WITH DEEP LEARNING
Video Description
Developers are worried about using various algorithms to solve different problems. This course is a perfect guide to identifying the best solution to efficiently build machine learning projects for different use cases to solve real-world problems.
In this course, you will learn how to build a model that takes complex feature vector form sensor data and classifies data points into classes with similar characteristics. Then you will predict the price of a house based on historical data. Finally, you will build a Deep Learning model that can guess personality traits using labeled data.
By the end of this course, you will have mastered each machine learning domain and will be able to build your own powerful projects at work.
Style and Approach
This is a step-by-step and fast-paced guide that will help you learn different ML techniques you can use to solve real-world problems, Every section will tackle a practical problem and take your ML skills to the next level
What You Will Learn
• Perform classification using the Weka Library.
• Implement Pattern Recognition of non-labeled data
• Build Regression models for data with multiple features
• Save trained models for further reusability
• Learn how to perform cross-validation
• Leverage Deep Learning in ML problems
• Implement Natural Language Processing with Deep Learning
Authors
Tomasz Lelek
Tomasz Lelek is a Software Engineer, programming mostly in Java, Scala. He has worked with ML algorithms for the past 5 years, with production experience in processing petabytes of data.
He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before solving a problem. Recently he was a speaker at conferences in Poland, Confitura and JDD (Java Developers Day), and also at Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.
He is a co-founder of www.initlearn.com, an e-learning platform that was built with the Java language.
He has also written articles about everything related to the Java world: http://www.baeldung.com/.
By: Tomasz Lelek
Released: Friday, March 29, 2019 [New Release!]
Torrent Contains: 30 Files, 5 Folders
Course Source: https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-projects-java-video
Learn how to leverage well-proven ML algorithms to solve day-to-day ML problems
Video Details
ISBN 9781789612455
Course Length 2 hour 12 minutes
Table of Contents
• FEATURE EXTRACTION FOR UNSTRUCTURED TEXTUAL NEWS FEED DATA
• ML CLASSIFICATION FOR PATTERN RECOGNITION OF SENSOR DATA USING WEKA LIBRARY
• BUILDING REGRESSION MODEL FOR HOUSING MARKET
• DEEP LEARNING FOR PREDICTING GENDER BASED ON THE NAME
• FINDING SIMILARITY OF WORDS IN A BOOK USING NLP WITH DEEP LEARNING
Video Description
Developers are worried about using various algorithms to solve different problems. This course is a perfect guide to identifying the best solution to efficiently build machine learning projects for different use cases to solve real-world problems.
In this course, you will learn how to build a model that takes complex feature vector form sensor data and classifies data points into classes with similar characteristics. Then you will predict the price of a house based on historical data. Finally, you will build a Deep Learning model that can guess personality traits using labeled data.
By the end of this course, you will have mastered each machine learning domain and will be able to build your own powerful projects at work.
Style and Approach
This is a step-by-step and fast-paced guide that will help you learn different ML techniques you can use to solve real-world problems, Every section will tackle a practical problem and take your ML skills to the next level
What You Will Learn
• Perform classification using the Weka Library.
• Implement Pattern Recognition of non-labeled data
• Build Regression models for data with multiple features
• Save trained models for further reusability
• Learn how to perform cross-validation
• Leverage Deep Learning in ML problems
• Implement Natural Language Processing with Deep Learning
Authors
Tomasz Lelek
Tomasz Lelek is a Software Engineer, programming mostly in Java, Scala. He has worked with ML algorithms for the past 5 years, with production experience in processing petabytes of data.
He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before solving a problem. Recently he was a speaker at conferences in Poland, Confitura and JDD (Java Developers Day), and also at Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.
He is a co-founder of www.initlearn.com, an e-learning platform that was built with the Java language.
He has also written articles about everything related to the Java world: http://www.baeldung.com/.