This document contains source code for a Tetris game written in C++. It includes class definitions and functions for game objects like Line, Square, LLighting shapes. It uses object oriented concepts like inheritance, polymorphism. Key functions include rotating and moving shapes, detecting collisions, clearing lines, updating score. Header files included are for input/output, graphics, memory allocation etc. The main() function initializes the game, calls functions to start the game, display screen, handle input and check for game over or level completion conditions.
Intro to Python for High School students, from basics up to built-in functions, recursion, and list/dict comprehensions. Does not include classes, which are in Unit #2.
Here is a draft purpose statement for a computer science program:
The purpose of the computer science program is to prepare students for careers in the dynamic field of computing through a combination of theoretical and practical learning. The program aims to develop students' technical skills and knowledge in core areas such as programming, software engineering, databases, networking, and cybersecurity. Students will gain hands-on experience through project-based learning opportunities that apply concepts to real-world problems.
The curriculum is designed to cultivate computational thinking and problem-solving abilities that are highly valued by employers. Courses also focus on professional skills like communication, collaboration, and project management. Upon graduating, students will be equipped to enter the workforce as software developers, network administrators,
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial For Be...Simplilearn
This presentation on TensorFlow will help you in understanding what exactly is TensorFlow and how it is used in Deep Learning. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this tutorial, you will learn the fundamentals of TensorFlow concepts, functions, and operations required to implement deep learning algorithms and leverage data like never before. This TensorFlow tutorial is ideal for beginners who want to pursue a career in Deep Learning. Now, let us deep dive into this TensorFlow tutorial and understand what TensorFlow actually is and how to use it.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning Libraries
3. Why TensorFlow?
4. What is TensorFlow?
5. What are Tensors?
6. What is a Data Flow Graph?
7. Program Elements in TensorFlow
8. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
This document discusses mining social data from graphs by extracting the data from social networks using their APIs or by crawling websites. It describes representing the social data as graphs and using graph mining techniques like finding frequent patterns and substructures using algorithms like Apriori, pattern growth, and CL-CBI (ChunkingLess - Constraint-Based Induction). Decision trees can also be used to iteratively find patterns that branch the data. The challenges include the graph nature of the data, errors and unknowns, and vanity metrics, but graphs are useful for capturing complex social structures.
The document discusses deep neural networks (DNN) and deep learning. It explains that deep learning uses multiple layers to learn hierarchical representations from raw input data. Lower layers identify lower-level features while higher layers integrate these into more complex patterns. Deep learning models are trained on large datasets by adjusting weights to minimize error. Applications discussed include image recognition, natural language processing, drug discovery, and analyzing satellite imagery. Both advantages like state-of-the-art performance and drawbacks like high computational costs are outlined.
Game Design as an Intro to Computer Science (Meaningful Play 2014)marksuter
Presented by Mark Suter at Michigan State University in November 2014 for the Meaningful Play Conference.
These are methods I use in my classroom to introduce computer science concepts, as well as some common syntax.
The document provides an overview of object-oriented programming (OOP) concepts, including:
1. It discusses procedural programming and structured programming, and how OOP improved upon these approaches by emphasizing data rather than procedures. OOP focuses on representing real-world objects like menus and buttons through objects with both data and functions.
2. The core concepts of OOP are described - objects, classes, encapsulation, inheritance, polymorphism. An object contains both data (attributes) and code (methods) and is an instance of a class. Classes organize similar objects, and encapsulation binds data to methods within an object.
3. Advantages of OOP include modularity, code reusability
Intro to Python for High School students, from basics up to built-in functions, recursion, and list/dict comprehensions. Does not include classes, which are in Unit #2.
Here is a draft purpose statement for a computer science program:
The purpose of the computer science program is to prepare students for careers in the dynamic field of computing through a combination of theoretical and practical learning. The program aims to develop students' technical skills and knowledge in core areas such as programming, software engineering, databases, networking, and cybersecurity. Students will gain hands-on experience through project-based learning opportunities that apply concepts to real-world problems.
The curriculum is designed to cultivate computational thinking and problem-solving abilities that are highly valued by employers. Courses also focus on professional skills like communication, collaboration, and project management. Upon graduating, students will be equipped to enter the workforce as software developers, network administrators,
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial For Be...Simplilearn
This presentation on TensorFlow will help you in understanding what exactly is TensorFlow and how it is used in Deep Learning. TensorFlow is a software library developed by Google for the purposes of conducting machine learning and deep neural network research. In this tutorial, you will learn the fundamentals of TensorFlow concepts, functions, and operations required to implement deep learning algorithms and leverage data like never before. This TensorFlow tutorial is ideal for beginners who want to pursue a career in Deep Learning. Now, let us deep dive into this TensorFlow tutorial and understand what TensorFlow actually is and how to use it.
Below topics are explained in this TensorFlow presentation:
1. What is Deep Learning?
2. Top Deep Learning Libraries
3. Why TensorFlow?
4. What is TensorFlow?
5. What are Tensors?
6. What is a Data Flow Graph?
7. Program Elements in TensorFlow
8. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
Learn more at: https://www.simplilearn.com
This document discusses mining social data from graphs by extracting the data from social networks using their APIs or by crawling websites. It describes representing the social data as graphs and using graph mining techniques like finding frequent patterns and substructures using algorithms like Apriori, pattern growth, and CL-CBI (ChunkingLess - Constraint-Based Induction). Decision trees can also be used to iteratively find patterns that branch the data. The challenges include the graph nature of the data, errors and unknowns, and vanity metrics, but graphs are useful for capturing complex social structures.
The document discusses deep neural networks (DNN) and deep learning. It explains that deep learning uses multiple layers to learn hierarchical representations from raw input data. Lower layers identify lower-level features while higher layers integrate these into more complex patterns. Deep learning models are trained on large datasets by adjusting weights to minimize error. Applications discussed include image recognition, natural language processing, drug discovery, and analyzing satellite imagery. Both advantages like state-of-the-art performance and drawbacks like high computational costs are outlined.
Game Design as an Intro to Computer Science (Meaningful Play 2014)marksuter
Presented by Mark Suter at Michigan State University in November 2014 for the Meaningful Play Conference.
These are methods I use in my classroom to introduce computer science concepts, as well as some common syntax.
The document provides an overview of object-oriented programming (OOP) concepts, including:
1. It discusses procedural programming and structured programming, and how OOP improved upon these approaches by emphasizing data rather than procedures. OOP focuses on representing real-world objects like menus and buttons through objects with both data and functions.
2. The core concepts of OOP are described - objects, classes, encapsulation, inheritance, polymorphism. An object contains both data (attributes) and code (methods) and is an instance of a class. Classes organize similar objects, and encapsulation binds data to methods within an object.
3. Advantages of OOP include modularity, code reusability
Traditional Machine Learning had used handwritten features and modality-specific machine learning to classify images, text or recognize voices. Deep learning / Neural network identifies features and finds different patterns automatically. Time to build these complex tasks has been drastically reduced and accuracy has exponentially increased because of advancements in Deep learning. Neural networks have been partly inspired from how 86 billion neurons work in a human and become more of a mathematical and a computer problem. We will see by the end of the blog how neural networks can be intuitively understood and implemented as a set of matrix multiplications, cost function, and optimization algorithms.
More information, visit: http://www.godatadriven.com/accelerator.html
Data scientists aren’t a nice-to-have anymore, they are a must-have. Businesses of all sizes are scooping up this new breed of engineering professional. But how do you find the right one for your business?
The Data Science Accelerator Program is a one year program, delivered in Amsterdam by world-class industry practitioners. It provides your aspiring data scientists with intensive on- and off-site instruction, access to an extensive network of speakers and mentors and coaching.
The Data Science Accelerator Program helps you assess and radically develop the skills of your data science staff or recruits.
Our goal is to deliver you excellent data scientists that help you become a data driven enterprise.
The right tools
We teach your organisation the proven data science tools.
The right hands
We are trusted by many industry leading partners.
The right experience
We've done big data and data science at many clients, we know what the real world is like.
The right experts
We have a world class selection of lecturers that you will be working with.
Vincent D. Warmerdam
Jonathan Samoocha
Ivo Everts
Rogier van der Geer
Ron van Weverwijk
Giovanni Lanzani
The right curriculum
We meet twice a month. Once for a lecture, once for a hackathon.
Lectures
The RStudio stack.
The art of simulation.
The iPython stack.
Linear modelling.
Operations research.
Nonlinear modelling.
Clustering & ensemble methods.
Natural language processing.
Time series.
Visualisation.
Scaling to big data.
Advanced topics.
Hackathons
Scrape and mine the internet.
Solving multiarmed bandit problems.
Webdev with flask and pandas as a backend.
Build an automation script for linear models.
Build a heuristic tsp solver.
Code review your automation for nonlinear models.
Build a method that outperforms random forests.
Build a markov chain to generate song lyrics.
Predict an optimal portfolio for the stock market.
Create an interactive d3 app with backend.
Start up a spark cluster with large s3 data.
You pick!
Interested?
Ping us here. signal@godatadriven.com
This document provides an overview of a machine learning course. It outlines the course structure, including topics covered, assignments, and grading. The course covers fundamental machine learning algorithms for classification, regression, clustering, and dimensionality reduction. It also discusses applications of machine learning like spam filtering, recommender systems, and chess playing computers.
This document provides an overview of a machine learning course. It outlines the course structure, including topics covered, assignments, and grading. The course covers fundamental machine learning algorithms for classification, regression, clustering, and dimensionality reduction. It also discusses applications of machine learning like spam filtering, recommender systems, and chess playing programs.
Social Network Analysis Introduction including Data Structure Graph overview. Doug Needham
Social Network Analysis Introduction including Data Structure Graph overview. Given in Cincinnati August 18th 2015 as part of the DataSeed Meetup group.
These questions will be a bit advanced level 2sadhana312471
These questions will be a bit advanced(Intermediate) in terms of Python interview.
This is the continuity of Nail the Python Interview Questions.
The fields that these questions will help you in are:
• Python Developer
• Data Analyst
• Research Analyst
• Data Scientist
The document provides an introduction to object oriented programming (OOP) compared to procedural programming. It discusses key concepts in OOP like objects, classes, attributes, methods, encapsulation. Objects contain attributes (data) and methods (behaviors). Classes are templates that define common attributes and methods for a set of objects. Encapsulation involves hiding class data and implementation details through access modifiers like private and public. Examples are provided to demonstrate how to define classes and create objects in C++ code.
This document provides an overview and introduction to deep learning. It discusses motivations for deep learning such as its powerful learning capabilities. It then covers deep learning basics like neural networks, neurons, training processes, and gradient descent. It also discusses different network architectures like convolutional neural networks and recurrent neural networks. Finally, it describes various deep learning applications, tools, and key researchers and companies in the field.
The document presents a Tic Tac Toe game project for Android. It introduces the project, describing it as a two-player game played on a 3x3 grid. It lists the required tools including Java, XML, Android Studio, and Android Virtual Devices. It outlines key features such as displaying whose turn it is and including a restart button. It then describes the development process including key files like the activity_main layout file and MainActivity java file to manage the interface and logic. It concludes by providing a link to the project source files.
TeelTech - Advancing Mobile Device Forensics (online version)Mike Felch
This document provides an overview of a training on advancing mobile device forensics through reverse engineering and programming techniques. It discusses how traditional forensic tools are becoming less effective at recovering data from newer devices and applications that are designed for privacy. The training will demonstrate extracting artifacts from a raw device image using a hex editor and Python scripts. It also outlines a simulated criminal investigation involving the murder of a victim, and how analyzing the digital evidence from the victim and suspect's mobile phones through these new techniques revealed deleted messages that are relevant to the case.
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.
Deep learning is introduced along with its applications and key players in the field. The document discusses the problem space of inputs and outputs for deep learning systems. It describes what deep learning is, providing definitions and explaining the rise of neural networks. Key deep learning architectures like convolutional neural networks are overviewed along with a brief history and motivations for deep learning.
This document provides an overview of object-oriented programming (OOP) including:
- The history and key concepts of OOP like classes, objects, inheritance, polymorphism, and encapsulation.
- Popular OOP languages like C++, Java, and Python.
- Differences between procedural and OOP like top-down design and modularity.
The Ring programming language version 1.8 book - Part 7 of 202Mahmoud Samir Fayed
Ring 1.8 includes several new features and improvements such as better performance, new applications like Find in Files and String2Constant, more 3D samples, compiling on Manjaro Linux, updated libraries, and notes for extension creators. Key updates include 10-100% faster performance, a Find in Files application, a String2Constant tool to convert code to use constants, a StopWatch application, improved Form Designer, RingQt, and code generator for extensions. The release provides better compiler, virtual machine, and overall performance.
This document provides an overview of deep learning and neural networks. It begins with definitions of machine learning, artificial intelligence, and the different types of machine learning problems. It then introduces deep learning, explaining that it uses neural networks with multiple layers to learn representations of data. The document discusses why deep learning works better than traditional machine learning for complex problems. It covers key concepts like activation functions, gradient descent, backpropagation, and overfitting. It also provides examples of applications of deep learning and popular deep learning frameworks like TensorFlow. Overall, the document gives a high-level introduction to deep learning concepts and techniques.
This document provides an introduction to threads, events, and mutexes in C# classes. It begins with a basic example of creating a thread to call a method. Subsequent examples demonstrate passing delegate methods to threads, using the Sleep method to simulate multithreading, and accessing the CurrentThread property. The document also notes that the Thread class is sealed and cannot be inherited from.
The document summarizes the author's work experience and education history. It includes the following key points:
1) The author studied software engineering in Germany from 2002-2008, gaining knowledge in database architecture and skills in areas like DB2 administration.
2) An internship at Intershop Communication AG involved completing a thesis analyzing database parameters and their impact on performance using Oracle.
3) Employment at CNOOC from 2009 involved contributing to a GIS project to store oil exploration data and make it accessible online.
4) After immigrating to Canada in 2013, the author studied game engines like DirectX and OGRE, focusing on techniques like lighting, shadows, and particles to prepare for work
This document studies the effects of sporamin, a protein from sweet potatoes, on human gut cancer cell lines. It finds that sporamin exerts significant anti-proliferative and anti-metastatic effects by inducing apoptosis in human pancreatic, esophageal, and colorectal cancer cell lines. Specifically, sporamin treatment was found to suppress tumor growth through influencing expression of Bcl-2 family proteins and inhibiting the NF-κB pathway. Figures 1-3 show results of experiments demonstrating sporamin's dose-dependent inhibition of cancer cell proliferation and induction of apoptosis.
This document discusses the composition, classification, structure, and functions of connective tissue. It begins by outlining the key elements that make up connective tissue - cells and extracellular matrix composed of ground substance and protein fibers. Connective tissue is then classified into loose connective tissue (areolar, adipose, reticular), dense connective tissue (collagenous, elastic), supporting connective tissue (cartilage, bone), and fluid connective tissue (blood, lymph). Each type is described in terms of its matrix composition and cellular components as well as its typical locations and functions in the body.
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Similar to Computer investigatroy project c++ class 12
Traditional Machine Learning had used handwritten features and modality-specific machine learning to classify images, text or recognize voices. Deep learning / Neural network identifies features and finds different patterns automatically. Time to build these complex tasks has been drastically reduced and accuracy has exponentially increased because of advancements in Deep learning. Neural networks have been partly inspired from how 86 billion neurons work in a human and become more of a mathematical and a computer problem. We will see by the end of the blog how neural networks can be intuitively understood and implemented as a set of matrix multiplications, cost function, and optimization algorithms.
More information, visit: http://www.godatadriven.com/accelerator.html
Data scientists aren’t a nice-to-have anymore, they are a must-have. Businesses of all sizes are scooping up this new breed of engineering professional. But how do you find the right one for your business?
The Data Science Accelerator Program is a one year program, delivered in Amsterdam by world-class industry practitioners. It provides your aspiring data scientists with intensive on- and off-site instruction, access to an extensive network of speakers and mentors and coaching.
The Data Science Accelerator Program helps you assess and radically develop the skills of your data science staff or recruits.
Our goal is to deliver you excellent data scientists that help you become a data driven enterprise.
The right tools
We teach your organisation the proven data science tools.
The right hands
We are trusted by many industry leading partners.
The right experience
We've done big data and data science at many clients, we know what the real world is like.
The right experts
We have a world class selection of lecturers that you will be working with.
Vincent D. Warmerdam
Jonathan Samoocha
Ivo Everts
Rogier van der Geer
Ron van Weverwijk
Giovanni Lanzani
The right curriculum
We meet twice a month. Once for a lecture, once for a hackathon.
Lectures
The RStudio stack.
The art of simulation.
The iPython stack.
Linear modelling.
Operations research.
Nonlinear modelling.
Clustering & ensemble methods.
Natural language processing.
Time series.
Visualisation.
Scaling to big data.
Advanced topics.
Hackathons
Scrape and mine the internet.
Solving multiarmed bandit problems.
Webdev with flask and pandas as a backend.
Build an automation script for linear models.
Build a heuristic tsp solver.
Code review your automation for nonlinear models.
Build a method that outperforms random forests.
Build a markov chain to generate song lyrics.
Predict an optimal portfolio for the stock market.
Create an interactive d3 app with backend.
Start up a spark cluster with large s3 data.
You pick!
Interested?
Ping us here. signal@godatadriven.com
This document provides an overview of a machine learning course. It outlines the course structure, including topics covered, assignments, and grading. The course covers fundamental machine learning algorithms for classification, regression, clustering, and dimensionality reduction. It also discusses applications of machine learning like spam filtering, recommender systems, and chess playing computers.
This document provides an overview of a machine learning course. It outlines the course structure, including topics covered, assignments, and grading. The course covers fundamental machine learning algorithms for classification, regression, clustering, and dimensionality reduction. It also discusses applications of machine learning like spam filtering, recommender systems, and chess playing programs.
Social Network Analysis Introduction including Data Structure Graph overview. Doug Needham
Social Network Analysis Introduction including Data Structure Graph overview. Given in Cincinnati August 18th 2015 as part of the DataSeed Meetup group.
These questions will be a bit advanced level 2sadhana312471
These questions will be a bit advanced(Intermediate) in terms of Python interview.
This is the continuity of Nail the Python Interview Questions.
The fields that these questions will help you in are:
• Python Developer
• Data Analyst
• Research Analyst
• Data Scientist
The document provides an introduction to object oriented programming (OOP) compared to procedural programming. It discusses key concepts in OOP like objects, classes, attributes, methods, encapsulation. Objects contain attributes (data) and methods (behaviors). Classes are templates that define common attributes and methods for a set of objects. Encapsulation involves hiding class data and implementation details through access modifiers like private and public. Examples are provided to demonstrate how to define classes and create objects in C++ code.
This document provides an overview and introduction to deep learning. It discusses motivations for deep learning such as its powerful learning capabilities. It then covers deep learning basics like neural networks, neurons, training processes, and gradient descent. It also discusses different network architectures like convolutional neural networks and recurrent neural networks. Finally, it describes various deep learning applications, tools, and key researchers and companies in the field.
The document presents a Tic Tac Toe game project for Android. It introduces the project, describing it as a two-player game played on a 3x3 grid. It lists the required tools including Java, XML, Android Studio, and Android Virtual Devices. It outlines key features such as displaying whose turn it is and including a restart button. It then describes the development process including key files like the activity_main layout file and MainActivity java file to manage the interface and logic. It concludes by providing a link to the project source files.
TeelTech - Advancing Mobile Device Forensics (online version)Mike Felch
This document provides an overview of a training on advancing mobile device forensics through reverse engineering and programming techniques. It discusses how traditional forensic tools are becoming less effective at recovering data from newer devices and applications that are designed for privacy. The training will demonstrate extracting artifacts from a raw device image using a hex editor and Python scripts. It also outlines a simulated criminal investigation involving the murder of a victim, and how analyzing the digital evidence from the victim and suspect's mobile phones through these new techniques revealed deleted messages that are relevant to the case.
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.
Deep learning is introduced along with its applications and key players in the field. The document discusses the problem space of inputs and outputs for deep learning systems. It describes what deep learning is, providing definitions and explaining the rise of neural networks. Key deep learning architectures like convolutional neural networks are overviewed along with a brief history and motivations for deep learning.
This document provides an overview of object-oriented programming (OOP) including:
- The history and key concepts of OOP like classes, objects, inheritance, polymorphism, and encapsulation.
- Popular OOP languages like C++, Java, and Python.
- Differences between procedural and OOP like top-down design and modularity.
The Ring programming language version 1.8 book - Part 7 of 202Mahmoud Samir Fayed
Ring 1.8 includes several new features and improvements such as better performance, new applications like Find in Files and String2Constant, more 3D samples, compiling on Manjaro Linux, updated libraries, and notes for extension creators. Key updates include 10-100% faster performance, a Find in Files application, a String2Constant tool to convert code to use constants, a StopWatch application, improved Form Designer, RingQt, and code generator for extensions. The release provides better compiler, virtual machine, and overall performance.
This document provides an overview of deep learning and neural networks. It begins with definitions of machine learning, artificial intelligence, and the different types of machine learning problems. It then introduces deep learning, explaining that it uses neural networks with multiple layers to learn representations of data. The document discusses why deep learning works better than traditional machine learning for complex problems. It covers key concepts like activation functions, gradient descent, backpropagation, and overfitting. It also provides examples of applications of deep learning and popular deep learning frameworks like TensorFlow. Overall, the document gives a high-level introduction to deep learning concepts and techniques.
This document provides an introduction to threads, events, and mutexes in C# classes. It begins with a basic example of creating a thread to call a method. Subsequent examples demonstrate passing delegate methods to threads, using the Sleep method to simulate multithreading, and accessing the CurrentThread property. The document also notes that the Thread class is sealed and cannot be inherited from.
The document summarizes the author's work experience and education history. It includes the following key points:
1) The author studied software engineering in Germany from 2002-2008, gaining knowledge in database architecture and skills in areas like DB2 administration.
2) An internship at Intershop Communication AG involved completing a thesis analyzing database parameters and their impact on performance using Oracle.
3) Employment at CNOOC from 2009 involved contributing to a GIS project to store oil exploration data and make it accessible online.
4) After immigrating to Canada in 2013, the author studied game engines like DirectX and OGRE, focusing on techniques like lighting, shadows, and particles to prepare for work
Similar to Computer investigatroy project c++ class 12 (20)
This document studies the effects of sporamin, a protein from sweet potatoes, on human gut cancer cell lines. It finds that sporamin exerts significant anti-proliferative and anti-metastatic effects by inducing apoptosis in human pancreatic, esophageal, and colorectal cancer cell lines. Specifically, sporamin treatment was found to suppress tumor growth through influencing expression of Bcl-2 family proteins and inhibiting the NF-κB pathway. Figures 1-3 show results of experiments demonstrating sporamin's dose-dependent inhibition of cancer cell proliferation and induction of apoptosis.
This document discusses the composition, classification, structure, and functions of connective tissue. It begins by outlining the key elements that make up connective tissue - cells and extracellular matrix composed of ground substance and protein fibers. Connective tissue is then classified into loose connective tissue (areolar, adipose, reticular), dense connective tissue (collagenous, elastic), supporting connective tissue (cartilage, bone), and fluid connective tissue (blood, lymph). Each type is described in terms of its matrix composition and cellular components as well as its typical locations and functions in the body.
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
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Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
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How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
1. UNDER THE GUIDANCE OF:
MRS.JINI
SUBMITTED BY:
G.MEENALOSHINI
CLASS XII A
REGISTER NO:
2. Acknowledgement
In the accomplishment of this project successfully, many people have
best owned upon me their blessings and the heart pledged support.
This time I am utilising to thank all the people who have been
concerned with this project.
Firstly, I would thank god for being able to complete this project with
triumph. Then I would like to thank my parents and family members
who helped me with their valuable suggestions.
Secondly, I would like to express my special thanks of gratitude to my
computer-science facilitator Mrs.Jini as well as our principal Mrs.G.Jigi
Mol who gave me the golden opportunity to do this wonderful project
on the topic ”TETRIS ROYALE-GAME”, which also helped me in doing a
lot of research and I came to know about so many new things.
Last but not the least I would like to thank all my friends and
classmates for their guidance in various phases of the completion of
the project.
Regards,
G.Meenaloshini
3. TABLE OF CONTENTS
S.NO TOPICS
1. INTRODUCTION
2.
SYSTEM OBJECTIVES & AIM OF THE
PROJECT
3. HARDWARE & SOFTWARE REQUIREMENTS
4. THEORY
5. HEADER FILES INCLUDED
6. SOURCE CODE
7. OUTPUT SCREENS
8. BIBLIOGRAPHY
4. INTRODUCTION
Tetris game code with sounds, high scores and gravity. It is intended as a learning tool with its
extensive comments and laid out code with easy get-around and simple functions. The code
explores things such as loops and if statements, patch, save/load, GUI techniques, call-backs and
sound generation.
Description
“Tetris is a game involving dropping blocks. Points are scored for each block which comes to rest
on the gradually collecting pile of blocks. Different blocks in different orientations score different
points. The goal is to prevent the pile from reaching the top and ending the game. Whenever a row
of blocks is completely filled, it is removed and all blocks above it drop down to fill the empty
row.”
Game Walkthrough
A random sequence of tetrominoes (sometimes called "tetrads" in older versions)—shapes
composed of four square blocks each—fall down the playing field (a rectangular vertical shaft,
called the "well" or "matrix"). The object of the game is to manipulate these tetrominoes, by moving
each one sideways and rotating it by 90 degree units, with the aim of creating a horizontal line of
blocks without gaps. When such a line is created, it disappears, and any block above the deleted
line will fall. As the game progresses, the tetrominoes fall faster, and the game ends when the
stack of tetrominoes reaches the top of the playing field and no new tetrominoes are able to enter.
Game Objectives
Complete levels by clearing a set number of lines
Clear lines to stop stack from reaching top of screen
Clear more lines at once for higher point values
Increasing difficulty
As players complete more levels, the difficulty of the game increases. Not only do players keep
the lines that they did not clear from the previous levels, but pieces also begin to fall faster, forcing
the player to think faster in order to clear lines.
5. SYSTEM OBJECTIVES & AIM OF THE PROJECT
The Tetris Effect
The Tetris Effect is a catchy term that players use to
describe the way they are inspired by the game and see
Tetriminos in everyday situations. Because Tetris, like the
real world, challenges players to make order out of chaos
using a specific organization system, the game
components translate easily into lifestyle interpretations.
Whether you're packing the trunk of your car,
loading a dishwasher, or organizing your
shelves, you're likely thinking about how each
object will fit together strategically with
minimal empty space. This is the Tetris Effect!
Learning Objectives
Recognize spatial requirements to clear lines by rotating and dropping pieces
Plan ahead by setting up lines for easy clearing
Adapt to faster falling pieces
Educational Outcomes
Tetris has been one the most recognized and enjoyed video games since its creation 25 years ago. Even today the game still
enjoys popularity as it has gained new interest through platforms such as the iPhone and Facebook. Because of its extensive
social influences, many researchers have attempted measure the effects of Tetris, each with varying results.
Spatial ability is the first area of learning that most people would point to improvement. However in two experiments
conducted by Valerie Sims and Richard Mayer, researchers found that spatial expertise is highly-domain specific. The
experiment compared skilled Tetris players, non-Tetris players, and participants who played Tetris for 12 hours. Researchers
discovered that Tetris players perform better in spatial ability than non-Tetris players when using identical or similar shapes to
Tetris pieces. Tetris players were able to mentally place these shapes faster and often rotated the shapes in alternative
ways. However, Tetris and non-Tetris players displayed little difference when performing spatial ability tests with non-Tetris
shapes.
Researchers at the Mind Research Network discovered differing results. Over a three-month period, researchers studied two
groups of adolescent girls, one group consistently played Tetris while the other did not. After analyzing the girls’ MRIs after
three months, researchers found that the girls who played Tetris displayed changes in their cortex. The increase in cortical
thickness is correlated with an increase in grey matter, which leads to greater brain efficiency when completing complex tasks.
6. HARDWARE REQUIREMENT
COMPONENTS REQUIREMENTS
Processor Intel(R) Pentium(R) CPU N3520 @
2.16GHz
Ram 2 GB
Hard disk drive 465 GB
Graphics memory 775 MB
SOFTWARE ENVIRONMENT
COMPONENTS REQUIREMENTS
Os 64-bit operating
system(WINDOWS 7)
Graphics Intel(R) HD Graphics
Programming Language TURBO C7 (C++)
FILES GENERATED
Text file: tetris.txt
Programme file: tetris.cpp
Object file: tetris.obj
Execution file: tetris.exe
7. C++ is a multi-paradigm programming language that supports object-oriented programming (OOP),
created by Bjarne Stroustrup in 1983 at Bell Labs, C++ is an extension(superset) of C programming and
the programs are written in C language can run in C++ compilers.
C++ is used by programmers to create computer software. It is used to create general systems
software, drivers for various computer devices, software for servers and software for specific applications
and also widely used in the creation of video games.
USES:
C++ is used by many programmers of different types and coming from different fields. C++ is mostly used
to write device driver programs, system software, and applications that depend on direct hardware
manipulation under real-time constraints. It is also used to teach the basics of object-oriented features
because it is simple and is also used in the fields of research. Also, many primary user
interfaces and system files of Windows and Macintosh are written using C++. So, C++ is a popular,
strong and frequently used programming language of this modern programming era.
FEATURES:
The main focus remains on data rather than
procedures.
Object-oriented programs are segmented into
parts called objects.
Data structures are designed to categorize the
objects.
Data member and functions are tied together as a
data structure.
Data can be hidden and cannot be accessed by
external functions using access specifier.
Objects can communicate among themselves
using functions.
New data and functions can be easily added
anywhere within a program whenever required.
Since this is an object-oriented programming language, it follows a bottom up approach, i.e. the
execution of codes starts from the main which resides at the lower section and then based on the
member function call the working is done from the classes.
8. Object Oriented Programming in C++
Object Oriented programming is a programming style that is associated with the concept of Class, Objects and
various other concepts revolving around these two, like Inheritance, Polymorphism, Abstraction, Encapsulation etc.
Encapsulation
Encapsulationis a process of combining data and
function into a single unit like capsule. This is to avoid
the access of private data members from outside the
class. To achieve encapsulation, we make all data
members of class private and create public functions,
using them we can get the values from these data
members or set the value to these data members.
Abstraction:
Abstractionis a process of hiding irrelevant details from
user.
Inheritance
Inheritanceis a feature using which an object of child class acquires the properties of parent class.
9. Polymorphism
Function overloadingand Operator
overloading are examples of polymorphism.
Polymorphism is a feature using which an
object behaves differently in different
situation.
MODULARITY:
Modularity: the written program can be splitted up in to modules by using classes and each class can
be considered as a module.
10. File Handling using File Streams in C++
File represents storage medium for storing data or information. Streams refer to sequence of bytes. In
Files we store data i.e. text or binary data permanently and use these data to read or write in the form of
input output operations by transferring bytes of data. So we use the term File Streams/File handling. We
use the header file <fstream>
ofstream: It represents output Stream and this is used for writing in
files.
ifstream: It represents input Stream and this is used for reading
from files.
fstream: It represents both output Stream and input Stream. So it
can read from files and write to files.
Operations in File Handling:
Creating a file: open()
Reading data: read()
Writing new data: write()
Closing a file: close()
SPECIAL OPERATIONS IN A FILE
There are few important functions to be used with file streams like:
tellp() - It tells the current position of the put pointer.
Syntax: filepointer.tellp()
tellg() - It tells the current position of the get pointer.
Syntax: filepointer.tellg()
seekp() - It moves the put pointer to mentioned location.
Syntax: filepointer.seekp(no of bytes,reference mode)
seekg() - It moves get pointer(input) to a specified
location.
Syntax: filepointer.seekg((no of bytes,reference point)
put() - It writes a single character to file.
get() - It reads a single character from file.
11. Header files included
///PreDefined HeaderFile
#include<iostream.h> Used as a stream of Input and Output.
#include<graphics.h> C graphics using graphics.h functions can be used to draw
different shapes, display text in different fonts, change colors and many more.
#include<stdlib.h> Perform standard utility functions like dynamic memory
allocation.
#include<conio.h> Perform console input and console output operations like clrscr()
to clear the screen and getch() to get the character from the keyboard.
#include<process.h> C header file which contains function declarations and macros
used in working with threads and processes.
#include<dos.h> header file of C language contains functions for handling
interrupts, producing sound, date and time functions etc.
#include<string.h> Perform string manipulation operations like strlen and strcpy.
#include<fstream.h> Contains function prototypes for functions that perform input
from files on disk and output to files on disk.
///User Defined Header File(modularity concept)
#include"C:TCbinsecond.cpp"
#include"C:TCincludekeyb.h"
#include"C:TCbinmainclas.cpp"
#include"C:TCbinlineobj.cpp"
#include"C:TCbinsqureobj.cpp"
#include"C:TCbintemp.cpp"
24. BOOL ch=DetectCollision(KEY_UP,temp);//checking new position is valid
if(ch==FALSE)return;
pos=LLightStanding;
}
else if(pos==LLightStanding)
{
temp[0].y=temp[0].y-1;
temp[0].x=temp[0].x+2;
temp[1].y=temp[1].y;
temp[1].x=temp[1].x+1;
temp[2].y=temp[2].y-1;
temp[2].x=temp[2].x;
temp[3].y=temp[3].y;
temp[3].x=temp[3].x-1;
BOOL ch=DetectCollision(KEY_UP,temp);//checking new position is valid
if(ch==FALSE)return;
pos=LLightSleep;
}
for(i=0;i<4;i++)
ShapeCoordinate[i]=temp[i];//sending information back to main shape
}
void ObjectRLighting::RotateObject()
{
coordinate temp[4];
for(int i=0;i<4;i++)
temp[i]=ShapeCoordinate[i];
if(pos==RLightSleep)
{
temp[0].y=temp[0].y-1;
temp[0].x=temp[0].x-2;
temp[1].y=temp[1].y;
temp[1].x=temp[1].x-1;
temp[2].y=temp[2].y-1;
temp[2].x=temp[2].x;
temp[3].y=temp[3].y;
temp[3].x=temp[3].x+1;
25. BOOL ch=DetectCollision(KEY_UP,temp);//checking new position is valid
if(ch==FALSE)return;
pos=RLightStanding;
}
else
if(pos==RLightStanding)
{
temp[0].y=temp[0].y+1;
temp[0].x=temp[0].x+2;
temp[1].y=temp[1].y;
temp[1].x=temp[1].x+1;
temp[2].y=temp[2].y+1;
temp[2].x=temp[2].x;
temp[3].y=temp[3].y;
temp[3].x=temp[3].x-1;
BOOL ch=DetectCollision(KEY_UP,temp);//checking new position is valid
if(ch==FALSE)return;
pos=RLightSleep;
}
for(i=0;i<4;i++)
ShapeCoordinate[i]=temp[i];//sending information back to main shape
}
void ObjectCross::RotateObject()
{
coordinate temp[4];
for(int i=0;i<4;i++)
temp[i]=ShapeCoordinate[i];
BOOL ch;
switch(pos)
{
case Cross1:
temp[0].x=temp[0].x+1;
temp[0].y=temp[0].y+1;
temp[1].x=temp[1].x-1;
temp[1].y=temp[1].y+1;
temp[2].x=temp[2].x;
temp[2].y=temp[2].y;
temp[3].x=temp[3].x+1;
temp[3].y=temp[3].y-1;
26. ch=DetectCollision(KEY_UP,temp);//checking new position is valid
if(ch==FALSE)return;
pos=Cross2;
break;
case Cross2:
temp[0].x=temp[0].x+1;
temp[0].y=temp[0].y-1;
temp[1].x=temp[1].x+1;
temp[1].y=temp[1].y+1;
temp[2].x=temp[2].x;
temp[2].y=temp[2].y;
temp[3].x=temp[3].x-1;
temp[3].y=temp[3].y-1;
ch=DetectCollision(KEY_UP,temp);//checking new position is valid if(ch==FALSE)return;
pos=Cross3;
break;
case Cross3:
temp[0].x=temp[0].x-1;
temp[0].y=temp[0].y-1;
temp[1].x=temp[1].x+1;
temp[1].y=temp[1].y-1;
temp[2].x=temp[2].x;
temp[2].y=temp[2].y;
temp[3].x=temp[3].x-1;
temp[3].y=temp[3].y+1;
ch=DetectCollision(KEY_UP,temp);//checking new position is valid
if(ch==FALSE)return;
pos=Cross4;
break;
case Cross4:
temp[0].x=temp[0].x-1;
temp[0].y=temp[0].y+1;
temp[1].x=temp[1].x-1;
temp[1].y=temp[1].y-1;
temp[2].x=temp[2].x;
temp[2].y=temp[2].y;
27. temp[3].x=temp[3].x+1;
temp[3].y=temp[3].y+1;
ch=DetectCollision(KEY_UP,temp);//checking new position is valid
if(ch==FALSE)return;
pos=Cross1;
break;
}
for(i=0;i<4;i++)
ShapeCoordinate[i]=temp[i];//sending information back to main shape
}
void ObjectRL::RotateObject()
{
coordinate temp[4];
for(int i=0;i<4;i++)
temp[i]=ShapeCoordinate[i];
BOOL ch;
switch(pos)
{
case LL1:
temp[0].x=temp[0].x+2;
temp[0].y=temp[0].y;
temp[1].x=temp[1].x-1;
temp[1].y=temp[1].y+1;
temp[2].x=temp[2].x;
temp[2].y=temp[2].y;
temp[3].x=temp[3].x+1;
temp[3].y=temp[3].y-1;
ch=DetectCollision(KEY_UP,temp);//checking new position is valid
if(ch==FALSE)return;
pos=LL2;
break;
case LL2:
temp[0].x=temp[0].x;
temp[0].y=temp[0].y-2;
temp[1].x=temp[1].x+1;
temp[1].y=temp[1].y+1;
28. temp[2].x=temp[2].x;
temp[2].y=temp[2].y;
temp[3].x=temp[3].x-1;
temp[3].y=temp[3].y-1;
ch=DetectCollision(KEY_UP,temp);//checking new position is valid if(ch==FALSE)return;
pos=LL3;
break;
case LL3:
temp[0].x=temp[0].x-2;
temp[0].y=temp[0].y;
temp[1].x=temp[1].x+1;
temp[1].y=temp[1].y-1;
temp[2].x=temp[2].x;
temp[2].y=temp[2].y;
temp[3].x=temp[3].x-1;
temp[3].y=temp[3].y+1;
ch=DetectCollision(KEY_UP,temp);//checking new position is valid
if(ch==FALSE)return;
pos=LL4;
break;
case LL4:
temp[0].x=temp[0].x;
temp[0].y=temp[0].y+2;
temp[1].x=temp[1].x-1;
temp[1].y=temp[1].y-1;
temp[2].x=temp[2].x;
temp[2].y=temp[2].y;
temp[3].x=temp[3].x+1;
temp[3].y=temp[3].y+1;
ch=DetectCollision(KEY_UP,temp);//checking new position is valid
if(ch==FALSE)return;
pos=LL1;
break;
}
for(i=0;i<4;i++)
ShapeCoordinate[i]=temp[i];//sending information back to main shape
29. }
void ObjectLL::RotateObject()
{
coordinate temp[4];
for(int i=0;i<4;i++)
temp[i]=ShapeCoordinate[i];
BOOL ch;
switch(pos)
{
case Cross1:
temp[0].x=temp[0].x;
temp[0].y=temp[0].y+2;
temp[1].x=temp[1].x-1;
temp[1].y=temp[1].y+1;
temp[2].x=temp[2].x;
temp[2].y=temp[2].y;
temp[3].x=temp[3].x+1;
temp[3].y=temp[3].y-1;
ch=DetectCollision(KEY_UP,temp);//checking new position is valid
if(ch==FALSE)return;
pos=Cross2;
break;
case Cross2:
temp[0].x=temp[0].x+2;
temp[0].y=temp[0].y;
temp[1].x=temp[1].x+1;
temp[1].y=temp[1].y+1;
temp[2].x=temp[2].x;
temp[2].y=temp[2].y;
temp[3].x=temp[3].x-1;
temp[3].y=temp[3].y-1;
ch=DetectCollision(KEY_UP,temp);//checking new position is valid if(ch==FALSE)return;
pos=Cross3;
break;
case Cross3:
36. temp[1].x=temp[0].x;
temp[2].y=temp[0].y+2;
temp[2].x=temp[0].x;
temp[3].y=temp[0].y+3;
temp[3].x=temp[0].x;
BOOL ch=DetectCollision(KEY_UP,temp);//checking new position is valid
if(ch==FALSE)return;
pos=SLEEP;
}
for(i=0;i<4;i++)
ShapeCoordinate[i]=temp[i];//sending information back to main shape
}
MAINCLAS CPP FILE:
void MainClass::DrawScreen()
{
setfillstyle(1,LIGHTGRAY); //setting initial color to grey
bar(50,30,370,450);
setfillstyle(1,0);
bar(56,36,364,444);
setcolor(DARKGRAY); //setting color white to give 3d look
rectangle(55,35,364,445);
rectangle(52,32,367,448);
setcolor(WHITE);
rectangle(53,33,366,447);
rectangle(50,30,369,450);
///Darwing SideBlock Containing Other Information
setcolor(DARKGRAY); //setting color white to give 3d look
rectangle(405,35,604,445);
rectangle(402,32,607,448);
line(405,290,604,290);
line(405,293,604,293);
line(405,180,604,180);
line(405,184,604,184);
line(500,180,500,290);
line(504,184,504,294);
setcolor(LIGHTGRAY);
rectangle(404,34,605,446);
rectangle(401,31,608,449);
line(405,291,604,291);
line(405,293,604,293);
line(405,181,604,181);
line(405,183,604,183);