What is in you
•Download as PPTX, PDF•
1 like•538 views
The document provides tips for developing a positive attitude and self-confidence. It advises avoiding negative thoughts and focusing instead on cultivating a positive mindset. Some key recommendations include being self-confident, open-minded, leading oneself through analysis and information gathering, thinking creatively, working as a team, and acting rather than just reacting. The overall message is the importance of avoiding negativity and remembering to keep an optimistic outlook.
Report
Share
Report
Share
Recommended
B7aaddce
18 teachers and 37 parents from Lithuania were surveyed about various aspects of education. The teachers agreed that they try to perform effective learning through various teaching methods and techniques and ensure student participation in lessons. They also agreed that they provide information technology resources and activities to improve student skills. The parents generally agreed that their child's school is a good learning environment, that the teacher is aware of their child's needs, and that games can support learning in the classroom.
Instance based learning
Instance-based learning, also known as lazy learning, is a non-parametric learning method where the training data is stored and a new instance is classified based on its similarity to the nearest stored instances. It is similar to a desktop in that all data is kept in memory. The key aspects are setting the K value for the K-nearest neighbors algorithm and the distance metric such as Euclidean distance. Training involves storing all input data, finding the K nearest neighbors of each test instance, and classifying based on the majority class of those neighbors.
Resolution(decision)
The document discusses resolution, a technique for automated theorem proving in logic. It begins by explaining how to convert first-order logic statements to conjunctive normal form. It then describes the resolution inference rule and how it allows logical statements to be resolved through unification of complementary literals. Additional topics covered include dealing with equality, resolution strategies to improve efficiency, and examples demonstrating how resolution can be used to prove logical statements.
Report generation
This document provides instructions for generating a report from a student database table in Oracle SQL, including creating a table, spooling output to a file, formatting column headers and data, adding titles, and changing number formats. The instructions should be typed into a .sql file and run from the command prompt to generate a formatted report with student names and IDs.
Security in Relational model
This document discusses database security and the use of GRANT and REVOKE statements in SQL. It defines authorization identifiers as database users assigned by the DBA. The owner of an object can grant privileges on it to other users using GRANT, and revoke those privileges using REVOKE. GRANT allows specifying privileges like SELECT, INSERT, UPDATE, DELETE etc. and REVOKE removes privileges that were previously granted. Both statements identify the user or users, object, and privileges involved.
Neural networks
Neural networks are composed of simple processing units (neurons) that are interconnected and can learn from data. Natural neural networks in the brain contain billions of neurons that communicate via electrochemical signals. Early artificial neural networks modeled neurons as simple processing units that sum their weighted inputs and use an activation function to determine their output. These networks had limitations in what functions they could represent. The development of multi-layer perceptrons overcame these limitations by introducing hidden layers that increased their computational and representational power.
Recommended
B7aaddce
18 teachers and 37 parents from Lithuania were surveyed about various aspects of education. The teachers agreed that they try to perform effective learning through various teaching methods and techniques and ensure student participation in lessons. They also agreed that they provide information technology resources and activities to improve student skills. The parents generally agreed that their child's school is a good learning environment, that the teacher is aware of their child's needs, and that games can support learning in the classroom.
Instance based learning
Instance-based learning, also known as lazy learning, is a non-parametric learning method where the training data is stored and a new instance is classified based on its similarity to the nearest stored instances. It is similar to a desktop in that all data is kept in memory. The key aspects are setting the K value for the K-nearest neighbors algorithm and the distance metric such as Euclidean distance. Training involves storing all input data, finding the K nearest neighbors of each test instance, and classifying based on the majority class of those neighbors.
Resolution(decision)
The document discusses resolution, a technique for automated theorem proving in logic. It begins by explaining how to convert first-order logic statements to conjunctive normal form. It then describes the resolution inference rule and how it allows logical statements to be resolved through unification of complementary literals. Additional topics covered include dealing with equality, resolution strategies to improve efficiency, and examples demonstrating how resolution can be used to prove logical statements.
Report generation
This document provides instructions for generating a report from a student database table in Oracle SQL, including creating a table, spooling output to a file, formatting column headers and data, adding titles, and changing number formats. The instructions should be typed into a .sql file and run from the command prompt to generate a formatted report with student names and IDs.
Security in Relational model
This document discusses database security and the use of GRANT and REVOKE statements in SQL. It defines authorization identifiers as database users assigned by the DBA. The owner of an object can grant privileges on it to other users using GRANT, and revoke those privileges using REVOKE. GRANT allows specifying privileges like SELECT, INSERT, UPDATE, DELETE etc. and REVOKE removes privileges that were previously granted. Both statements identify the user or users, object, and privileges involved.
Neural networks
Neural networks are composed of simple processing units (neurons) that are interconnected and can learn from data. Natural neural networks in the brain contain billions of neurons that communicate via electrochemical signals. Early artificial neural networks modeled neurons as simple processing units that sum their weighted inputs and use an activation function to determine their output. These networks had limitations in what functions they could represent. The development of multi-layer perceptrons overcame these limitations by introducing hidden layers that increased their computational and representational power.
Logic agent
This document provides an overview of logical agents and knowledge representation. It discusses knowledge-based agents and the Wumpus world example. It introduces propositional logic and various inference techniques like forward chaining, backward chaining, and resolution that can be used for automated reasoning. It also discusses concepts like entailment, models, validity, and satisfiability. Finally, it discusses how these logical concepts can be applied to build an agent that reasons about the Wumpus world using propositional logic.
Data preprocessing
This document discusses techniques for preprocessing data including data cleaning, transformation, integration, and reduction. Data cleaning removes noise and inconsistencies by filling missing values and identifying outliers. Data integration merges data from multiple sources while dealing with issues like different naming conventions. Data transformation techniques include normalization, aggregation, generalization, attribute selection, and dimensionality reduction to reduce data size and handle inconsistencies. Preprocessing is needed to clean noisy, inconsistent data and handle incomplete data from various sources.
Crystal report generation in visual studio 2010
This document provides steps to generate Crystal Reports in Visual Studio 2010 using a Microsoft Access database. The steps include: 1) opening Visual Studio 2010, adding a report viewer form, and connecting to the Access database; 2) designing a new report using the database configuration wizard to select the dataset and fields; and 3) viewing the final report with data from the Access database displayed.
Statistical learning
The document discusses statistical learning approaches like Naive Bayes and Bayesian networks. It provides an example of using Bayesian learning to predict the flavor of candy in a bag based on observations, calculating the probability of hypotheses given data. The document also covers parameter estimation, the naive Bayes assumption of conditional independence between variables, and using maximum likelihood estimates from training data to learn probabilities.
Propositional logic & inference
Propositional logic deals with propositions as units and the connectives that relate them. It has a syntax that defines allowable sentences using proposition symbols and logical connectives like conjunction, disjunction, implication and equivalence. Sentences are formed using Backus Naur Form grammar. Semantics specify how to compute the truth of sentences using truth tables and models. Knowledge bases can be represented as a set of sentences and inference is used to decide if conclusions are true in all models where the KB is true, such as using a truth table algorithm.
Major issues in data mining
The document discusses major issues in data mining including mining methodology, user interaction, performance, and data types. Specifically, it outlines challenges of mining different types of knowledge, interactive mining at multiple levels of abstraction, incorporating background knowledge, visualization of results, handling noisy data, evaluating pattern interestingness, efficiency and scalability of algorithms, parallel and distributed mining, and handling relational and complex data types from heterogeneous databases.
Trigger
Triggers are stored procedures that are automatically executed in response to certain events occurring on a particular table or view in a database, such as insert, update or delete statements. Triggers consist of an event, condition, and action. The event specifies when the trigger should fire, the condition is an optional filter to determine whether the trigger action should execute, and the action contains SQL statements or code that will execute in response to the triggering event if the condition evaluates to true. Triggers allow data integrity checks, auditing, and other actions to be performed automatically in response to data changes.
Logical reasoning
This document discusses logical agents and their use of knowledge representation and reasoning. It covers knowledge-based agents, the Wumpus World environment, and logic. Knowledge-based agents contain a knowledge base that represents knowledge using sentences in a formal language. The agent can add new facts and query its knowledge. Wumpus World is a classic environment for testing logical agents, with glitter, breeze, and smell sensors and actions like moving, shooting, and grabbing. Logic involves the syntax, semantics, and entailment of sentences in a knowledge base.
Entity Relationship Model
The document provides an overview of entity-relationship (ER) modeling concepts used in database design. It defines key terms like entities, attributes, relationships, and cardinalities. It explains how ER diagrams visually represent these concepts using symbols like rectangles, diamonds, and lines. The document also discusses entity types, relationship degrees, key attributes, weak entities, and how to model one-to-one, one-to-many, many-to-one, and many-to-many relationships. Overall, the document serves as a guide to basic ER modeling principles for conceptual database design.
OLAP
OLAP provides multidimensional analysis of large datasets to help solve business problems. It uses a multidimensional data model to allow for drilling down and across different dimensions like students, exams, departments, and colleges. OLAP tools are classified as MOLAP, ROLAP, or HOLAP based on how they store and access multidimensional data. MOLAP uses a multidimensional database for fast performance while ROLAP accesses relational databases through metadata. HOLAP provides some analysis directly on relational data or through intermediate MOLAP storage. Web-enabled OLAP allows interactive querying over the internet.
Neural networks
This document discusses neural networks and their biological and technical underpinnings. It covers how natural neural networks operate using electrochemical signals and thresholds. It also discusses early artificial neural network models like McCulloch-Pitts networks and perceptrons. Perceptrons are defined as single-layer feedforward networks and can only represent linearly separable functions. The document introduces the concept of adding hidden layers to networks to increase their computational power and ability to represent more complex functions like XOR.
Logical reasoning 21.1.13
The document discusses logical agents and their components. It describes how logical agents use knowledge representation and reasoning to solve problems and generate new knowledge. It then discusses knowledge-based agents specifically, noting they have a knowledge base that stores facts and can be queried. The document also summarizes the classic "Wumpus World" environment and how logic can be applied in that domain to reason about the agent's surroundings based on its perceptions. It concludes by defining key logical concepts like syntax, semantics, entailment, and sound inference algorithms.
Statistical learning
This document discusses statistical learning approaches, including Naive Bayes classification. It provides an example of predicting the flavor of candy from different bags based on prior probabilities. It explains how Bayesian learning uses all hypotheses weighted by their probabilities to make predictions. The document also discusses Naive Bayes, which makes a strong independence assumption to simplify probability calculations for diagnosis problems using symptoms. It provides an example of using symptom probabilities learned from training data to determine the most likely diagnosis.
Reinforcement learning 7313
This document discusses reinforcement learning. It defines reinforcement learning as a learning method where an agent learns how to behave via interactions with an environment. The agent receives rewards or penalties based on its actions but is not told which actions are correct. Several reinforcement learning concepts and algorithms are covered, including model-based vs model-free approaches, passive vs active learning, temporal difference learning, adaptive dynamic programming, and exploration-exploitation tradeoffs. Generalization methods like function approximation and genetic algorithms are also briefly mentioned.
Instance based learning
Instance-based learning stores all training instances and classifies new instances based on their similarity to stored examples as determined by a distance metric, typically Euclidean distance. It is a non-parametric approach where the hypothesis complexity grows with the amount of data. K-nearest neighbors specifically finds the K most similar training examples to a new instance and assigns the most common class among those K neighbors. Key aspects are choosing the value of K and the distance metric to evaluate similarity between instances.
Input & output devices
1. The document discusses various input and output devices for computers. Common input devices include keyboards, mice, touch screens, joysticks, and scanners.
2. Keyboards allow text input and use a QWERTY layout. Mice are used to select menus and interact with programs. Touch screens can be optical or electrical.
3. Common output devices are monitors and printers. Monitors can be CRT, flat panel, monochrome, grayscale or color. Printers include dot matrix, inkjet, and laser printers.
Accessing I/O Devices
The document discusses input/output (I/O) interfaces. An I/O interface is required for communication between the CPU, I/O devices, and memory. It performs data buffering, control and timing, and error detection. There are two main techniques for I/O interfacing - memory mapped I/O and I/O mapped I/O. Programmed I/O is an approach where the CPU polls I/O devices by checking their status periodically to see when operations complete.
Basic Processing Unit
The document discusses the basic processing unit of a computer. It describes the objective, fundamental concepts, and components of a processor including the datapath, control unit, instruction cycle of fetch, decode, and execute. It explains the concepts of registers, arithmetic logic unit (ALU), and how instructions are executed through register transfers, arithmetic/logic operations, and reading/writing from memory. It also compares single-bus and multiple-bus processor organizations.
Cache performance considerations
- A key objective of computer systems is achieving high performance at low cost, measured by price/performance ratio.
- Processor performance depends on how fast instructions can be fetched from memory and executed.
- Caches improve performance by storing recently accessed data from main memory closer to the processor, reducing access time compared to main memory. This can increase hit rates but requires managing cache misses and write policies.
Cachememory
Cache memory is used to improve processor performance by making main memory access appear faster. It works based on the principle of locality of reference, where programs tend to access the same data/instructions repeatedly. A cache hit provides faster access than main memory, while a miss requires retrieving data from main memory. Caches use mapping functions like direct, associative, or set-associative mapping to determine where to place blocks of data from main memory.
Memory management
The document discusses memory management in operating systems. It explains that the operating system is concerned with transferring programs and data between secondary storage and main memory. It divides virtual address space into a system space for the operating system routines and multiple user spaces for application programs. The Memory Management Unit translates virtual addresses to physical addresses using a page table pointed to by the page table base register, allowing only one address space to be accessible at a time. The processor has supervisor and user states to prevent user programs from modifying each other's memory spaces or changing the page table base register.
More Related Content
Viewers also liked
Logic agent
This document provides an overview of logical agents and knowledge representation. It discusses knowledge-based agents and the Wumpus world example. It introduces propositional logic and various inference techniques like forward chaining, backward chaining, and resolution that can be used for automated reasoning. It also discusses concepts like entailment, models, validity, and satisfiability. Finally, it discusses how these logical concepts can be applied to build an agent that reasons about the Wumpus world using propositional logic.
Data preprocessing
This document discusses techniques for preprocessing data including data cleaning, transformation, integration, and reduction. Data cleaning removes noise and inconsistencies by filling missing values and identifying outliers. Data integration merges data from multiple sources while dealing with issues like different naming conventions. Data transformation techniques include normalization, aggregation, generalization, attribute selection, and dimensionality reduction to reduce data size and handle inconsistencies. Preprocessing is needed to clean noisy, inconsistent data and handle incomplete data from various sources.
Crystal report generation in visual studio 2010
This document provides steps to generate Crystal Reports in Visual Studio 2010 using a Microsoft Access database. The steps include: 1) opening Visual Studio 2010, adding a report viewer form, and connecting to the Access database; 2) designing a new report using the database configuration wizard to select the dataset and fields; and 3) viewing the final report with data from the Access database displayed.
Statistical learning
The document discusses statistical learning approaches like Naive Bayes and Bayesian networks. It provides an example of using Bayesian learning to predict the flavor of candy in a bag based on observations, calculating the probability of hypotheses given data. The document also covers parameter estimation, the naive Bayes assumption of conditional independence between variables, and using maximum likelihood estimates from training data to learn probabilities.
Propositional logic & inference
Propositional logic deals with propositions as units and the connectives that relate them. It has a syntax that defines allowable sentences using proposition symbols and logical connectives like conjunction, disjunction, implication and equivalence. Sentences are formed using Backus Naur Form grammar. Semantics specify how to compute the truth of sentences using truth tables and models. Knowledge bases can be represented as a set of sentences and inference is used to decide if conclusions are true in all models where the KB is true, such as using a truth table algorithm.
Major issues in data mining
The document discusses major issues in data mining including mining methodology, user interaction, performance, and data types. Specifically, it outlines challenges of mining different types of knowledge, interactive mining at multiple levels of abstraction, incorporating background knowledge, visualization of results, handling noisy data, evaluating pattern interestingness, efficiency and scalability of algorithms, parallel and distributed mining, and handling relational and complex data types from heterogeneous databases.
Trigger
Triggers are stored procedures that are automatically executed in response to certain events occurring on a particular table or view in a database, such as insert, update or delete statements. Triggers consist of an event, condition, and action. The event specifies when the trigger should fire, the condition is an optional filter to determine whether the trigger action should execute, and the action contains SQL statements or code that will execute in response to the triggering event if the condition evaluates to true. Triggers allow data integrity checks, auditing, and other actions to be performed automatically in response to data changes.
Logical reasoning
This document discusses logical agents and their use of knowledge representation and reasoning. It covers knowledge-based agents, the Wumpus World environment, and logic. Knowledge-based agents contain a knowledge base that represents knowledge using sentences in a formal language. The agent can add new facts and query its knowledge. Wumpus World is a classic environment for testing logical agents, with glitter, breeze, and smell sensors and actions like moving, shooting, and grabbing. Logic involves the syntax, semantics, and entailment of sentences in a knowledge base.
Entity Relationship Model
The document provides an overview of entity-relationship (ER) modeling concepts used in database design. It defines key terms like entities, attributes, relationships, and cardinalities. It explains how ER diagrams visually represent these concepts using symbols like rectangles, diamonds, and lines. The document also discusses entity types, relationship degrees, key attributes, weak entities, and how to model one-to-one, one-to-many, many-to-one, and many-to-many relationships. Overall, the document serves as a guide to basic ER modeling principles for conceptual database design.
OLAP
OLAP provides multidimensional analysis of large datasets to help solve business problems. It uses a multidimensional data model to allow for drilling down and across different dimensions like students, exams, departments, and colleges. OLAP tools are classified as MOLAP, ROLAP, or HOLAP based on how they store and access multidimensional data. MOLAP uses a multidimensional database for fast performance while ROLAP accesses relational databases through metadata. HOLAP provides some analysis directly on relational data or through intermediate MOLAP storage. Web-enabled OLAP allows interactive querying over the internet.
Neural networks
This document discusses neural networks and their biological and technical underpinnings. It covers how natural neural networks operate using electrochemical signals and thresholds. It also discusses early artificial neural network models like McCulloch-Pitts networks and perceptrons. Perceptrons are defined as single-layer feedforward networks and can only represent linearly separable functions. The document introduces the concept of adding hidden layers to networks to increase their computational power and ability to represent more complex functions like XOR.
Viewers also liked (11)
More from Slideshare
Logical reasoning 21.1.13
The document discusses logical agents and their components. It describes how logical agents use knowledge representation and reasoning to solve problems and generate new knowledge. It then discusses knowledge-based agents specifically, noting they have a knowledge base that stores facts and can be queried. The document also summarizes the classic "Wumpus World" environment and how logic can be applied in that domain to reason about the agent's surroundings based on its perceptions. It concludes by defining key logical concepts like syntax, semantics, entailment, and sound inference algorithms.
Statistical learning
This document discusses statistical learning approaches, including Naive Bayes classification. It provides an example of predicting the flavor of candy from different bags based on prior probabilities. It explains how Bayesian learning uses all hypotheses weighted by their probabilities to make predictions. The document also discusses Naive Bayes, which makes a strong independence assumption to simplify probability calculations for diagnosis problems using symptoms. It provides an example of using symptom probabilities learned from training data to determine the most likely diagnosis.
Reinforcement learning 7313
This document discusses reinforcement learning. It defines reinforcement learning as a learning method where an agent learns how to behave via interactions with an environment. The agent receives rewards or penalties based on its actions but is not told which actions are correct. Several reinforcement learning concepts and algorithms are covered, including model-based vs model-free approaches, passive vs active learning, temporal difference learning, adaptive dynamic programming, and exploration-exploitation tradeoffs. Generalization methods like function approximation and genetic algorithms are also briefly mentioned.
Instance based learning
Instance-based learning stores all training instances and classifies new instances based on their similarity to stored examples as determined by a distance metric, typically Euclidean distance. It is a non-parametric approach where the hypothesis complexity grows with the amount of data. K-nearest neighbors specifically finds the K most similar training examples to a new instance and assigns the most common class among those K neighbors. Key aspects are choosing the value of K and the distance metric to evaluate similarity between instances.
Input & output devices
1. The document discusses various input and output devices for computers. Common input devices include keyboards, mice, touch screens, joysticks, and scanners.
2. Keyboards allow text input and use a QWERTY layout. Mice are used to select menus and interact with programs. Touch screens can be optical or electrical.
3. Common output devices are monitors and printers. Monitors can be CRT, flat panel, monochrome, grayscale or color. Printers include dot matrix, inkjet, and laser printers.
Accessing I/O Devices
The document discusses input/output (I/O) interfaces. An I/O interface is required for communication between the CPU, I/O devices, and memory. It performs data buffering, control and timing, and error detection. There are two main techniques for I/O interfacing - memory mapped I/O and I/O mapped I/O. Programmed I/O is an approach where the CPU polls I/O devices by checking their status periodically to see when operations complete.
Basic Processing Unit
The document discusses the basic processing unit of a computer. It describes the objective, fundamental concepts, and components of a processor including the datapath, control unit, instruction cycle of fetch, decode, and execute. It explains the concepts of registers, arithmetic logic unit (ALU), and how instructions are executed through register transfers, arithmetic/logic operations, and reading/writing from memory. It also compares single-bus and multiple-bus processor organizations.
Cache performance considerations
- A key objective of computer systems is achieving high performance at low cost, measured by price/performance ratio.
- Processor performance depends on how fast instructions can be fetched from memory and executed.
- Caches improve performance by storing recently accessed data from main memory closer to the processor, reducing access time compared to main memory. This can increase hit rates but requires managing cache misses and write policies.
Cachememory
Cache memory is used to improve processor performance by making main memory access appear faster. It works based on the principle of locality of reference, where programs tend to access the same data/instructions repeatedly. A cache hit provides faster access than main memory, while a miss requires retrieving data from main memory. Caches use mapping functions like direct, associative, or set-associative mapping to determine where to place blocks of data from main memory.
Memory management
The document discusses memory management in operating systems. It explains that the operating system is concerned with transferring programs and data between secondary storage and main memory. It divides virtual address space into a system space for the operating system routines and multiple user spaces for application programs. The Memory Management Unit translates virtual addresses to physical addresses using a page table pointed to by the page table base register, allowing only one address space to be accessible at a time. The processor has supervisor and user states to prevent user programs from modifying each other's memory spaces or changing the page table base register.
Secondary storage devices
The document summarizes secondary storage devices, including magnetic disks and optical disks. Magnetic disks store data on circular platters that rotate rapidly. Data is written to and read from the disks using read/write heads. Disks are organized into tracks, sectors, cylinders, and clusters. Accessing data involves seek time, rotational latency, and transfer time. Optical disks like CD-ROMs encode data as pits and lands that are read using a laser. CD-ROMs organize data into sectors along a spiral track to take advantage of all storage space.
Magnetic tape system
Magnetic tape systems store large amounts of data using magnetic tape. Data is recorded across the width of the tape in parallel using multiple read/write heads. Data is organized into records separated by gaps and grouped into files marked by file marks. Controller commands are used to position and manage the tape, such as rewinding, spacing between records and files, and writing tape marks. Tape systems are useful for data storage and backup due to their large capacity.
More from Slideshare (13)
Recently uploaded
The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
Hindi varnamala | hindi alphabet PPT.pdf
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...Nguyen Thanh Tu Collection
https://app.box.com/s/tacvl9ekroe9hqupdnjruiypvm9rdaneবাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
Physical pharmaceutics notes for B.pharm students
Advanced Java[Extra Concepts, Not Difficult].docx
This is part 2 of my Java Learning Journey. This contains Hashing, ArrayList, LinkedList, Date and Time Classes, Calendar Class and more.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
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.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
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
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...Nguyen Thanh Tu Collection
https://app.box.com/s/y977uz6bpd3af4qsebv7r9b7s21935vdHow to Add Chatter in the odoo 17 ERP Module
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
How to Create a More Engaging and Human Online Learning Experience
How to Create a More Engaging and Human Online Learning Experience Wahiba Chair Training & Consulting
Wahiba Chair's Talk at the 2024 Learning Ideas Conference. ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Build a Module in Odoo 17 Using the Scaffold Method
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Fix the Import Error in the Odoo 17
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.
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)Academy of Science of South Africa
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.Recently uploaded (20)
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
How to Create a More Engaging and Human Online Learning Experience
How to Create a More Engaging and Human Online Learning Experience
How to Build a Module in Odoo 17 Using the Scaffold Method
How to Build a Module in Odoo 17 Using the Scaffold Method
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)
What is in you
- 1. What is in you??? Ms. V. Saranya M.E., AP / CSE, Sri Vidya College of Engg and Tech, Virudhunagar
- 2. 2 1. Avoid what is with you
- 7. 7
- 8. 8
- 9. 9
- 10. 10
- 12. search 12
- 15. 15
- 16. Team work 16
- 17. Act – React 17
- 18. 18
- 19. 19
- 20. 20 Avoid
- 21. 21
- 22. 22
- 23. 23 Always remember one thing that….