Machine learning may be overhyped nowadays, but there is still a strong belief that this area is exclusively for data scientists with a deep mathematical background who leverage the Python (scikit-learn, Theano, TensorFlow, etc.) or R ecosystems and use specific tools like R Studio, Matlab, or Octave. Obviously, there is some truth to this statement, but Java engineers can also take the best of the machine-learning world from an applied perspective by using our native language and familiar frameworks like Apache Spark. Taras Matyashovsky explains how to use Apache Spark MLlib to build a supervised learning NLP pipeline to distinguish pop music from heavy metal—and have fun in the process. Along the way, Taras offers an overview of the simplest machine-learning tasks and algorithms, like regression and classification.
Source code: https://github.com/tmatyashovsky/spark-ml-samples
Design by Yarko Filevych: http://filevych.com/
Machine learning is overhyped nowadays. There is a strong belief that this area is exclusively for data scientists with a deep mathematical background that leverage Python (scikit-learn, Theano, Tensorflow, etc.) or R ecosystem and use specific tools like Matlab, Octave or similar. Of course, there is a big grain of truth in this statement, but we, Java engineers, also can take the best of machine learning universe from an applied perspective by using our native language and familiar frameworks like Apache Spark. During this introductory presentation, you will get acquainted with the simplest machine learning tasks and algorithms, like regression, classification, clustering, widen your outlook and use Apache Spark MLlib to distinguish pop music from heavy metal and simply have fun.
Source code: https://github.com/tmatyashovsky/spark-ml-samples
Design by Yarko Filevych: http://filevych.com/
Bestseller Analysis: Visualization Fiction (for PyData Boston 2013)Lynn Cherny
A version of my OpenVisConf talk "Bones of a Bestseller" that gives more detail on topic analysis plus adds python code. Blog post and ipynb code here: http://blogger.ghostweather.com/2013/08/pydata-boston-2013-more-on-fiction.html
From robots to online coding games, the different uses of pythonCeline Boudier
Python is one of the most popular programming languages, and for many good reasons.
It can be used for many different purposes, including web development, language processing or teaching how to code.
We will look at some reasons that make Python so widely useful, and different libraries, frameworks and projects that showcase it.
-
Talk given in October 2018 at Kraków 4Developers in Poland
Describes short summary and achievements of Morning@Lohika events (http://morning.lohika.com) during the third year of operation.
Design by Yarko Filevych (www.filevych.com)
We all are professionals, e.g. software engineers, quality engineers, technical/team leaders, project/product managers, etc. But we all are humans too. Often due to different reasons, like tight deadlines, push from customers/clients, etc., we all tend to neglect common sense and omit important practices. In this talk based on my both positive and negative experience we will review some patterns how we make common mistakes and what terrible results they may lead us to.
Presented at XP Days Ukraine Conference in Kyiv in 2015.
Design by Yarko Filevych (http://www.filevych.com/)
Machine learning is overhyped nowadays. There is a strong belief that this area is exclusively for data scientists with a deep mathematical background that leverage Python (scikit-learn, Theano, Tensorflow, etc.) or R ecosystem and use specific tools like Matlab, Octave or similar. Of course, there is a big grain of truth in this statement, but we, Java engineers, also can take the best of machine learning universe from an applied perspective by using our native language and familiar frameworks like Apache Spark. During this introductory presentation, you will get acquainted with the simplest machine learning tasks and algorithms, like regression, classification, clustering, widen your outlook and use Apache Spark MLlib to distinguish pop music from heavy metal and simply have fun.
Source code: https://github.com/tmatyashovsky/spark-ml-samples
Design by Yarko Filevych: http://filevych.com/
Bestseller Analysis: Visualization Fiction (for PyData Boston 2013)Lynn Cherny
A version of my OpenVisConf talk "Bones of a Bestseller" that gives more detail on topic analysis plus adds python code. Blog post and ipynb code here: http://blogger.ghostweather.com/2013/08/pydata-boston-2013-more-on-fiction.html
From robots to online coding games, the different uses of pythonCeline Boudier
Python is one of the most popular programming languages, and for many good reasons.
It can be used for many different purposes, including web development, language processing or teaching how to code.
We will look at some reasons that make Python so widely useful, and different libraries, frameworks and projects that showcase it.
-
Talk given in October 2018 at Kraków 4Developers in Poland
Describes short summary and achievements of Morning@Lohika events (http://morning.lohika.com) during the third year of operation.
Design by Yarko Filevych (www.filevych.com)
We all are professionals, e.g. software engineers, quality engineers, technical/team leaders, project/product managers, etc. But we all are humans too. Often due to different reasons, like tight deadlines, push from customers/clients, etc., we all tend to neglect common sense and omit important practices. In this talk based on my both positive and negative experience we will review some patterns how we make common mistakes and what terrible results they may lead us to.
Presented at XP Days Ukraine Conference in Kyiv in 2015.
Design by Yarko Filevych (http://www.filevych.com/)
This presentation is inspired by famous book by Robert Cialdini "Influence: The Psychology of Persuasion" and will be useful to those who would like to get acquainted with popular weapons of influence or just broaden own outlook. It recalls real life cases mentioned in the book as well as similar situations that are fully IT-related and based on my own experience and observation.
Design by Yarko Filevych (http://www.filevych.com/)
JEEConf 2015 - Introduction to real-time big data with Apache SparkTaras Matyashovsky
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on JEEConf 2015 in Kyiv.
Design by Yarko Filevych: http://www.filevych.com/
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on Morning@Lohika tech talks in Lviv.
Design by Yarko Filevych: http://www.filevych.com/
This presentation will be useful to those
who would like to get acquainted with lifetime history
of successful monolithic Java application.
It shows architectural and technical evolution of one Java web startup that is beyond daily coding routine and contains a lot of simplifications, Captain Obvious and internet memes.
But this presentation is not intended for monolithic vs. micro services architectures comparison.
Do you need to scale your application, share data across cluster, perform massive parallel processing on many JVMs or maybe consider alternative to your favorite NoSQL technology? Hazelcast to the rescue! With Hazelcast distributed development is much easier. This presentation will be useful to those who would like to get acquainted with Hazelcast top features and see some of them in action, e.g. how to cluster application, cache data in it, partition in-memory data, distribute workload onto many servers, take advantage of parallel processing, etc.
Presented on JavaDay Kyiv 2014 conference.
Morning@Lohika events were initiated by Lohika Systems Company. This presentation covers basic information about Morning@Lohika initiative, e.g. main goals, format, organizers, etc.
From cache to in-memory data grid. Introduction to Hazelcast.Taras Matyashovsky
This presentation:
* covers basics of caching and popular cache types
* explains evolution from simple cache to distributed, and from distributed to IMDG
* not describes usage of NoSQL solutions for caching
* is not intended for products comparison or for promotion of Hazelcast as the best solution
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
This presentation is inspired by famous book by Robert Cialdini "Influence: The Psychology of Persuasion" and will be useful to those who would like to get acquainted with popular weapons of influence or just broaden own outlook. It recalls real life cases mentioned in the book as well as similar situations that are fully IT-related and based on my own experience and observation.
Design by Yarko Filevych (http://www.filevych.com/)
JEEConf 2015 - Introduction to real-time big data with Apache SparkTaras Matyashovsky
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on JEEConf 2015 in Kyiv.
Design by Yarko Filevych: http://www.filevych.com/
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on Morning@Lohika tech talks in Lviv.
Design by Yarko Filevych: http://www.filevych.com/
This presentation will be useful to those
who would like to get acquainted with lifetime history
of successful monolithic Java application.
It shows architectural and technical evolution of one Java web startup that is beyond daily coding routine and contains a lot of simplifications, Captain Obvious and internet memes.
But this presentation is not intended for monolithic vs. micro services architectures comparison.
Do you need to scale your application, share data across cluster, perform massive parallel processing on many JVMs or maybe consider alternative to your favorite NoSQL technology? Hazelcast to the rescue! With Hazelcast distributed development is much easier. This presentation will be useful to those who would like to get acquainted with Hazelcast top features and see some of them in action, e.g. how to cluster application, cache data in it, partition in-memory data, distribute workload onto many servers, take advantage of parallel processing, etc.
Presented on JavaDay Kyiv 2014 conference.
Morning@Lohika events were initiated by Lohika Systems Company. This presentation covers basic information about Morning@Lohika initiative, e.g. main goals, format, organizers, etc.
From cache to in-memory data grid. Introduction to Hazelcast.Taras Matyashovsky
This presentation:
* covers basics of caching and popular cache types
* explains evolution from simple cache to distributed, and from distributed to IMDG
* not describes usage of NoSQL solutions for caching
* is not intended for products comparison or for promotion of Hazelcast as the best solution
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
6. “I'm a rolling thunder, a pouring rain
I'm comin' on like a hurricane
My lightning's flashing across the sky
You're only young but you're gonna die
I won't take no prisoners, won't spare no lives
Nobody's putting up a fight
I got my bell, I'm gonna take you to hell
I'm gonna get you, Satan get you”
https://github.com/tmatyashovsky/spark-ml-samples
6
7. “I'm a rolling thunder, a pouring rain
I'm comin' on like a hurricane
My lightning's flashing across the sky
You're only young but you're gonna die
I won't take no prisoners, won't spare no lives
Nobody's putting up a fight
I got my bell, I'm gonna take you to hell
I'm gonna get you, Satan get you”
https://github.com/tmatyashovsky/spark-ml-samples
7
15. Date & time
Conference name
Speaker
Talk name
Track
Duration
Type
Overall impression
Overall rating
Number of slides
Time spent on live
coding
Number of jokes
Etc.
15
32. Collect data set of lyrics:
Abba, Ace of base, Backstreet Boys, Britney Spears,
Christina Aguilera, Madonna, etc.
Black Sabbath, In Flames, Iron Maiden, Metallica,
Moonspell, Nightwish, Sentenced, etc.
Create training set, i.e. label (0|1) + features
Train logistic regression (or other classification
algorithm)
https://github.com/tmatyashovsky/spark-ml-samples
32
38. 38
Verse Cosine Distance
baby one more time 0.482028
crazy for you 0.437875
show me the meaning
of being lonely
0.258147
highway to hell -0.1120049
kill them all -0.231876
https://github.com/tmatyashovsky/spark-ml-samples
51. Is a library of ML algorithms and utilities
designed to run in parallel on Spark cluster
51
52. Introduces a few new data types, e.g.
vector (dense and sparse), labeled point,
rating, etc.
Allows to invoke various algorithms on
distributed datasets (RDD/Dataset)
http://spark.apache.org/docs/latest/mllib-guide.html
52
54. Utilities: linear algebra, statistics, etc.
Features extraction, features transforming, etc.
Regression
Classification
Clustering
Collaborative filtering, e.g. alternating least squares
Dimensionality reduction
And many more
http://spark.apache.org/docs/latest/mllib-guide.html
54
55. ”All” spark.mllib features plus:
• Pipelines
• Persistence
• Model selection and tuning:
• Train validation split
• K-folds cross validation
http://spark.apache.org/docs/latest/ml-guide.html
55
59. I'm a rolling thunder, a pouring rain
I'm comin' on like a hurricane
My lightning's flashing across the sky
You're only young but you're gonna die
I won't take no prisoners, won't spare no lives
Nobody's putting up a fight
I got my bell, I'm gonna take you to hell
I'm gonna get you, Satan get you
https://github.com/tmatyashovsky/spark-ml-samples
59
61. I'm a rolling thunder, a pouring rain
I'm comin' on like a hurricane
My lightning's flashing across the sky
You're only young but you're gonna die
I won't take no prisoners, won't spare no lives
Nobody's putting up a fight
I got my bell, I'm gonna take you to hell
I'm gonna get you, Satan get you
https://github.com/tmatyashovsky/spark-ml-samples
61
63. Im a rolling thunder a pouring rain
Im comin on like a hurricane
My lightnings flashing across the sky
Youre only young but youre gonna die
I wont take no prisoners wont spare no lives
Nobodys putting up a fight
I got my bell Im gonna take you to hell
Im gonna get you Satan get you
https://github.com/tmatyashovsky/spark-ml-samples
63
1
2
3
4
5
6
7
8
65. im a rolling thunder a pouring rain
im comin on like a hurricane
My lightnings flashing across the sky
youre only young but youre gonna die
I wont take no prisoners wont spare no lives
nobodys putting up a fight
I got my bell im gonna take you to hell
im gonna get you satan get you
https://github.com/tmatyashovsky/spark-ml-samples
65
1
2
3
4
5
6
7
8
67. im rolling thunder pouring rain
im comin like hurricane
lightnings flashing across sky
youre young youre gonna die
wont take prisoners wont spare lives
nobodiys putting fight
got bell im gonna take hell
im gonna get satan get
https://github.com/tmatyashovsky/spark-ml-samples
67
1
2
3
4
5
6
7
8
69. 4
im roll thunder pour rain
im comin like hurrican
lightn flash across sky
your young your gonna die
wont take prison wont spare live
nobodi put fight
got bell im gonna take hell
im gonna get satan get
https://github.com/tmatyashovsky/spark-ml-samples
69
1
2
3
4
5
6
7
8
verse1
verse2
70. 8
im roll thunder pour rain
im comin like hurrican
Light n flash across sky
your young your gonna die
wont take prison wont spare live
nobodi put fight
got bell im gonna take hell
im gonna get satan get
https://github.com/tmatyashovsky/spark-ml-samples
70
1
2
3
4
5
6
7
8
verse1
81. • Other feature extractors:
• Term Frequency – Inverse Document
Frequency (TD-IDF), Token counts (TF), etc.
• Other classification algorithms:
• Naive Bayes, Random Forest, Support Vector
Machines (SVM), etc.
http://spark.apache.org/docs/latest/ml-guide.html
81
93. 93
ML is not as complex as it seems from an applied
perspective
Existing libraries and frameworks reduce a lot of
tedious work
For instance, Spark MLlib can help to build nice ML
pipelines
Quantity of jokes used. Liked or not liked the speaker.
Bag of words – a single word is a one hot encoding vector with the size of the dictionary. As a result – a lot of sparse vectors.
Behind the scenes - a two-layer neural net that processes text.
Captures semantic and morphologic similarity so similar words are close in the vector space
Similar words would be clustered together in the high dimensional sphere.
If two words are very close to synonymous, you’d expect them to show up in similar contexts, and indeed synonymous words tend to be close.
For two completely random words, the similarity is pretty close to 0.
On an opposite side there is not an antonym, but usually just a noise.
Used Google News Negative 300.
My corpus - 8316 words
Let’s finally go to the implementation using a library or framework that is going to help us to avoid tedious transformations and provide algorithms as well as feature extractors out-of-the-box.