The slides I was using when delivering a meetup about the matplotlib library. More info about that meetup can be found at https://www.meetup.com/life-michael/events/271738271/
This Edureka Python Matplotlib tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what is data visualization and how to perform data visualization using Matplotlib. It also explains how to modify your plot and how to plot various types of graphs. Below are the topics covered in this tutorial:
1. Why Data Visualization?
2. What Is Data Visualization?
3. Various Types Of Plots
4. What Is Matplotlib?
6. How To Use Matplotlib?
Visualization and Matplotlib using Python.pptxSharmilaMore5
This document provides an overview of Matplotlib, a Python data visualization library. It discusses Matplotlib's pyplot and OO APIs, how to install Matplotlib, create basic plots using functions like plot(), and customize plots using markers and line styles. It also covers displaying plots, the Matplotlib user interface, Matplotlib's relationships with NumPy and Pandas, and examples of different types of graphs and charts like line plots that can be created with Matplotlib.
This document introduces the Seaborn library for statistical data visualization in Python. It discusses how Seaborn builds on Matplotlib and Pandas to provide higher-level visualization functions. Specifically, it covers using distplot to create histograms and kernel density estimates, regplot for scatter plots and regression lines, and lmplot for faceted scatter plot grids. Examples are provided to illustrate customizing distplot, combining different plot elements, and using faceting controls in lmplot.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
This document discusses Python libraries and modules. It defines a library as a collection of modules that provide specific functionality. The standard library contains commonly used modules like math and random. Other important libraries mentioned are NumPy, SciPy, and tkinter. A module is a .py file that contains related variables, classes, functions etc. Modules can be imported using import, from, or from * statements. Namespaces and module aliasing are also covered. The document concludes by explaining how to create Python packages and the role of the __init__.py file in making a directory a package.
NumPy is a Python library that provides multidimensional array and matrix objects to perform scientific computing. It contains efficient functions for operations on arrays like arithmetic, aggregation, copying, indexing, slicing, and reshaping. NumPy arrays have advantages over native Python sequences like fixed size and efficient mathematical operations. Common NumPy operations include elementwise arithmetic, aggregation functions, copying and transposing arrays, changing array shapes, and indexing/slicing arrays.
This document contains a presentation by Abhijeet Anand on NumPy. It introduces NumPy as a Python library for working with arrays, which aims to provide array objects that are faster than traditional Python lists. NumPy arrays benefit from being stored continuously in memory, unlike lists. The presentation covers 1D, 2D and 3D arrays in NumPy and basic array properties and operations like shape, size, dtype, copying, sorting, addition, subtraction and more.
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
Machine learning algorithms can adapt and learn from experience. The three main machine learning methods are supervised learning (using labeled training data), unsupervised learning (using unlabeled data), and semi-supervised learning (using some labeled and some unlabeled data). Supervised learning includes classification and regression tasks, while unsupervised learning includes cluster analysis.
NumPy is a Python library used for working with multi-dimensional arrays and matrices for scientific computing. It allows fast operations on large data sets and arrays. NumPy arrays can be created from lists or ranges of values and support element-wise operations via universal functions. NumPy is the foundation of the Python scientific computing stack and provides key features like broadcasting for efficient computations.
PYTHON-Chapter 4-Plotting and Data Science PyLab - MAULIK BORSANIYAMaulik Borsaniya
This document discusses data visualization and Matplotlib. It begins with an introduction to data visualization and its importance. It then covers basic visualization rules like labeling axes and adding titles. It discusses what Matplotlib is and how to install it. It provides examples of common plot types in Matplotlib like sine waves, scatter plots, bar charts, and pie charts. It also discusses working with data science and Pandas, including how to create Pandas Series and DataFrames from various data sources.
This document discusses using the Seaborn library in Python for data visualization. It covers installing Seaborn, importing libraries, reading in data, cleaning data, and creating various plots including distribution plots, heatmaps, pair plots, and more. Code examples are provided to demonstrate Seaborn's functionality for visualizing and exploring data.
The document discusses files in Python. It defines a file as an object that stores data, information, settings or commands used with a computer program. There are two main types of files - text files which store data as strings, and binary files which store data as bytes. The document outlines how to open, read, write, append, close and manipulate files in Python using functions like open(), read(), write(), close() etc. It also discusses pickling and unpickling objects to binary files for serialization. Finally, it covers working with directories and running other programs from Python.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
This presentation educates you about Python - GUI Programming(Tkinter), Tkinter Programming with syntaxe example, Tkinter Widgets with Operator & Description, Standard attributes.
For more topics stay tuned with learnbay.
The Agenda for the Webinar:
1. Introduction to Python.
2. Python and Big Data.
3. Python and Data Science.
4. Key features of Python and their usage in Business Analytics.
5. Business Analytics with Python – Real world Use Cases.
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
The document discusses different Python data types including lists, tuples, and dictionaries. It provides information on how to create, access, modify, and delete items from each data type. For lists, it covers indexing, slicing, and common list methods. For tuples, it discusses creation, concatenation, slicing, and built-in methods. For dictionaries, it explains how they are created as a collection of unique keys and values, and how to access, add, remove, and delete key-value pairs.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
Machine learning and its applications was submitted by Bhuvan Chopra to Er. Seema Rani. The document provides an introduction to machine learning, the basic prerequisites for machine learning including algebra, linear algebra, statistics and Python programming. It describes the main types of machine learning including supervised learning, unsupervised learning and reinforcement learning. Finally, it discusses some common applications of machine learning such as virtual personal assistants, video surveillance, social media services, email spam filtering, online customer support, product recommendations, and online fraud detection.
This document provides an overview of data visualization in Python. It discusses popular Python libraries and modules for visualization like Matplotlib, Seaborn, Pandas, NumPy, Plotly, and Bokeh. It also covers different types of visualization plots like bar charts, line graphs, pie charts, scatter plots, histograms and how to create them in Python using the mentioned libraries. The document is divided into sections on visualization libraries, version overview of updates to plots, and examples of various plot types created in Python.
The tutorial will introduce you to Python Packages. This Python basic tutorial will help you understand creating a Python package. You will understand the example of a Python Package. After that, you will understand different ways to access Python Packages. Further, the demonstration will educate you on how to create Python Package.
NumPy provides two fundamental objects for multi-dimensional arrays: the N-dimensional array object (ndarray) and the universal function object (ufunc). An ndarray is a homogeneous collection of items indexed using N integers. The shape and data type define an ndarray. NumPy arrays have a dtype attribute that returns the data type layout. Arrays can be created using the array() function and have various dimensions like 0D, 1D, 2D and 3D.
Introduction to IPython & Jupyter NotebooksEueung Mulyana
The document discusses IPython and the Jupyter Notebook. IPython is an interactive shell for Python that provides features like command history, tab completion, object introspection, and support for parallel computing. It has three main components: an enhanced interactive Python shell, a two-process communication model that allows clients to connect to a computation kernel, and architecture for interactive parallel computing. The Jupyter Notebook provides a browser-based notebook interface that allows code, text, plots and other media to be combined. IPython QtConsole provides a graphical interface for IPython with features like inline figures and multiline editing.
In this power point presentation i have explained about Seaborn Library in Data Visualization.
I have touched the topics like Introduction, what is Seaborn types etc.
Hope this ppt will help you & you will like it.
Thank You
All the best
Topic modeling is a technique for discovering hidden semantic patterns in large document collections. It represents documents as probability distributions over latent topics, where each topic is characterized by a distribution over words. Two common probabilistic topic models are latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (pLSA). LDA assumes each document exhibits multiple topics in different proportions, with topics modeled as distributions over words. Topic modeling provides dimensionality reduction and can be applied to problems like text classification, collaborative filtering, and computer vision tasks like image classification.
A Python dictionary is an unordered collection of key-value pairs where keys must be unique and immutable. It allows fast lookup of values using keys. Dictionaries can be created using curly braces and keys are used to access values using indexing or the get() method. Dictionaries are mutable and support various methods like clear(), copy(), pop(), update() etc to modify or retrieve data.
Introduction to Pylab and Matploitlib. yazad dumasia
This document provides an introduction and overview of the Pylab module in Python. It discusses how Pylab is embedded in Matplotlib and provides a MATLAB-like experience for plotting and visualization. The document then provides examples of basic plotting libraries that can be used with Matplotlib like NumPy. It also demonstrates how to install Matplotlib on different operating systems like Windows, Ubuntu Linux, and CentOS Linux. Finally, it showcases various basic plot types like line plots, scatter plots, histograms, pie charts, and subplots with code examples.
This document discusses how to create line charts, bar charts, pie charts, histograms, and scatter plots using Matplotlib in Python. It covers how to import Matplotlib, customize line styles, colors, markers, legends, titles and labels. It provides code examples for plotting single and multiple lines, formatting plots, saving figures, and using different chart types like pie charts, bar charts and histograms.
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
Machine learning algorithms can adapt and learn from experience. The three main machine learning methods are supervised learning (using labeled training data), unsupervised learning (using unlabeled data), and semi-supervised learning (using some labeled and some unlabeled data). Supervised learning includes classification and regression tasks, while unsupervised learning includes cluster analysis.
NumPy is a Python library used for working with multi-dimensional arrays and matrices for scientific computing. It allows fast operations on large data sets and arrays. NumPy arrays can be created from lists or ranges of values and support element-wise operations via universal functions. NumPy is the foundation of the Python scientific computing stack and provides key features like broadcasting for efficient computations.
PYTHON-Chapter 4-Plotting and Data Science PyLab - MAULIK BORSANIYAMaulik Borsaniya
This document discusses data visualization and Matplotlib. It begins with an introduction to data visualization and its importance. It then covers basic visualization rules like labeling axes and adding titles. It discusses what Matplotlib is and how to install it. It provides examples of common plot types in Matplotlib like sine waves, scatter plots, bar charts, and pie charts. It also discusses working with data science and Pandas, including how to create Pandas Series and DataFrames from various data sources.
This document discusses using the Seaborn library in Python for data visualization. It covers installing Seaborn, importing libraries, reading in data, cleaning data, and creating various plots including distribution plots, heatmaps, pair plots, and more. Code examples are provided to demonstrate Seaborn's functionality for visualizing and exploring data.
The document discusses files in Python. It defines a file as an object that stores data, information, settings or commands used with a computer program. There are two main types of files - text files which store data as strings, and binary files which store data as bytes. The document outlines how to open, read, write, append, close and manipulate files in Python using functions like open(), read(), write(), close() etc. It also discusses pickling and unpickling objects to binary files for serialization. Finally, it covers working with directories and running other programs from Python.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
This presentation educates you about Python - GUI Programming(Tkinter), Tkinter Programming with syntaxe example, Tkinter Widgets with Operator & Description, Standard attributes.
For more topics stay tuned with learnbay.
The Agenda for the Webinar:
1. Introduction to Python.
2. Python and Big Data.
3. Python and Data Science.
4. Key features of Python and their usage in Business Analytics.
5. Business Analytics with Python – Real world Use Cases.
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
The document discusses different Python data types including lists, tuples, and dictionaries. It provides information on how to create, access, modify, and delete items from each data type. For lists, it covers indexing, slicing, and common list methods. For tuples, it discusses creation, concatenation, slicing, and built-in methods. For dictionaries, it explains how they are created as a collection of unique keys and values, and how to access, add, remove, and delete key-value pairs.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
Machine learning and its applications was submitted by Bhuvan Chopra to Er. Seema Rani. The document provides an introduction to machine learning, the basic prerequisites for machine learning including algebra, linear algebra, statistics and Python programming. It describes the main types of machine learning including supervised learning, unsupervised learning and reinforcement learning. Finally, it discusses some common applications of machine learning such as virtual personal assistants, video surveillance, social media services, email spam filtering, online customer support, product recommendations, and online fraud detection.
This document provides an overview of data visualization in Python. It discusses popular Python libraries and modules for visualization like Matplotlib, Seaborn, Pandas, NumPy, Plotly, and Bokeh. It also covers different types of visualization plots like bar charts, line graphs, pie charts, scatter plots, histograms and how to create them in Python using the mentioned libraries. The document is divided into sections on visualization libraries, version overview of updates to plots, and examples of various plot types created in Python.
The tutorial will introduce you to Python Packages. This Python basic tutorial will help you understand creating a Python package. You will understand the example of a Python Package. After that, you will understand different ways to access Python Packages. Further, the demonstration will educate you on how to create Python Package.
NumPy provides two fundamental objects for multi-dimensional arrays: the N-dimensional array object (ndarray) and the universal function object (ufunc). An ndarray is a homogeneous collection of items indexed using N integers. The shape and data type define an ndarray. NumPy arrays have a dtype attribute that returns the data type layout. Arrays can be created using the array() function and have various dimensions like 0D, 1D, 2D and 3D.
Introduction to IPython & Jupyter NotebooksEueung Mulyana
The document discusses IPython and the Jupyter Notebook. IPython is an interactive shell for Python that provides features like command history, tab completion, object introspection, and support for parallel computing. It has three main components: an enhanced interactive Python shell, a two-process communication model that allows clients to connect to a computation kernel, and architecture for interactive parallel computing. The Jupyter Notebook provides a browser-based notebook interface that allows code, text, plots and other media to be combined. IPython QtConsole provides a graphical interface for IPython with features like inline figures and multiline editing.
In this power point presentation i have explained about Seaborn Library in Data Visualization.
I have touched the topics like Introduction, what is Seaborn types etc.
Hope this ppt will help you & you will like it.
Thank You
All the best
Topic modeling is a technique for discovering hidden semantic patterns in large document collections. It represents documents as probability distributions over latent topics, where each topic is characterized by a distribution over words. Two common probabilistic topic models are latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (pLSA). LDA assumes each document exhibits multiple topics in different proportions, with topics modeled as distributions over words. Topic modeling provides dimensionality reduction and can be applied to problems like text classification, collaborative filtering, and computer vision tasks like image classification.
A Python dictionary is an unordered collection of key-value pairs where keys must be unique and immutable. It allows fast lookup of values using keys. Dictionaries can be created using curly braces and keys are used to access values using indexing or the get() method. Dictionaries are mutable and support various methods like clear(), copy(), pop(), update() etc to modify or retrieve data.
Introduction to Pylab and Matploitlib. yazad dumasia
This document provides an introduction and overview of the Pylab module in Python. It discusses how Pylab is embedded in Matplotlib and provides a MATLAB-like experience for plotting and visualization. The document then provides examples of basic plotting libraries that can be used with Matplotlib like NumPy. It also demonstrates how to install Matplotlib on different operating systems like Windows, Ubuntu Linux, and CentOS Linux. Finally, it showcases various basic plot types like line plots, scatter plots, histograms, pie charts, and subplots with code examples.
This document discusses how to create line charts, bar charts, pie charts, histograms, and scatter plots using Matplotlib in Python. It covers how to import Matplotlib, customize line styles, colors, markers, legends, titles and labels. It provides code examples for plotting single and multiple lines, formatting plots, saving figures, and using different chart types like pie charts, bar charts and histograms.
Making informative visualizations is called as Plots, important task in data analysis
In exploratory analysis – identifying outliers, data transformations ,generating models visualization can be used.
Matplotlib is a desktop plotting package designed for creating publication-quality plots.
Project started by John Hunter to enable MATLAB like plotting interface in python.
Making informative visualizations is called as Plots, important task in data analysis
In exploratory analysis – identifying outliers, data transformations ,generating models visualization can be used.
Matplotlib is a desktop plotting package designed for creating publication-quality plots.
Project started by John Hunter to enable MATLAB like plotting interface in python.
Making informative visualizations is called as Plots, important task in data analysis
In exploratory analysis – identifying outliers, data transformations ,generating models visualization can be used.
Matplotlib is a desktop plotting package designed for creating publication-quality plots.
Project started by John Hunter to enable MATLAB like plotting interface in python.
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matplotlib-installatin-interactive-contour-example-guideArulalan T
This document provides instructions for installing Matplotlib and examples of interactive contour plotting in 3D using Matplotlib. It describes downloading and installing dependencies like NumPy, libpng, and freetype. It then explains downloading and installing Matplotlib. Two examples are given of interactive contour plotting where the contour levels can be changed: one takes input at the command line, the other reads levels from a file. The output demonstrates changing the contour levels in the 3D plot to see how it is updated.
Use the Matplotlib, Luke @ PyCon Taiwan 2012Wen-Wei Liao
Matplotlib is a Python 2D plotting library that produces publication-quality figures in both hardcopy and interactive environments across platforms. It provides both object-oriented and MATLAB-style state machine interfaces. Matplotlib can be used to create simple plots with just a few commands or customize plots extensively by manipulating the object properties of figures, axes, and artists.
Matplotlib is a popular Python library used for data visualization and making 2D plots from data. It provides an object-oriented API that allows plots to be embedded in Python applications. Matplotlib has a MATLAB-like procedural interface called Pylab and can be considered an open source alternative to MATLAB. It is written in Python and relies on NumPy for numerical computations. Examples shown how to generate simple plots by importing Matplotlib and NumPy, preparing data, and using Matplotlib functions to plot and display the results.
This document provides an introduction and overview of Matplotlib, a Python data visualization library. It discusses what Matplotlib is, how to use it in both Jupyter notebooks and the Python shell, and provides several examples of basic plotting techniques like sine waves, scatter plots, histograms, and more. It also demonstrates how to customize plots by changing colors, labels, and other display properties.
This document discusses data visualization using Matplotlib and Pyplot in Python. It provides an overview of data visualization, describes how to import and use Pyplot for creating basic line and scatter plots, and demonstrates how to customize plots by changing line colors, styles, markers, and labels.
This is the presentation I was using when delivering the meetup about the NumPy library. More info about that meetup can be found at https://www.meetup.com/life-michael/events/271732862/
The Visitor Classic Design Pattern [Free Meetup]Haim Michael
These slides were prepared for the online meetup that took place on Tuesday, June 3, 2025. During this meetup overviewed a new approach for implementing the visitor design pattern using pattern matching, sealed classes, and record classes.
You can find the code at https://github.com/lifemichael/design.patterns.visitor
You can find the video of this meetup at https://youtu.be/c9wMyJiN0os
You can find more information about this meetup at https://www.meetup.com/lifemichael/events/306824058/
Typing in Python: Bringing Clarity, Safety and Speed to Your Code [Free Meetup]Haim Michael
These slides were use for delivering the talk at the meetup about Types in Python. More information about that meetup at https://www.meetup.com/lifemichael/events/304738344. You can find the video of that meetup at https://youtu.be/n7HrOYAol8M.
Introduction to Pattern Matching in Java [Free Meetup]Haim Michael
This presentation was prepared for a meetup that focused on Pattern Matching in Java. You can find more information about that meetup at https://www.meetup.com/lifemichael/events/302670923. You can find the video of that meetup at https://youtu.be/ITNi1On_KI8
Join our Java Monthly newsletter at https://www.linkedin.com/newsletters/java-monthly-review-7196786144515100674/
More information about our professional training services for software developers can be found at https://lifemichael.com.
Mastering The Collections in JavaScript [Free Meetup]Haim Michael
This is the slides I was using when delivering my talk at the JavaScript Collections meetup, that took place on February 4th. More info about that meetup can be found at https://www.meetup.com/lifemichael/events/304737983. You can find the video at https://youtu.be/ZsguwdfqFtc
Beyond Java - Evolving to Scala and KotlinHaim Michael
These are the slides that I was using when delivering the meetup 'Beyond Java: Evolving to Scala and Kotlin'. More information about this meetup can be found at https://www.meetup.com/lifemichael/events/304737713. You can find the video at https://youtu.be/DxYBTOnNUDI.
Stay tuned with the development of the Java programming language by subscribing to the Java Monthly Review at https://www.linkedin.com/newsletters/java-monthly-review-7196786144515100674/
Join the 'Scala Developers' group on Facebook at https://www.facebook.com/groups/203788593023488
Join the 'Java Developers' group on Facebook at https://www.facebook.com/groups/416382100240052
Join the 'Kotlin Developers' group on Facebook at https://www.facebook.com/groups/1977843402436668
JavaScript Promises Simplified [Free Meetup]Haim Michael
This is the presentation that I was using when delivering the meetup 'JavaScript Promise Simplified'. This presentation focuses on the use of the Promise constructor function. It is highly recommended for every JavaScript developer who wants to clarify his/her understanding of the async and the await keywords, and of the Promise constructor function.
More info about that meetup at https://www.meetup.com/lifemichael/events/302135153/.
You can find the video that was captured at https://youtu.be/DrQBhT-b0I8
Join our JavaScript Monthly Review newsletter for free at https://www.linkedin.com/newsletters/javascript-monthly-review-7207562914604494848/
Join our JavaScript Developers' professional group on Facebook at https://www.facebook.com/groups/407961892610345
Join our online international conference for JavaScript at https://xtremejs.dev
Scala Jump Start [Free Online Meetup in English]Haim Michael
This is the presentation that was in use when delivering the Scala Jump Start free meetup, described at https://www.meetup.com/lifemichael/events/294781025. You can find the video at https://youtu.be/eEE9zDwPMbw
The MVVM Architecture in Java [Free Meetup]Haim Michael
You can find the code that was coded (live) during the meetup at
https://github.com/lifemichael/java-mvvm
You can find the video of the two parts on YouTube at https://youtu.be/ri-gKGsXWcc
More information about the two meetups these slides refer to can be found at
July 2nd, 2024:
https://www.meetup.com/lifemichael/events/295751855/
July 9th, 2024:
https://www.meetup.com/lifemichael/events/301993871/
Kotlin Jump Start Online Free Meetup (June 4th, 2024)Haim Michael
These are the slides that I used when delivering the Kotlin Jump Start online meetup on June 4th, 2024.
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This is the presentation I was using when delivering the meetup about Anti Patterns.
We at life michael continuously develop professional seminars. High-tech companies can invite us to deliver these seminars to their employees. You can find more information about what we do at https://lifemichael.com.
You can find more information about that meetup at https://www.meetup.com/lifemichael/events/293712620/.
You can find the video that was captured at https://youtu.be/xbBUC2Wyhs8
You can join our meetup group (for free) at https://meetup.com/lifemichael
This is the slides I was using when delivering the meetup about Virtual Threads in Java. It took place in July, 2023. You can find the video at https://youtu.be/Ja8bc6YpF2g.
More information about our company premium professional software development, consulting, and training services can be found at https://lifemichael.com
These are the slides I was using when delivering the meetup described at https://www.meetup.com/lifemichael/events/292574718/ You can find the video of this meetup at https://youtu.be/qT9NmgPU1j8
I was using this presentation when delivering our meetup about SQL Injections.
You can find the video of that event at https://youtu.be/akpe3vKFeoc
More information about our professional services (including training and consulting) can be found at https://lifemichael.com
This is the presentation that was prepared for our meetup about Record Classes in Java. You can find more information about that meetup at https://www.meetup.com/lifemichael/events/288771190/ You can find the video that was captured at https://youtu.be/LN4-NuNvrvQ You can find more information about our courses and seminars at https://lifemichael.com
This is the video capture of the meetup described at https://www.meetup.com/lifemichael/events/287981390/ This video includes the two talks the meetup included. The first one is an introductory talk for the topic. The second one covers the SAGA design pattern.
This document provides an introduction and overview of structural pattern matching in Python. It discusses how pattern matching can be considered as switch statements on steroids, and demonstrates various pattern matching techniques including matching specific values, sequences, objects, attributes, enums, mappings, adding conditions, and more. Examples are provided throughout to illustrate each technique. The document is intended to help explain the capabilities and usage of Python's new pattern matching feature.
I used these slides when delivering a meetup about Unit Testing in Python. You can find the video at https://youtu.be/5QHArdkUeYc
This presentation was used during the 'OOP Best Practices in JavaScript' meetup that took place on April 11th, 2022. More information about this meetup group can be found at https://meetup.com/lifemichael
These slides were prepared for the Java Jump Start meetup I delivered on March 7th, 2022. More info about that meetup and others at https://www.meetup.com/lifemichael/events/278744096/
This is the presentation I was using when delivering the JavaScript Jump Start meetup on February 14th, 2022. More information about that meetup can be found at https://www.meetup.com/lifemichael/events/278743661/ You can find the video at https://youtu.be/F1e-KHTEKzo
Exploring the advantages of on-premises Dell PowerEdge servers with AMD EPYC processors vs. the cloud for small to medium businesses’ AI workloads
AI initiatives can bring tremendous value to your business, but you need to support your new AI workloads effectively. That means choosing the best possible infrastructure for your needs—and many companies are finding that the cloud isn’t right for them. According to a recent Rackspace survey of IT executives, 69 percent of companies have moved some of their applications on-premises from the cloud, with half of those citing security and compliance as the reason and 44 percent citing cost.
On-premises solutions provide a number of advantages. With full control over your security infrastructure, you can be certain that all compliance requirements remain firmly in the hands of your IT team. Opting for on-premises also gives you the ability to design your infrastructure to the precise needs of that team and your new AI workloads. Depending on the workload, you may also see performance benefits, along with more predictable costs. As you start to build your next AI initiative, consider an on-premises solution utilizing AMD EPYC processor-powered Dell PowerEdge servers.
Developing Schemas with FME and Excel - Peak of Data & AI 2025Safe Software
When working with other team members who may not know the Esri GIS platform or may not be database professionals; discussing schema development or changes can be difficult. I have been using Excel to help illustrate and discuss schema design/changes during meetings and it has proven a useful tool to help illustrate how a schema will be built. With just a few extra columns, that Excel file can be sent to FME to create new feature classes/tables. This presentation will go thru the steps needed to accomplish this task and provide some lessons learned and tips/tricks that I use to speed the process.
Boosting MySQL with Vector Search -THE VECTOR SEARCH CONFERENCE 2025 .pdfAlkin Tezuysal
As the demand for vector databases and Generative AI continues to rise, integrating vector storage and search capabilities into traditional databases has become increasingly important. This session introduces the *MyVector Plugin*, a project that brings native vector storage and similarity search to MySQL. Unlike PostgreSQL, which offers interfaces for adding new data types and index methods, MySQL lacks such extensibility. However, by utilizing MySQL's server component plugin and UDF, the *MyVector Plugin* successfully adds a fully functional vector search feature within the existing MySQL + InnoDB infrastructure, eliminating the need for a separate vector database. The session explains the technical aspects of integrating vector support into MySQL, the challenges posed by its architecture, and real-world use cases that showcase the advantages of combining vector search with MySQL's robust features. Attendees will leave with practical insights on how to add vector search capabilities to their MySQL systems.
Soulmaite review - Find Real AI soulmate reviewSoulmaite
Looking for an honest take on Soulmaite? This Soulmaite review covers everything you need to know—from features and pricing to how well it performs as a real AI soulmate. We share how users interact with adult chat features, AI girlfriend 18+ options, and nude AI chat experiences. Whether you're curious about AI roleplay porn or free AI NSFW chat with no sign-up, this review breaks it down clearly and informatively.
Evaluation Challenges in Using Generative AI for Science & Technical ContentPaul Groth
Evaluation Challenges in Using Generative AI for Science & Technical Content.
Foundation Models show impressive results in a wide-range of tasks on scientific and legal content from information extraction to question answering and even literature synthesis. However, standard evaluation approaches (e.g. comparing to ground truth) often don't seem to work. Qualitatively the results look great but quantitive scores do not align with these observations. In this talk, I discuss the challenges we've face in our lab in evaluation. I then outline potential routes forward.
Domino IQ – Was Sie erwartet, erste Schritte und Anwendungsfällepanagenda
Webinar Recording: https://www.panagenda.com/webinars/domino-iq-was-sie-erwartet-erste-schritte-und-anwendungsfalle/
HCL Domino iQ Server – Vom Ideenportal zur implementierten Funktion. Entdecken Sie, was es ist, was es nicht ist, und erkunden Sie die Chancen und Herausforderungen, die es bietet.
Wichtige Erkenntnisse
- Was sind Large Language Models (LLMs) und wie stehen sie im Zusammenhang mit Domino iQ
- Wesentliche Voraussetzungen für die Bereitstellung des Domino iQ Servers
- Schritt-für-Schritt-Anleitung zur Einrichtung Ihres Domino iQ Servers
- Teilen und diskutieren Sie Gedanken und Ideen, um das Potenzial von Domino iQ zu maximieren
AI Agents in Logistics and Supply Chain Applications Benefits and ImplementationChristine Shepherd
AI agents are reshaping logistics and supply chain operations by enabling automation, predictive insights, and real-time decision-making across key functions such as demand forecasting, inventory management, procurement, transportation, and warehouse operations. Powered by technologies like machine learning, NLP, computer vision, and robotic process automation, these agents deliver significant benefits including cost reduction, improved efficiency, greater visibility, and enhanced adaptability to market changes. While practical use cases show measurable gains in areas like dynamic routing and real-time inventory tracking, successful implementation requires careful integration with existing systems, quality data, and strategic scaling. Despite challenges such as data integration and change management, AI agents offer a strong competitive edge, with widespread industry adoption expected by 2025.
DevOps in the Modern Era - Thoughtfully Critical PodcastChris Wahl
https://youtu.be/735hP_01WV0
My journey through the world of DevOps! From the early days of breaking down silos between developers and operations to the current complexities of cloud-native environments. I'll talk about my personal experiences, the challenges we faced, and how the role of a DevOps engineer has evolved.
6th Power Grid Model Meetup
Join the Power Grid Model community for an exciting day of sharing experiences, learning from each other, planning, and collaborating.
This hybrid in-person/online event will include a full day agenda, with the opportunity to socialize afterwards for in-person attendees.
If you have a hackathon proposal, tell us when you register!
About Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/state-space-models-vs-transformers-for-ultra-low-power-edge-ai-a-presentation-from-brainchip/
Tony Lewis, Chief Technology Officer at BrainChip, presents the “State-space Models vs. Transformers for Ultra-low-power Edge AI” tutorial at the May 2025 Embedded Vision Summit.
At the embedded edge, choices of language model architectures have profound implications on the ability to meet demanding performance, latency and energy efficiency requirements. In this presentation, Lewis contrasts state-space models (SSMs) with transformers for use in this constrained regime. While transformers rely on a read-write key-value cache, SSMs can be constructed as read-only architectures, enabling the use of novel memory types and reducing power consumption. Furthermore, SSMs require significantly fewer multiply-accumulate units—drastically reducing compute energy and chip area.
New techniques enable distillation-based migration from transformer models such as Llama to SSMs without major performance loss. In latency-sensitive applications, techniques such as precomputing input sequences allow SSMs to achieve sub-100 ms time-to-first-token, enabling real-time interactivity. Lewis presents a detailed side-by-side comparison of these architectures, outlining their trade-offs and opportunities at the extreme edge.
What is Oracle EPM A Guide to Oracle EPM Cloud Everything You Need to KnowSMACT Works
In today's fast-paced business landscape, financial planning and performance management demand powerful tools that deliver accurate insights. Oracle EPM (Enterprise Performance Management) stands as a leading solution for organizations seeking to transform their financial processes. This comprehensive guide explores what Oracle EPM is, its key benefits, and how partnering with the right Oracle EPM consulting team can maximize your investment.
Create Your First AI Agent with UiPath Agent BuilderDianaGray10
Join us for an exciting virtual event where you'll learn how to create your first AI Agent using UiPath Agent Builder. This session will cover everything you need to know about what an agent is and how easy it is to create one using the powerful AI-driven UiPath platform. You'll also discover the steps to successfully publish your AI agent. This is a wonderful opportunity for beginners and enthusiasts to gain hands-on insights and kickstart their journey in AI-powered automation.
Neural representations have shown the potential to accelerate ray casting in a conventional ray-tracing-based rendering pipeline. We introduce a novel approach called Locally-Subdivided Neural Intersection Function (LSNIF) that replaces bottom-level BVHs used as traditional geometric representations with a neural network. Our method introduces a sparse hash grid encoding scheme incorporating geometry voxelization, a scene-agnostic training data collection, and a tailored loss function. It enables the network to output not only visibility but also hit-point information and material indices. LSNIF can be trained offline for a single object, allowing us to use LSNIF as a replacement for its corresponding BVH. With these designs, the network can handle hit-point queries from any arbitrary viewpoint, supporting all types of rays in the rendering pipeline. We demonstrate that LSNIF can render a variety of scenes, including real-world scenes designed for other path tracers, while achieving a memory footprint reduction of up to 106.2x compared to a compressed BVH.
https://arxiv.org/abs/2504.21627
Securiport is a border security systems provider with a progressive team approach to its task. The company acknowledges the importance of specialized skills in creating the latest in innovative security tech. The company has offices throughout the world to serve clients, and its employees speak more than twenty languages at the Washington D.C. headquarters alone.
How Advanced Environmental Detection Is Revolutionizing Oil & Gas Safety.pdfRejig Digital
Unlock the future of oil & gas safety with advanced environmental detection technologies that transform hazard monitoring and risk management. This presentation explores cutting-edge innovations that enhance workplace safety, protect critical assets, and ensure regulatory compliance in high-risk environments.
🔍 What You’ll Learn:
✅ How advanced sensors detect environmental threats in real-time for proactive hazard prevention
🔧 Integration of IoT and AI to enable rapid response and minimize incident impact
📡 Enhancing workforce protection through continuous monitoring and data-driven safety protocols
💡 Case studies highlighting successful deployment of environmental detection systems in oil & gas operations
Ideal for safety managers, operations leaders, and technology innovators in the oil & gas industry, this presentation offers practical insights and strategies to revolutionize safety standards and boost operational resilience.
👉 Learn more: https://www.rejigdigital.com/blog/continuous-monitoring-prevent-blowouts-well-control-issues/
Trends Artificial Intelligence - Mary MeekerClive Dickens
Mary Meeker’s 2024 AI report highlights a seismic shift in productivity, creativity, and business value driven by generative AI. She charts the rapid adoption of tools like ChatGPT and Midjourney, likening today’s moment to the dawn of the internet. The report emphasizes AI’s impact on knowledge work, software development, and personalized services—while also cautioning about data quality, ethical use, and the human-AI partnership. In short, Meeker sees AI as a transformative force accelerating innovation and redefining how we live and work.
Trends Artificial Intelligence - Mary MeekerClive Dickens
The matplotlib Library
1. The matplotlib Library
Haim Michael
October 14th
, 2020
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