5 Simple Steps To Install Python On Windows | Install Python 3.7 | Python Training | Edureka
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** Python Certification Training: https://www.edureka.co/python ** This Edureka tutorial on Installing Python on Windows Tutorial covers all the aspects of installing and setting up Python to write code in. It is pretty simple and straightforward to setup Python completely in Windows as shown. Following topics are covered in this PPT: Agenda Introduction to Python Popularity of Python Why should you learn Python? Installing Python Python Development Environments Conclusion Python Tutorial Playlist: https://goo.gl/WsBpKe Blog Series: http://bit.ly/2sqmP4s Instagram: https://www.instagram.com/edureka_lea... Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
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Chapter - 1.pptxMikialeTesfamariam Python was created in the late 1980s by Guido van Rossum. The first version was released in 1991. It is derived from the ABC language. Python is an interpreted, object-oriented scripting language that is portable, easy to read and maintain. It has a large standard library and supports GUI, web and database applications. Major versions include Python 2.7 and Python 3.0 which introduced changes like print becoming a function. Python is widely used in areas like web development, science, education and software development.
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Covered its applications. Also compared its syntax with another programming language.
As how it's better to read and write with fewer lines of code.
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Written down our first Hello-world python program.
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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.
Dancing with AI - A Developer's Journey.pptx
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We’ll examine the shift from routine coding to creative leadership, highlighting the new developer superpowers of vision, integration, and innovation.
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Introduction to Typescript - GDG On Campus EUE
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DevOps in the Modern Era - Thoughtfully Critical Podcast
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Boosting MySQL with Vector Search -THE VECTOR SEARCH CONFERENCE 2025 .pdf
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“How Qualcomm Is Powering AI-driven Multimedia at the Edge,” a Presentation f...
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Ning Bi, Vice President of Engineering at Qualcomm Technologies, presents the “How Qualcomm Is Powering AI-driven Multimedia at the Edge” tutorial at the May 2025 Embedded Vision Summit.
In this talk, Bi explores the evolution of multimedia processing at the edge, from simple early use cases such as audio and video processing powered by algorithm-centric approaches to modern sophisticated capabilities such as digital human avatars that are transmitted over the communication channel, powered by data-driven AI. He explains how Qualcomm is applying AI and generative AI technologies on the edge to enrich computer vision for new and high-quality visual solutions. He also shows how Qualcomm enables a broad range of OEMs, ODMs and third-party developers to harness innovative technologies via initiatives such as the Qualcomm AI Hub, which provides a library of optimized machine learning models to enable developers to quickly incorporate AI into their applications.
Oracle Cloud Infrastructure Generative AI Professional
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Mastering AI Workflows with FME - Peak of Data & AI 2025
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Trends Artificial Intelligence - Mary Meeker
Trends Artificial Intelligence - Mary MeekerClive Dickens Mary Meeker’s 2024 AI report highlights a seismic shift in productivity, creativity, and business value driven by generative AI. She charts the rapid adoption of tools like ChatGPT and Midjourney, likening today’s moment to the dawn of the internet. The report emphasizes AI’s impact on knowledge work, software development, and personalized services—while also cautioning about data quality, ethical use, and the human-AI partnership. In short, Meeker sees AI as a transformative force accelerating innovation and redefining how we live and work.
“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.
ELNL2025 - Unlocking the Power of Sensitivity Labels - A Comprehensive Guide....
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Introduction to Typescript - GDG On Campus EUE
Introduction to Typescript - GDG On Campus EUEGoogle Developer Group On Campus European Universities in Egypt
“How Qualcomm Is Powering AI-driven Multimedia at the Edge,” a Presentation f...
“How Qualcomm Is Powering AI-driven Multimedia at the Edge,” a Presentation f...Edge AI and Vision Alliance
“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
5 Simple Steps To Install Python On Windows | Install Python 3.7 | Python Training | Edureka
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