Abstract: This PDSG workshop covers the basics of OOP programming in Python. Concepts covered are class, object, scope, method overloading and inheritance.
Level: Fundamental
Requirements: One should have some knowledge of programming.
This document introduces object-oriented programming concepts in Python, including classes, objects, encapsulation, inheritance, and polymorphism. It provides examples of defining a Rectangle class with width and height attributes and a method to calculate area. Instances of Rectangle are created to demonstrate setting and getting attribute values and calling methods. The concepts of inheritance and polymorphism are demonstrated by creating subclasses of Rectangle like Cuboid that inherit attributes and can override methods. Constructors and destructors in classes are also described.
This document provides an introduction to object oriented programming concepts in Python. It begins with examples of basic Python data types and loops. It then discusses why object oriented programming is useful, describing real world objects in code, reusability, and manageability. The key concepts of object oriented programming are defined, including classes, objects, attributes, and methods. An example taxi class is created to demonstrate these concepts, including defining attributes like driver name and cities, and methods like pick up and drop off passengers. The document discusses how to define a class, create objects from a class, and add methods to classes to define object behavior. It also introduces the concept of class variables that are shared among all objects versus object variables that are unique to
Basics of Object Oriented Programming in PythonSujith Kumar
The document discusses key concepts of object-oriented programming (OOP) including classes, objects, methods, encapsulation, inheritance, and polymorphism. It provides examples of classes in Python and explains OOP principles like defining classes with the class keyword, using self to reference object attributes and methods, and inheriting from base classes. The document also describes operator overloading in Python to allow operators to have different meanings based on the object types.
OOPS concepts are one of the most important concepts in high level languages. Here in this PPT we will learn more about Object oriented approach in python programming which includes details related to classes and objects, inheritance, dat abstraction, polymorphism and many more with examples and code.
This document discusses object-oriented programming concepts in Python including multiple inheritance, method resolution order, method overriding, and static and class methods. It provides examples of multiple inheritance where a class inherits from more than one parent class. It also explains method resolution order which determines the search order for methods and attributes in cases of multiple inheritance. The document demonstrates method overriding where a subclass redefines a method from its parent class. It describes static and class methods in Python, noting how static methods work on class data instead of instance data and can be called through both the class and instances, while class methods always receive the class as the first argument.
This document provides an overview of object-oriented programming (OOP) with Python. It defines some key OOP concepts like objects, classes, encapsulation, inheritance, and polymorphism. It explains that in OOP, data and functions that operate on that data are bundled together into objects. Classes provide templates for creating objects with common properties and functions. The document also gives examples of various OOP features like class variables, instance variables, methods, and constructors.
Here is a Python class with the specifications provided in the question:
class PICTURE:
def __init__(self, pno, category, location):
self.pno = pno
self.category = category
self.location = location
def FixLocation(self, new_location):
self.location = new_location
This defines a PICTURE class with three instance attributes - pno, category and location as specified in the question. It also defines a FixLocation method to assign a new location as required.
This document provides an introduction to object oriented programming in Python. It discusses key OOP concepts like classes, methods, encapsulation, abstraction, inheritance, polymorphism, and more. Each concept is explained in 1-2 paragraphs with examples provided in Python code snippets. The document is presented as a slideshow that is meant to be shared and provide instruction on OOP in Python.
This document discusses object-oriented programming concepts in Python, including inheritance and method overriding. It provides examples of using inheritance to create child classes like Taxi and Bus that inherit attributes and methods from a parent Vehicle class. It also demonstrates overriding the __init__ and __str__ methods in child classes to customize initialization and string representation while still calling the parent methods. The document includes exercises for readers to practice modeling inheritance relationships and method overriding.
This document provides an introduction to object oriented programming in Python. It discusses why OOP is useful, defines some key concepts like classes, objects, methods, and variables. It provides an example of modeling a Taxi using a Taxi class with attributes like driver name and methods like pickUpPassenger. It shows how to define a class, create objects from the class, and call methods on those objects. It also introduces the concept of class variables that are shared among all objects versus instance variables that are unique to each object.
This document provides an overview of object-oriented programming concepts in Python including objects, classes, inheritance, polymorphism, and encapsulation. It defines key terms like objects, classes, and methods. It explains how to create classes and objects in Python. It also discusses special methods, modules, and the __name__ variable.
PYTHON-Chapter 3-Classes and Object-oriented Programming: MAULIK BORSANIYAMaulik Borsaniya
Classes and Object-oriented Programming:
Classes: Creating a Class, The Self Variable, Constructor, Types of Variables, Namespaces, Types of Methods (Instance Methods, Class Methods, Static Methods), Passing Members of One Class to Another Class, Inner Classes
Inheritance and Polymorphism: Constructors in Inheritance, Overriding Super Class Constructors and Methods, The super() Method, Types of Inheritance, Single Inheritance, Multiple Inheritance, Method Resolution Order (MRO), Polymorphism, Duck Typing Philosophy of Python, Operator Overloading, Method Overloading, Method Overriding
Abstract Classes and Interfaces: Abstract Method and Abstract Class, Interfaces in Python, Abstract Classes vs. Interfaces,
Object oriented programming with pythonArslan Arshad
A short intro to how Object Oriented Paradigm work in Python Programming language. This presentation created for beginner like bachelor student of Computer Science.
This document discusses Python modules, classes, inheritance, and properties. Some key points:
- Modules allow the organization of Python code into reusable libraries by saving code in files with a .py extension. Modules can contain functions, variables, and be imported into other code.
- Classes are templates that define the properties and methods common to all objects of a certain kind. The __init__() method initializes new objects. Inheritance allows child classes to inherit properties and methods from parent classes.
- Properties provide a way to control access to class attributes, allowing them to be accessed like attributes while hiding the implementation details behind getter and setter methods.
Constructors and destructors in Python.
Constructors are special methods that are called automatically when an object is created. They initialize variables and ensure objects are properly initialized. There are two types of constructors: default and parameterized. Default don't take arguments, parameterized do.
Destructors are called when an object is destroyed. Defined using __del__(), they are useful for releasing resources like closing files before a program exits.
The document then provides code examples of classes with constructors, parameterized constructors, and destructors. It also discusses Python's garbage collection and how the collector deletes unneeded objects to free memory space.
This document provides an overview of basic object-oriented programming (OOP) concepts including objects, classes, inheritance, polymorphism, encapsulation, and data abstraction. It defines objects as entities with both data (characteristics) and behavior (operations). Classes are blueprints that are used to create objects. Inheritance allows objects to inherit properties from parent classes. Polymorphism allows code to take different forms. Encapsulation wraps data and functions into classes, hiding information. Data abstraction focuses on important descriptions without details.
The document provides an agenda and overview of key concepts in object-oriented programming with Java including:
1. Class syntax such as access modifiers, static members, constructors, initializers, and the 'this' keyword.
2. Inheritance concepts like subclasses, superclasses, overriding methods, and the 'super' keyword.
3. Interfaces as contracts that can be implemented by classes to define common behaviors without implementation.
4. Nested classes including static nested classes and inner classes, as well as anonymous classes defined without a class name.
5. Enums, constructors, and initializers are also covered but examples are not shown.
1. Inheritance is a mechanism where a new class is derived from an existing class, known as the base or parent class. The derived class inherits properties and methods from the parent class.
2. There are 5 types of inheritance: single, multilevel, multiple, hierarchical, and hybrid. Multiple inheritance allows a class to inherit from more than one parent class.
3. Overriding allows a subclass to replace or extend a method defined in the parent class, while still calling the parent method using the super() function or parent class name. This allows the subclass to provide a specific implementation of a method.
The document discusses classes and objects in object-oriented programming. It defines a class as a blueprint for objects that bind data and functions together. A class defines data members and member functions. Objects are instances of a class that can access class data and functions. The document provides examples of defining a class called "test" with private and public members, and creating objects of the class to demonstrate accessing members.
In this article we will learn classes and objects in C# programming.
Till now in the past two articles we have seen all the labs which was using functional programming. From now in coming all the articles we will do the programming using classes and objects. As this is professional approach of doing the programming. With classes and objects approach, code it reduces code reading complexity and it improves readability and also offers re-usability.
The document discusses object-oriented programming concepts in C#, including defining classes, constructors, properties, static members, interfaces, inheritance, and polymorphism. It provides examples of defining a simple Cat class with fields, a constructor, properties, and methods. It also demonstrates using the Dog class by creating dog objects, setting their properties, and calling their bark method.
The document discusses classes and objects in .NET. It defines classes as templates that define an object's properties and behaviors. Objects are instances of classes that have state defined by their property values. The document provides examples of declaring classes and objects in C#, and discusses accessing fields, properties, methods, and constructors of classes and objects. It also covers the differences between instance and static members of classes.
Object-oriented programming focuses on data. An object is a basic run-time entity. A class is also known as a user-defined data type. Inheritance provides the idea of reusability. Objects can communicate with each other through message passing. Polymorphism is achieved through operator overloading and function overloading.
The document discusses key concepts in C++ classes including encapsulation, information hiding, access specifiers, and constructors. It defines a class as a way to combine attributes and behaviors of real-world objects into a single unit. A class uses encapsulation to associate code and data, and information hiding to secure data from direct access. Access specifiers like public, private, and protected determine member visibility. Constructors are special member functions that initialize objects upon instantiation.
This document discusses file handling in C++. It introduces three classes - ofstream, ifstream, and fstream - that allow performing output and input of characters to and from files. The ofstream class is used to write to files, ifstream is used to read from files, and fstream can both read and write. The open() method is used to open a file, specifying the file name and optional file mode. Writing to a file uses the << operator and reading uses the >> operator. Operator overloading allows user-defined types like classes to define the meaning of operators like + when used on their objects.
Python Programming - VI. Classes and ObjectsRanel Padon
This document discusses classes and objects in Python programming. It covers key concepts like class attributes, instantiating classes to create objects, using constructors and destructors, composition where objects have other objects as attributes, and referencing objects. The document uses examples like a Time class to demonstrate class syntax and how to define attributes and behaviors for classes.
Chap 3 Python Object Oriented Programming - Copy.pptmuneshwarbisen1
The document discusses object oriented programming concepts in Python including classes, objects, instances, methods, inheritance, and class attributes. It provides examples of defining classes, instantiating objects, using methods, and the difference between class and instance attributes. Key concepts covered include defining classes with the class keyword, creating object instances, using the __init__() method for initialization, and allowing derived classes to inherit from base classes.
The document introduces Python modules and importing. It discusses three formats for importing modules: import somefile, from somefile import *, and from somefile import className. It describes commonly used Python modules like sys, os, and math. It also covers defining your own modules, directories for module files, object-oriented programming in Python including defining classes, creating and deleting instances, methods and self, accessing attributes and methods, attributes, inheritance, and redefining methods.
This document discusses object-oriented programming concepts in Python, including inheritance and method overriding. It provides examples of using inheritance to create child classes like Taxi and Bus that inherit attributes and methods from a parent Vehicle class. It also demonstrates overriding the __init__ and __str__ methods in child classes to customize initialization and string representation while still calling the parent methods. The document includes exercises for readers to practice modeling inheritance relationships and method overriding.
This document provides an introduction to object oriented programming in Python. It discusses why OOP is useful, defines some key concepts like classes, objects, methods, and variables. It provides an example of modeling a Taxi using a Taxi class with attributes like driver name and methods like pickUpPassenger. It shows how to define a class, create objects from the class, and call methods on those objects. It also introduces the concept of class variables that are shared among all objects versus instance variables that are unique to each object.
This document provides an overview of object-oriented programming concepts in Python including objects, classes, inheritance, polymorphism, and encapsulation. It defines key terms like objects, classes, and methods. It explains how to create classes and objects in Python. It also discusses special methods, modules, and the __name__ variable.
PYTHON-Chapter 3-Classes and Object-oriented Programming: MAULIK BORSANIYAMaulik Borsaniya
Classes and Object-oriented Programming:
Classes: Creating a Class, The Self Variable, Constructor, Types of Variables, Namespaces, Types of Methods (Instance Methods, Class Methods, Static Methods), Passing Members of One Class to Another Class, Inner Classes
Inheritance and Polymorphism: Constructors in Inheritance, Overriding Super Class Constructors and Methods, The super() Method, Types of Inheritance, Single Inheritance, Multiple Inheritance, Method Resolution Order (MRO), Polymorphism, Duck Typing Philosophy of Python, Operator Overloading, Method Overloading, Method Overriding
Abstract Classes and Interfaces: Abstract Method and Abstract Class, Interfaces in Python, Abstract Classes vs. Interfaces,
Object oriented programming with pythonArslan Arshad
A short intro to how Object Oriented Paradigm work in Python Programming language. This presentation created for beginner like bachelor student of Computer Science.
This document discusses Python modules, classes, inheritance, and properties. Some key points:
- Modules allow the organization of Python code into reusable libraries by saving code in files with a .py extension. Modules can contain functions, variables, and be imported into other code.
- Classes are templates that define the properties and methods common to all objects of a certain kind. The __init__() method initializes new objects. Inheritance allows child classes to inherit properties and methods from parent classes.
- Properties provide a way to control access to class attributes, allowing them to be accessed like attributes while hiding the implementation details behind getter and setter methods.
Constructors and destructors in Python.
Constructors are special methods that are called automatically when an object is created. They initialize variables and ensure objects are properly initialized. There are two types of constructors: default and parameterized. Default don't take arguments, parameterized do.
Destructors are called when an object is destroyed. Defined using __del__(), they are useful for releasing resources like closing files before a program exits.
The document then provides code examples of classes with constructors, parameterized constructors, and destructors. It also discusses Python's garbage collection and how the collector deletes unneeded objects to free memory space.
This document provides an overview of basic object-oriented programming (OOP) concepts including objects, classes, inheritance, polymorphism, encapsulation, and data abstraction. It defines objects as entities with both data (characteristics) and behavior (operations). Classes are blueprints that are used to create objects. Inheritance allows objects to inherit properties from parent classes. Polymorphism allows code to take different forms. Encapsulation wraps data and functions into classes, hiding information. Data abstraction focuses on important descriptions without details.
The document provides an agenda and overview of key concepts in object-oriented programming with Java including:
1. Class syntax such as access modifiers, static members, constructors, initializers, and the 'this' keyword.
2. Inheritance concepts like subclasses, superclasses, overriding methods, and the 'super' keyword.
3. Interfaces as contracts that can be implemented by classes to define common behaviors without implementation.
4. Nested classes including static nested classes and inner classes, as well as anonymous classes defined without a class name.
5. Enums, constructors, and initializers are also covered but examples are not shown.
1. Inheritance is a mechanism where a new class is derived from an existing class, known as the base or parent class. The derived class inherits properties and methods from the parent class.
2. There are 5 types of inheritance: single, multilevel, multiple, hierarchical, and hybrid. Multiple inheritance allows a class to inherit from more than one parent class.
3. Overriding allows a subclass to replace or extend a method defined in the parent class, while still calling the parent method using the super() function or parent class name. This allows the subclass to provide a specific implementation of a method.
The document discusses classes and objects in object-oriented programming. It defines a class as a blueprint for objects that bind data and functions together. A class defines data members and member functions. Objects are instances of a class that can access class data and functions. The document provides examples of defining a class called "test" with private and public members, and creating objects of the class to demonstrate accessing members.
In this article we will learn classes and objects in C# programming.
Till now in the past two articles we have seen all the labs which was using functional programming. From now in coming all the articles we will do the programming using classes and objects. As this is professional approach of doing the programming. With classes and objects approach, code it reduces code reading complexity and it improves readability and also offers re-usability.
The document discusses object-oriented programming concepts in C#, including defining classes, constructors, properties, static members, interfaces, inheritance, and polymorphism. It provides examples of defining a simple Cat class with fields, a constructor, properties, and methods. It also demonstrates using the Dog class by creating dog objects, setting their properties, and calling their bark method.
The document discusses classes and objects in .NET. It defines classes as templates that define an object's properties and behaviors. Objects are instances of classes that have state defined by their property values. The document provides examples of declaring classes and objects in C#, and discusses accessing fields, properties, methods, and constructors of classes and objects. It also covers the differences between instance and static members of classes.
Object-oriented programming focuses on data. An object is a basic run-time entity. A class is also known as a user-defined data type. Inheritance provides the idea of reusability. Objects can communicate with each other through message passing. Polymorphism is achieved through operator overloading and function overloading.
The document discusses key concepts in C++ classes including encapsulation, information hiding, access specifiers, and constructors. It defines a class as a way to combine attributes and behaviors of real-world objects into a single unit. A class uses encapsulation to associate code and data, and information hiding to secure data from direct access. Access specifiers like public, private, and protected determine member visibility. Constructors are special member functions that initialize objects upon instantiation.
This document discusses file handling in C++. It introduces three classes - ofstream, ifstream, and fstream - that allow performing output and input of characters to and from files. The ofstream class is used to write to files, ifstream is used to read from files, and fstream can both read and write. The open() method is used to open a file, specifying the file name and optional file mode. Writing to a file uses the << operator and reading uses the >> operator. Operator overloading allows user-defined types like classes to define the meaning of operators like + when used on their objects.
Python Programming - VI. Classes and ObjectsRanel Padon
This document discusses classes and objects in Python programming. It covers key concepts like class attributes, instantiating classes to create objects, using constructors and destructors, composition where objects have other objects as attributes, and referencing objects. The document uses examples like a Time class to demonstrate class syntax and how to define attributes and behaviors for classes.
Chap 3 Python Object Oriented Programming - Copy.pptmuneshwarbisen1
The document discusses object oriented programming concepts in Python including classes, objects, instances, methods, inheritance, and class attributes. It provides examples of defining classes, instantiating objects, using methods, and the difference between class and instance attributes. Key concepts covered include defining classes with the class keyword, creating object instances, using the __init__() method for initialization, and allowing derived classes to inherit from base classes.
The document introduces Python modules and importing. It discusses three formats for importing modules: import somefile, from somefile import *, and from somefile import className. It describes commonly used Python modules like sys, os, and math. It also covers defining your own modules, directories for module files, object-oriented programming in Python including defining classes, creating and deleting instances, methods and self, accessing attributes and methods, attributes, inheritance, and redefining methods.
در این جلسه به بررسی بحث برنامه نویسی شی گرا و کلاس ها در پایتون پرداختیم
PySec101 Fall 2013 J7E1 By Mohammad Reza Kamalifard
Talk About:
Object oriented programming and Classes in Python
Data Structures and Algorithms in PythonJakeLWright
Classes allow programmers to implement abstract data types (ADTs) to provide logical descriptions of data objects. A class defines an object's state via attributes and behaviors via methods. The Fraction class example demonstrates defining state as numerator and denominator attributes initialized in the __init__() constructor. Methods like __add__() and __eq__() allow Fraction objects to behave like numeric values and compare for equality. Exceptions must be handled to prevent program crashes from errors. List comprehensions provide a concise way to build lists through iteration and filtering.
The document discusses key concepts of object-oriented programming such as classes, objects, encapsulation, inheritance, and polymorphism. It provides examples of defining a Point class in Python with methods like translate() and distance() as well as using concepts like constructors, private and protected members, and naming conventions using underscores. The document serves as an introduction to object-oriented programming principles and their implementation in Python.
This document provides an overview of object-oriented programming concepts in Python including classes, objects, inheritance, polymorphism and data hiding. It defines key OOP terms like class, object, method, and inheritance. It also demonstrates how to define classes with attributes and methods, create object instances, and extend functionality via inheritance. The document shows how operators and methods can be overloaded in classes.
Python classes allow for the creation of object-oriented programming through defining blueprints for objects with shared attributes and behaviors. Classes are created using the class keyword and contain attributes like variables and methods like functions. Objects are instantiated from classes and can access both class level and instance level attributes. Key concepts covered include inheritance, encapsulation, polymorphism, and special methods.
Python classes allow for the creation of object-oriented programming in Python. Classes define blueprints for objects with shared attributes and behaviors. Key aspects of classes include defining attributes and methods, constructing objects from classes, inheritance that allows subclasses to extend parent classes, and special methods that enable built-in behaviors. Classes are a fundamental part of Python that enable code reuse and organization.
Types of errors include syntax errors, logical errors, and runtime errors. Exceptions are errors that occur during program execution. When an exception occurs, Python generates an exception object that can be handled to avoid crashing the program. Exceptions allow errors to be handled gracefully. The try and except blocks are used to catch and handle exceptions. Python has a hierarchy of built-in exceptions like ZeroDivisionError, NameError, and IOError.
This document discusses object-oriented programming concepts in Python including:
- Classes define templates for objects with attributes and methods. Objects are instances of classes.
- The __init__ method initializes attributes when an object is constructed.
- Classes can make attributes private using double underscores. Encapsulation hides implementation details.
- Objects can be mutable, allowing state changes, or immutable like strings which cannot change.
- Inheritance allows subclasses to extend and modify parent class behavior through polymorphism.
An object oriented concept in python is detailed for the students or anyone who aspire to learn more powerful concept that helps in developing software or any web development to the persons who work in a tech filed
This document discusses classes and objects in Python. It introduces defining a class with fields and methods, using a class by creating objects and calling methods, constructors, the __init__ method, the __str__ method for string representation, operator overloading, inheritance between classes, and calling superclass methods from a subclass. Examples of a Point class are provided throughout to demonstrate various object-oriented programming concepts in Python.
This document discusses four types of learning agents in artificial intelligence: simple reflex agents, model-based agents, goal-based agents, and utility-based agents. A learning agent improves upon these by dynamically learning a policy to model its environment and build action-state rules based on rewards from interacting with its environment.
Abstract: This workshop teaches the application of statistics to the software quality assurance process. The course covers smoke testing, acceptance testing, Pareto principle, defect distributions, automated vs. manual testing, predicting effort, and experimental thoughts using Bellman equation approach and machine learning.
Level: Intermediate
Requirements: Some basic statistics knowledge is preferred and experience or exposure to the software quality assurance process.
Abstract: This workshop teaches basic algorithms in whiteboarding interviews. All the code examples are in Python and the course has dual purpose teaching basic Python programming.
Abstract: This PDSG workshop covers the basics of OOP programming in Python. Concepts covered are class, object, scope, method overloading and inheritance.
Level: Fundamental
Requirements: One should have some knowledge of programming.
Python - Installing and Using Python and Jupyter NotepadAndrew Ferlitsch
Abstract: This PDSG workshop covers installing Python and Juypter Notebook, and how to create a notebook.
Level: Fundamental
Requirements: One should have some knowledge of programming.
Natural Language Processing - Groupings (Associations) GenerationAndrew Ferlitsch
Abstract: This PDSG workshop covers methods to automatically generate word groupings as associations, which can be used to teach associations between objects to pre-school and early school children. Ex. What item does not belong? Cat, Dog, Fire Truck, Bird
In this presentation, I will cover how to build categorical and association dictionaries to automatically generate associations of the form, what item does not belong.
Level: Intermediate
Requirements: One should have some programming knowledge.
Natural Language Provessing - Handling Narrarive Fields in Datasets for Class...Andrew Ferlitsch
Abstract: It is common for government and public datasets to include narrative fields, such as inspection reports, incident reporting, surveys, 911 calls, fire response, etc. In addition to categorical fields, such as datetime, location, demographics, these datasets tend to include a narrative description (e.g., what happened). It is typically in the narrative field that the most interesting data resides for the purpose of classifying. The problem, is that since the narrative is human interpreted and entered, each entry may be unique and if we use the whole entry as a single value, one will end up with an overfitted model that works only on the training data.
In this presentation, I will cover how natural language processing techniques are used to convert narrative fields into categorical data.
Level: Intermediate
Requirements: One should know basics of linear regression models. No prior programming knowledge is required.
Machine Learning - Introduction to Recurrent Neural NetworksAndrew Ferlitsch
Abstract: This PDSG workshop introduces basic concepts of recurrent neural networks. Concepts covered are feed forward vs. recurrent, time progression, memory cells, short term memory predictions and long term memory predictions.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Machine Learning - Introduction to Convolutional Neural NetworksAndrew Ferlitsch
Abstract: This PDSG workshop introduces basic concepts of convolutional neural networks. Concepts covered are image pixels, image preprocessing, feature detectors, feature maps, convolution, ReLU, pooling and flattening.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required. Some knowledge of neural networks is recommended.
Machine Learning - Introduction to Neural NetworksAndrew Ferlitsch
Abstract: This PDSG workshop introduces basic concepts of neural networks. Concepts covered are Neurons, Binary vs. Categorical vs. Real Value output, activation functions, fully connected networks, deep neural networks, specialized learners, cost function and feed forward.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
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 - Accuracy and Confusion MatrixAndrew Ferlitsch
Abstract: This PDSG workshop introduces basic concepts on measuring accuracy of your trained model. Concepts covered are loss functions and confusion matrices.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Abstract: This PDSG workshop introduces basic concepts of ensemble methods in machine learning. Concepts covered are Condercet Jury Theorem, Weak Learners, Decision Stumps, Bagging and Majority Voting.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Abstract: This PDSG workshop introduces basic concepts of multiple linear regression in machine learning. Concepts covered are Feature Elimination and Backward Elimination, with examples in Python.
Level: Fundamental
Requirements: Should have some experience with Python programming.
Abstract: This PDSG workshop introduces basic concepts of simple linear regression in machine learning. Concepts covered are Slope of a Line, Loss Function, and Solving Simple Linear Regression Equation, with examples.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Abstract: This PDSG workshop introduces basic concepts of categorical variables in training data. Concepts covered are dummy variable conversion, and dummy variable trap.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Abstract: This PDSG workshop introduces basic concepts of splitting a dataset for training a model in machine learning. Concepts covered are training, test and validation data, serial and random splitting, data imbalance and k-fold cross validation.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Dataset Preparation
Abstract: This PDSG workshop introduces basic concepts on preparing a dataset for training a model. Concepts covered are data wrangling, replacing missing values, categorical variable conversion, and feature scaling.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Abstract: This PDSG workshop introduces basic concepts on TensorFlow. The course covers fundamentals. Concepts covered are Vectors/Matrices/Vectors, Design&Run, Constants, Operations, Placeholders, Bindings, Operators, Loss Function and Training.
Level: Fundamental
Requirements: Some basic programming knowledge is preferred. No prior statistics background is required.
Abstract: This PDSG workshop introduces basic concepts on machine learning. The course covers fundamentals of Supervised and Unsupervised Learning, Decision Trees, Pruning, Ensemble Trees, Linear Regressions, Loss Functions, K-means, and dataset preparation.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
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.
מכונות CNC קידוח אנכיות הן הבחירה הנכונה והטובה ביותר לקידוח ארונות וארגזים לייצור רהיטים. החלק נוסע לאורך ציר ה-x באמצעות ציר דיגיטלי מדויק, ותפוס ע"י צבת מכנית, כך שאין צורך לבצע setup (התאמות) לגדלים שונים של חלקים.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
No-Code Workflows for CAD & 3D Data: Scaling AI-Driven InfrastructureSafe Software
When projects depend on fast, reliable spatial data, every minute counts.
AI Clearing needed a faster way to handle complex spatial data from drone surveys, CAD designs and 3D project models across construction sites. With FME Form, they built no-code workflows to clean, convert, integrate, and validate dozens of data formats – cutting analysis time from 5 hours to just 30 minutes.
Join us, our partner Globema, and customer AI Clearing to see how they:
-Automate processing of 2D, 3D, drone, spatial, and non-spatial data
-Analyze construction progress 10x faster and with fewer errors
-Handle diverse formats like DWG, KML, SHP, and PDF with ease
-Scale their workflows for international projects in solar, roads, and pipelines
If you work with complex data, join us to learn how to optimize your own processes and transform your results with FME.
Mastering AI Workflows with FME - Peak of Data & AI 2025Safe Software
Harness the full potential of AI with FME: From creating high-quality training data to optimizing models and utilizing results, FME supports every step of your AI workflow. Seamlessly integrate a wide range of models, including those for data enhancement, forecasting, image and object recognition, and large language models. Customize AI models to meet your exact needs with FME’s powerful tools for training, optimization, and seamless integration
Presentation given at the LangChain community meetup London
https://lu.ma/9d5fntgj
Coveres
Agentic AI: Beyond the Buzz
Introduction to AI Agent and Agentic AI
Agent Use case and stats
Introduction to LangGraph
Build agent with LangGraph Studio V2
Establish Visibility and Manage Risk in the Supply Chain with Anchore SBOMAnchore
Over 70% of any given software application consumes open source software (most likely not even from the original source) and only 15% of organizations feel confident in their risk management practices.
With the newly announced Anchore SBOM feature, teams can start safely consuming OSS while mitigating security and compliance risks. Learn how to import SBOMs in industry-standard formats (SPDX, CycloneDX, Syft), validate their integrity, and proactively address vulnerabilities within your software ecosystem.
Integration of Utility Data into 3D BIM Models Using a 3D Solids Modeling Wor...Safe Software
Jacobs has developed a 3D utility solids modelling workflow to improve the integration of utility data into 3D Building Information Modeling (BIM) environments. This workflow, a collaborative effort between the New Zealand Geospatial Team and the Australian Data Capture Team, employs FME to convert 2D utility data into detailed 3D representations, supporting enhanced spatial analysis and clash detection.
To enable the automation of this process, Jacobs has also developed a survey data standard that standardizes the capture of existing utilities. This standard ensures consistency in data collection, forming the foundation for the subsequent automated validation and modelling steps. The workflow begins with the acquisition of utility survey data, including attributes such as location, depth, diameter, and material of utility assets like pipes and manholes. This data is validated through a custom-built tool that ensures completeness and logical consistency, including checks for proper connectivity between network components. Following validation, the data is processed using an automated modelling tool to generate 3D solids from 2D geometric representations. These solids are then integrated into BIM models to facilitate compatibility with 3D workflows and enable detailed spatial analyses.
The workflow contributes to improved spatial understanding by visualizing the relationships between utilities and other infrastructure elements. The automation of validation and modeling processes ensures consistent and accurate outputs, minimizing errors and increasing workflow efficiency.
This methodology highlights the application of FME in addressing challenges associated with geospatial data transformation and demonstrates its utility in enhancing data integration within BIM frameworks. By enabling accurate 3D representation of utility networks, the workflow supports improved design collaboration and decision-making in complex infrastructure projects
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.
Bridging the divide: A conversation on tariffs today in the book industry - T...BookNet Canada
A collaboration-focused conversation on the recently imposed US and Canadian tariffs where speakers shared insights into the current legislative landscape, ongoing advocacy efforts, and recommended next steps. This event was presented in partnership with the Book Industry Study Group.
Link to accompanying resource: https://bnctechforum.ca/sessions/bridging-the-divide-a-conversation-on-tariffs-today-in-the-book-industry/
Presented by BookNet Canada and the Book Industry Study Group on May 29, 2025 with support from the Department of Canadian Heritage.
Enabling BIM / GIS integrations with Other Systems with FMESafe Software
Jacobs has successfully utilized FME to tackle the complexities of integrating diverse data sources in a confidential $1 billion campus improvement project. The project aimed to create a comprehensive digital twin by merging Building Information Modeling (BIM) data, Construction Operations Building Information Exchange (COBie) data, and various other data sources into a unified Geographic Information System (GIS) platform. The challenge lay in the disparate nature of these data sources, which were siloed and incompatible with each other, hindering efficient data management and decision-making processes.
To address this, Jacobs leveraged FME to automate the extraction, transformation, and loading (ETL) of data between ArcGIS Indoors and IBM Maximo. This process ensured accurate transfer of maintainable asset and work order data, creating a comprehensive 2D and 3D representation of the campus for Facility Management. FME's server capabilities enabled real-time updates and synchronization between ArcGIS Indoors and Maximo, facilitating automatic updates of asset information and work orders. Additionally, Survey123 forms allowed field personnel to capture and submit data directly from their mobile devices, triggering FME workflows via webhooks for real-time data updates. This seamless integration has significantly enhanced data management, improved decision-making processes, and ensured data consistency across the project lifecycle.
Your startup on AWS - How to architect and maintain a Lean and Mean accountangelo60207
Prevent infrastructure costs from becoming a significant line item on your startup’s budget! Serial entrepreneur and software architect Angelo Mandato will share his experience with AWS Activate (startup credits from AWS) and knowledge on how to architect a lean and mean AWS account ideal for budget minded and bootstrapped startups. In this session you will learn how to manage a production ready AWS account capable of scaling as your startup grows for less than $100/month before credits. We will discuss AWS Budgets, Cost Explorer, architect priorities, and the importance of having flexible, optimized Infrastructure as Code. We will wrap everything up discussing opportunities where to save with AWS services such as S3, EC2, Load Balancers, Lambda Functions, RDS, and many others.
2. Class Definition
• Class defined with the keyword class.
class Node:
….
• Constructor defined with the special reserved name __init__.
class Node:
def __init__( self ):
…
…
Class keyword
Class Name
Keyword to define a method (function)
Reserved name for constructor
Required first parameter, self refers to this instantiated instance of the class.
3. Class Example
# Base Class definition for Node in Tree/Graph
class Node:
key = None # node data
# constructor: set the node data
def __init__( self, key ):
self.key = key
# Get or Set the node data
def Key( self, key = None ):
if None is key:
return self.key
self.key = key
Start of class definition
Initialization if member variable
Constructor with parameter
Use keyword self to refer to member variable
Define class method
4. Class Scope
• Parameter to Methods.
class Node:
def myFunc( self, flag )
if flag == True:
• Local to Method
class Node:
def myFunc( self ):
flag = True
• Class Member Variable
class Node:
flag = False
def myFunc( self ):
self.flag = True
• Global Scope
class Node:
def myFunc( self ):
global flag
Parameter ‘flag’
Referenced without qualifier
Referenced without qualifier and not defined as parameter.
Referenced with qualifier self, refers to class variable
When declared with global keyword, all references in method
Will refer to the global (not local) scope of variable.
5. Method Overloading
• There is NO direct support for method overloading in Python.
• Can be emulated (spoofed) by using Default parameters.
• Example: Setter and Getter
Next() # get the next element
Next(ele) # set the next element
• Without method overloading
# Set Next
def Next( self, next )
self.next = next
# Get Next
def GetNext(self)
return self.next
Must use different function name
6. Method Overloading using Default Parameter
• A default parameter to a function is where a default value is
specified in the function definition, when the function is called
without the parameter.
• Example: Setter and Getter
# Set of Get Next
def Next( self, next = None )
if next is None:
return self.next
self.next = next
Default Paramter
Get
Set
7. Operator Overloading
• Built-in operators (+, -, str(), int(), … ) can be overloaded for a
class object in Python.
• Each class has a default implementation for built-in operators.
the default implementations (magic methods) can be overridden to
implement operator overloading.
• Built-in operators are designated using the double dash __name__
convention. Below are some of these magic methods:
__str__ string conversion __add__ + operator
__int__ integer conversion __sub__ - operator
__eq__ == operator __mul__ * operator
__ne__ != operator __divmod__ / and % operator
__lt__ < operator __len__ len() operator
__le__ <= operator __getitem__ [] index operator
8. Operator Overloading
• Example:
class Node(object):
def __init__(self, name):
self.name = name
def __str__( self ):
return name
def__eq__( self, other ):
return (self.name == other.name)
node = Node( ‘foobar’)
print (str( node ) ) # will print foobar
Override string conversion
Override equal comparison
A full list of magic (special) methods that can be overwritten is available on the python.org website:
https://docs.python.org/3/reference/datamodel.html#special-method-names
9. Class Inheritance
• Defines a new class that extends a base (superclass).
• The new class (subclass) inherits the methods and member
variables of the base (superclass).
class BinaryTree( Node ):
• Invoking the base (superclass) constructor
Derived (subclass) name Base (superclass) that is inherited
class BinaryTree( Node ):
# Constructor: set the node data and left/right subtrees to null
def __init__( self, key ):
super().__init__( self, key )
Derived (subclass) constructor
Invoke base (superclass) constructor
Good Practice: it's generally considered good practice to have non-inheriting classes explicitly inherit from the "object" class.
Example: class Node: would become class Node(object):