Latihan 03
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The document shows that when a number is cubed (multiplied by itself three times), the results are: 1, 8, 27, 64, 125, 216, 343, 512, 729, 1000. These numbers resulting from cubing integers 1 through 10 are called cubic numbers. A slide was then created using shapes to increment a variable called BIL from an initial value of 10 by 1 each time until the slide ends.
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The document lists the cubes of the integers from 1 to 10. It shows that these numbers can be written as the integer cubed, such as 1^3 = 1, 2^3 = 8, and so on up to 10^3 = 1000. It then provides examples of the cubic numbers from 1 to 10 as a mathematical pattern involving exponents.3.1 power point
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The document shows the multiplication of numbers 1 through 10 by themselves, with the results being 1, 8, 27, 64, 125, 216, 343, 512, 729, and 1000. It then states these are called cubic numbers. It provides some additional mathematical operations and formulas for perimeter of shapes including rectangles, squares, and triangles.Tugas 1
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The document shows that when a number is cubed (multiplied by itself three times), it produces the following results: 1^3 = 1, 2^3 = 8, 3^3 = 27, 4^3 = 64, 5^3 = 125, 6^3 = 216, 7^3 = 343, 8^3 = 512, 9^3 = 729, 10^3 = 1000. These numbers resulting from cubing integers are called cubic numbers.Soal latihan Powerpoint
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The document provides examples of cube numbers from 1 to 10. It lists the cube of each integer from 1 to 10, which are the results of multiplying an integer by itself three times. The cube numbers are: 1, 8, 27, 64, 125, 216, 343, 512, 729, 1000. These numbers are also called cubic numbers.Latihan
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The document shows that when numbers from 1 to 10 are cubed (multiplied by themselves three times), they produce the cubic numbers 1, 8, 27, 64, 125, 216, 343, 512, 729, and 1000. These numbers are also called cubic numbers. The document then provides examples of perimeter formulas for rectangles, squares, and triangles.Latihan
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The document shows the multiplication of numbers 1 through 10 by themselves, with the results being 1, 8, 27, 64, 125, 216, 343, 512, 729, 1000. These numbers are called cubic numbers. A pseudocode is then shown for a program that would print the cubic numbers from 1 to 10. Finally, basic formulas are shown for calculating the perimeter of a rectangle, square, and triangle.Tugas 1
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**Data Privacy Presentation**
**Introduction**
In the digital age, data privacy has become a crucial topic for individuals and organizations. As technology advances, the amount of personal and sensitive data being collected, stored, and processed continues to grow. Protecting this data from unauthorized access, breaches, and misuse is essential to maintaining trust and compliance with legal requirements.
**What is Data Privacy?**
Data privacy refers to the protection of personal and sensitive information from unauthorized access, use, or disclosure. It involves ensuring that individuals have control over how their data is collected, shared, and used. Data privacy is a fundamental right and plays a critical role in cybersecurity.
**Why is Data Privacy Important?**
1. **Protecting Personal Information:** Safeguarding data such as financial records, health information, and personal identifiers prevents identity theft and fraud.
2. **Regulatory Compliance:** Governments and international bodies have implemented strict data protection laws, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), to ensure companies handle data responsibly.
3. **Maintaining Trust:** Organizations that prioritize data privacy build trust with their customers and stakeholders, enhancing their reputation and credibility.
4. **Preventing Cyber Threats:** Cybercriminals often target personal and corporate data for financial gain. Strong data privacy measures reduce the risk of data breaches and cyberattacks.
**Key Data Privacy Regulations**
- **General Data Protection Regulation (GDPR):** Enforced in the European Union, GDPR sets strict guidelines on data collection, processing, and storage. It grants individuals rights over their personal data and imposes hefty fines for non-compliance.
- **California Consumer Privacy Act (CCPA):** A U.S.-based regulation that provides California residents with rights to know, delete, and opt out of the sale of their personal data.
- **Health Insurance Portability and Accountability Act (HIPAA):** Protects sensitive health information in the U.S.
- **Personal Data Protection Act (PDPA):** Enforces data protection measures in countries like Singapore.
**Challenges in Data Privacy**
1. **Growing Volume of Data:** With the rise of big data and cloud storage, managing and securing vast amounts of information is challenging.
2. **Evolving Cyber Threats:** Hackers continuously develop new methods to exploit vulnerabilities, making data security a constant battle.
3. **Lack of Awareness:** Many users and employees are unaware of best practices for protecting their data, leading to unintentional breaches.
4. **Compliance Complexity:** Different regulations across regions create difficulties for multinational organizations in ensuring full compliance.
**Best Practices for Data Privacy**
- **Data Encryption:** Encrypt sensitive information to protect it from unauthorized access.
- **Strong Authenti
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**The Rise and Impact of Large Language Models (LLMs)**
**Introduction**
In the rapidly evolving landscape of artificial intelligence (AI), one of the most groundbreaking advancements has been the development of Large Language Models (LLMs). These AI systems, trained on massive amounts of text data, have demonstrated remarkable capabilities in understanding, generating, and manipulating human language. LLMs have transformed industries, reshaped the way people interact with technology, and raised ethical concerns regarding their usage. This essay delves into the history, development, applications, challenges, and future of LLMs, providing a comprehensive understanding of their significance.
**Historical Background and Development**
The foundation of LLMs is built on decades of research in natural language processing (NLP) and machine learning (ML). Early language models were relatively simple and rule-based, relying on statistical methods to predict word sequences. However, the emergence of deep learning, particularly the introduction of neural networks, revolutionized NLP. The introduction of recurrent neural networks (RNNs) and long short-term memory (LSTM) networks in the late 1990s and early 2000s allowed for better sequential data processing.
The breakthrough moment for LLMs came with the development of Transformer architectures, introduced in the seminal 2017 paper "Attention Is All You Need" by Vaswani et al. The Transformer model enabled more efficient parallel processing and improved context understanding. This led to the creation of models like BERT (Bidirectional Encoder Representations from Transformers) and OpenAIs GPT (Generative Pre-trained Transformer) series, which have since set new benchmarks in AI-driven text generation and comprehension.
**Core Mechanisms of LLMs**
LLMs rely on deep neural networks trained on extensive datasets comprising books, articles, websites, and other textual resources. The training process involves:
1. **Tokenization:** Breaking down text into smaller units (words, subwords, or characters) to be processed by the model.
2. **Pretraining:** The model learns general language patterns through unsupervised learning, predicting missing words or the next sequence in a text.
3. **Fine-tuning:** Adjusting the model for specific tasks, such as summarization, translation, or question-answering, using supervised learning.
4. **Inference:** The trained model generates text based on user input, leveraging probabilistic predictions to produce coherent responses.
Through these mechanisms, LLMs can perform a wide range of linguistic tasks with human-like proficiency.
**Applications of LLMs**
LLMs have found applications across various domains, including but not limited to:
1. **Content Generation:** LLMs assist in writing articles, blogs, poetry, and even code, helping content creators enhance productivity.
2. **Customer Support:** Chatbots and virtual assistants powered by LLMs provide automated yet contCertificado Business Intelligence - Alber Florentin.pdf
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Cost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheetCost sheet. with basics and formats of sheet
Analyzing Consumer Spending Trends and Purchasing Behavior
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This project explores consumer spending patterns using Kaggle-sourced data to uncover key trends in purchasing behavior. The analysis involved cleaning and preparing the data, performing exploratory data analysis (EDA), and visualizing insights using ExcelI. Key focus areas included customer demographics, product performance, seasonal trends, and pricing strategies. The project provided actionable insights into consumer preferences, helping businesses optimize sales strategies and improve decision-making.Optimizing Common Table Expressions in Apache Hive with Calcite
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In many real-world queries, certain expressions may appear multiple times, requiring repeated computations to construct the final result. These recurring computations, known as common table expressions (CTEs), can be explicitly defined in SQL queries using the WITH clause or implicitly derived through transformation rules. Identifying and leveraging CTEs is essential for reducing the cost of executing complex queries and is a critical component of modern data management systems.
Apache Hive, a SQL-based data management system, provides powerful mechanisms to detect and exploit CTEs through heuristic and cost-based optimization techniques.
This talk delves into the internals of Hive's planner, focusing on its integration with Apache Calcite for CTE optimization. We will begin with a high-level overview of Hive's planner architecture and its reliance on Calcite in various planning phases. The discussion will then shift to the CTE rewriting phase, highlighting key Calcite concepts and demonstrating how they are employed to optimize CTEs effectively.PRGTUG Meeting: Lost in Data? Let's Chart the Way Out!
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Tableau User Group (PRGTUG) Meeting:
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Discover how the best news APIs function with this visual guide. Learn how they fetch real-time news, filter content, and integrate seamlessly with apps. Understand key features like authentication, endpoints, and data formats, making it easier to choose the right API for your needs. Perfect for developers and businesses!
For more information please visit here https://www.contify.com/news-api/
Latihan 03
- 1. 1 x 1 x 1 = 1 2 x 2 x 2 = 8 3 x 3 x 3 = 27 4 x 4 x 4 = 64 5 x 5 x 5 = 125 6 x 6 x 6 = 216 7 x 7 x 7 = 343 8 x 8 x 8 = 512 9 x 9 x 9 = 729 10 x 10 x 10 = 1000 Dapat ditulis 1 3 = 1 2 3 = 8 3 3 = 27 4 3 = 64 5 3 = 125 6 3 = 216 7 3 = 343 8 3 = 512 9 3 = 729 10 3 = 1000 Jadi, 1, 8, 27, 64, 125, 216, 343, 512, 719 dan 1000 Disebut juga BILANGAN KUBIK
- 2. = 2 + 1 = 3 = 3 - 1 = 2
- 3. Dengan menggunakan fasilitas Shapes , Buatlah slide seperti berikut: START BIL > 10 BIL = BIL + 1 BIL END PRINT BIL
- 4. Keliling Persegi panjang Bujur Sangkar Segitiga a = 8 cm a = 6 cm a b c B=4cm K = 8 + 5 + 8+ 5 K = 6 + 6 + 6 + 6 K = a + b + c