Python NumPy Tutorial | NumPy Array | Edureka
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( Python Training: https://www.edureka.co/python ) This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples. Check out our Python Training Playlist: https://goo.gl/Na1p9G This tutorial helps you to learn the following topics: 1. What is Numpy? 2. Numpy v/s Lists 3. Numpy Operations 4. Numpy Special Functions
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![www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING
What is Numpy?
❑ Numpy is the core library for scientific computing in Python.
❑ It provides a high-performance multidimensional array object, and tools for working with these arrays.
11 12 13
8 9 10
5 6 7
1 2 3A[0,0], A[0,1], A[0,2]
A[1,0], A[1,1], A[1,2]
A[2,0], A[2,1], A[2,2]
A[3,0], A[3,1], A[3,2]
What is Multi-
dimensional
array?](https://image.slidesharecdn.com/numpy-190405125856/85/Python-NumPy-Tutorial-NumPy-Array-Edureka-4-320.jpg)
![www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING
Numpy Operations
12 13
10 11
8 9
axis 1
axis 0
Sum of axis 0: [ 30, 33]
Sum of axis 1: [ 17, 21, 25]](https://image.slidesharecdn.com/numpy-190405125856/85/Python-NumPy-Tutorial-NumPy-Array-Edureka-12-320.jpg)
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Integration of Utility Data into 3D BIM Models Using a 3D Solids Modeling Wor...Safe Software Jacobs has developed a 3D utility solids modelling workflow to improve the integration of utility data into 3D Building Information Modeling (BIM) environments. This workflow, a collaborative effort between the New Zealand Geospatial Team and the Australian Data Capture Team, employs FME to convert 2D utility data into detailed 3D representations, supporting enhanced spatial analysis and clash detection.
To enable the automation of this process, Jacobs has also developed a survey data standard that standardizes the capture of existing utilities. This standard ensures consistency in data collection, forming the foundation for the subsequent automated validation and modelling steps. The workflow begins with the acquisition of utility survey data, including attributes such as location, depth, diameter, and material of utility assets like pipes and manholes. This data is validated through a custom-built tool that ensures completeness and logical consistency, including checks for proper connectivity between network components. Following validation, the data is processed using an automated modelling tool to generate 3D solids from 2D geometric representations. These solids are then integrated into BIM models to facilitate compatibility with 3D workflows and enable detailed spatial analyses.
The workflow contributes to improved spatial understanding by visualizing the relationships between utilities and other infrastructure elements. The automation of validation and modeling processes ensures consistent and accurate outputs, minimizing errors and increasing workflow efficiency.
This methodology highlights the application of FME in addressing challenges associated with geospatial data transformation and demonstrates its utility in enhancing data integration within BIM frameworks. By enabling accurate 3D representation of utility networks, the workflow supports improved design collaboration and decision-making in complex infrastructure projects
Boosting MySQL with Vector Search -THE VECTOR SEARCH CONFERENCE 2025 .pdf
Boosting MySQL with Vector Search -THE VECTOR SEARCH CONFERENCE 2025 .pdfAlkin Tezuysal As the demand for vector databases and Generative AI continues to rise, integrating vector storage and search capabilities into traditional databases has become increasingly important. This session introduces the *MyVector Plugin*, a project that brings native vector storage and similarity search to MySQL. Unlike PostgreSQL, which offers interfaces for adding new data types and index methods, MySQL lacks such extensibility. However, by utilizing MySQL's server component plugin and UDF, the *MyVector Plugin* successfully adds a fully functional vector search feature within the existing MySQL + InnoDB infrastructure, eliminating the need for a separate vector database. The session explains the technical aspects of integrating vector support into MySQL, the challenges posed by its architecture, and real-world use cases that showcase the advantages of combining vector search with MySQL's robust features. Attendees will leave with practical insights on how to add vector search capabilities to their MySQL systems.
No-Code Workflows for CAD & 3D Data: Scaling AI-Driven Infrastructure
No-Code Workflows for CAD & 3D Data: Scaling AI-Driven InfrastructureSafe Software When projects depend on fast, reliable spatial data, every minute counts.
AI Clearing needed a faster way to handle complex spatial data from drone surveys, CAD designs and 3D project models across construction sites. With FME Form, they built no-code workflows to clean, convert, integrate, and validate dozens of data formats – cutting analysis time from 5 hours to just 30 minutes.
Join us, our partner Globema, and customer AI Clearing to see how they:
-Automate processing of 2D, 3D, drone, spatial, and non-spatial data
-Analyze construction progress 10x faster and with fewer errors
-Handle diverse formats like DWG, KML, SHP, and PDF with ease
-Scale their workflows for international projects in solar, roads, and pipelines
If you work with complex data, join us to learn how to optimize your own processes and transform your results with FME.
AI Agents in Logistics and Supply Chain Applications Benefits and Implementation
AI Agents in Logistics and Supply Chain Applications Benefits and ImplementationChristine Shepherd AI agents are reshaping logistics and supply chain operations by enabling automation, predictive insights, and real-time decision-making across key functions such as demand forecasting, inventory management, procurement, transportation, and warehouse operations. Powered by technologies like machine learning, NLP, computer vision, and robotic process automation, these agents deliver significant benefits including cost reduction, improved efficiency, greater visibility, and enhanced adaptability to market changes. While practical use cases show measurable gains in areas like dynamic routing and real-time inventory tracking, successful implementation requires careful integration with existing systems, quality data, and strategic scaling. Despite challenges such as data integration and change management, AI agents offer a strong competitive edge, with widespread industry adoption expected by 2025.
Providing an OGC API Processes REST Interface for FME Flow
Providing an OGC API Processes REST Interface for FME FlowSafe Software This presentation will showcase an adapter for FME Flow that provides REST endpoints for FME Workspaces following the OGC API Processes specification. The implementation delivers robust, user-friendly API endpoints, including standardized methods for parameter provision. Additionally, it enhances security and user management by supporting OAuth2 authentication. Join us to discover how these advancements can elevate your enterprise integration workflows and ensure seamless, secure interactions with FME Flow.
Your startup on AWS - How to architect and maintain a Lean and Mean account
Your startup on AWS - How to architect and maintain a Lean and Mean accountangelo60207 Prevent infrastructure costs from becoming a significant line item on your startup’s budget! Serial entrepreneur and software architect Angelo Mandato will share his experience with AWS Activate (startup credits from AWS) and knowledge on how to architect a lean and mean AWS account ideal for budget minded and bootstrapped startups. In this session you will learn how to manage a production ready AWS account capable of scaling as your startup grows for less than $100/month before credits. We will discuss AWS Budgets, Cost Explorer, architect priorities, and the importance of having flexible, optimized Infrastructure as Code. We will wrap everything up discussing opportunities where to save with AWS services such as S3, EC2, Load Balancers, Lambda Functions, RDS, and many others.
If You Use Databricks, You Definitely Need FME
If You Use Databricks, You Definitely Need FMESafe Software DataBricks makes it easy to use Apache Spark. It provides a platform with the potential to analyze and process huge volumes of data. Sounds awesome. The sales brochure reads as if it is a can-do-all data integration platform. Does it replace our beloved FME platform or does it provide opportunities for FME to shine? Challenge accepted
TimeSeries Machine Learning - PyData London 2025
TimeSeries Machine Learning - PyData London 2025Suyash Joshi Timeseries Machine Learning - forecasting and anomaly detection with InfluxDB
Your startup on AWS - How to architect and maintain a Lean and Mean account J...
Your startup on AWS - How to architect and maintain a Lean and Mean account J...angelo60207 Prevent infrastructure costs from becoming a significant line item on your startup’s budget! Serial entrepreneur and software architect Angelo Mandato will share his experience with AWS Activate (startup credits from AWS) and knowledge on how to architect a lean and mean AWS account ideal for budget minded and bootstrapped startups. In this session you will learn how to manage a production ready AWS account capable of scaling as your startup grows for less than $100/month before credits. We will discuss AWS Budgets, Cost Explorer, architect priorities, and the importance of having flexible, optimized Infrastructure as Code. We will wrap everything up discussing opportunities where to save with AWS services such as S3, EC2, Load Balancers, Lambda Functions, RDS, and many others.
Oracle Cloud Infrastructure AI Foundations
Oracle Cloud Infrastructure AI FoundationsVICTOR MAESTRE RAMIREZ Oracle Cloud Infrastructure AI Foundations
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...Edge AI and Vision Alliance For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/state-space-models-vs-transformers-for-ultra-low-power-edge-ai-a-presentation-from-brainchip/
Tony Lewis, Chief Technology Officer at BrainChip, presents the “State-space Models vs. Transformers for Ultra-low-power Edge AI” tutorial at the May 2025 Embedded Vision Summit.
At the embedded edge, choices of language model architectures have profound implications on the ability to meet demanding performance, latency and energy efficiency requirements. In this presentation, Lewis contrasts state-space models (SSMs) with transformers for use in this constrained regime. While transformers rely on a read-write key-value cache, SSMs can be constructed as read-only architectures, enabling the use of novel memory types and reducing power consumption. Furthermore, SSMs require significantly fewer multiply-accumulate units—drastically reducing compute energy and chip area.
New techniques enable distillation-based migration from transformer models such as Llama to SSMs without major performance loss. In latency-sensitive applications, techniques such as precomputing input sequences allow SSMs to achieve sub-100 ms time-to-first-token, enabling real-time interactivity. Lewis presents a detailed side-by-side comparison of these architectures, outlining their trade-offs and opportunities at the extreme edge.
National Fuels Treatments Initiative: Building a Seamless Map of Hazardous Fu...
National Fuels Treatments Initiative: Building a Seamless Map of Hazardous Fu...Safe Software The National Fuels Treatments Initiative (NFT) is transforming wildfire mitigation by creating a standardized map of nationwide fuels treatment locations across all land ownerships in the United States. While existing state and federal systems capture this data in diverse formats, NFT bridges these gaps, delivering the first truly integrated national view. This dataset will be used to measure the implementation of the National Cohesive Wildland Strategy and demonstrate the positive impact of collective investments in hazardous fuels reduction nationwide. In Phase 1, we developed an ETL pipeline template in FME Form, leveraging a schema-agnostic workflow with dynamic feature handling intended for fast roll-out and light maintenance. This was key as the initiative scaled from a few to over fifty contributors nationwide. By directly pulling from agency data stores, oftentimes ArcGIS Feature Services, NFT preserves existing structures, minimizing preparation needs. External mapping tables ensure consistent attribute and domain alignment, while robust change detection processes keep data current and actionable. Now in Phase 2, we’re migrating pipelines to FME Flow to take advantage of advanced scheduling, monitoring dashboards, and automated notifications to streamline operations. Join us to explore how this initiative exemplifies the power of technology, blending FME, ArcGIS Online, and AWS to solve a national business problem with a scalable, automated solution.
Secure Access with Azure Active Directory
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Cisco ISE Performance, Scalability and Best Practices.pdf
Cisco ISE Performance, Scalability and Best Practices.pdfsuperdpz Cisco ISE Performance, Scalability and Best Practices
Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...
Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...NTT DATA Technology & Innovation Can We Use Rust to Develop Extensions for PostgreSQL?
(POSETTE: An Event for Postgres 2025)
June 11, 2025
Shinya Kato
NTT DATA Japan Corporation
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...Edge AI and Vision Alliance
Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...
Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...NTT DATA Technology & Innovation
Python NumPy Tutorial | NumPy Array | Edureka
- 1. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Python Numpy
- 2. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Agenda ➢ What is Numpy? ➢ Numpy v/s List ➢ Numpy Operations ➢ Numpy Special Functions
- 3. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING What is Numpy?
- 4. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING What is Numpy? ❑ Numpy is the core library for scientific computing in Python. ❑ It provides a high-performance multidimensional array object, and tools for working with these arrays. 11 12 13 8 9 10 5 6 7 1 2 3A[0,0], A[0,1], A[0,2] A[1,0], A[1,1], A[1,2] A[2,0], A[2,1], A[2,2] A[3,0], A[3,1], A[3,2] What is Multi- dimensional array?
- 5. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy v/s List
- 6. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy v/s List Why should I use Numpy when I have List? Less Memory Fast Convenient Advantages of Numpy over List
- 7. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy Operations
- 8. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy Operations Find the dimension of the array Find the byte size of each element Find the data type of the elements
- 9. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy Operations Find the size of an array 11 12 13 8 9 10 5 6 7 1 2 3 Find the shape of an array 4
- 10. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy Operations 11 12 13 8 9 10 12 13 10 11 8 9 Reshape 12 13 10 11 8 9 Slicing 9 11
- 11. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy Operations MaxMin Sum
- 12. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy Operations 12 13 10 11 8 9 axis 1 axis 0 Sum of axis 0: [ 30, 33] Sum of axis 1: [ 17, 21, 25]
- 13. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy Operations 12 13 10 11 8 9 Finding the square root Finding the Standard Deviation 12 13 10 11 8 9
- 14. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy Operations 12 13 10 11 8 9 12 13 10 11 8 9 12 13 10 11 8 9 12 13 10 11 8 9 12 13 10 11 8 9 12 13 10 11 8 9 12 13 10 11 8 9 12 13 10 11 8 9
- 15. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy Special Functions
- 16. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy Special Functions Cosine Function Sine Function
- 17. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Numpy Special Functions Exponential Function Logarithmic Function
- 18. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Session In A Minute What is Numpy? Numpy v/s List Numpy Operations Numpy Special Functions
- 19. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Thank You … Questions/Queries/Feedback