#Perceptron types

[ingoampt]

Day 10 _ Regression vs Classification Multi Layer Perceptrons (MLPs)

Regression with MLPs Regression with Multi-Layer Perceptrons (MLPs) Introduction Neural networks, particularly Multi-Layer Perceptrons (MLPs), are essential tools in machine learning for solving both regression and classification problems. This guide will provide a detailed explanation of MLPs, covering their structure, activation functions, and implementation using Scikit-Learn. Regression vs.…

[eaglet-if]

Орлёнок (Eaglet)

Орлёнок (Eaglet) is an interactive story set in a country similar to 1910s-1920s Russia. You're a member of the overthrown Imperial Family, shaping the future of the Empire by virtue of arms.

It aims to be equal parts role-playing, dress-up and strategy game, with an emphasis on romance.

Although there will be no explicit nsfw scenes, it does include graphic descriptions of the horrors of war as well as personal tragedies, so please refer to the content warnings at the end of this post.

(as the project is still a wip, this overview is somewhat incomplete and will be gradually updated in tandem with the progress of writing)

DEMO: here (v0.0.2a, 21.06.2024)

Forum post: here

Number Spelling Function (IF writer resource): here

Secondary project: @a-dying-wish-if Tertiary (mini-)project: @perceptron-failure-if Quaternary project: [redacted]

The Empire of Nevetskiya - old, proud, and utterly dilapidated. While the Industrial Revolution has enabled other Monarchies - after a few quickly suppressed workers' uprisings - to become modern colonialist superpowers, exerting their influence all over the world, Nevetskiya is still overwhelmingly agrarian, and barely holding onto its outlying territories acquired in golden times long past.

Your Father Emperor, while ruling with an iron fist and unquestionable authority over the common people, is completely dependent on the shaky loyalty of the High Nobility, who frustrate any attempt to modernize the economy or administration, out of fear upstart merchants might, in time, replace the old Aristocracy.

When a sloppily executed coup d'etat eventually leaves your family dead and you a refugee, it becomes time you grab the reins of your destiny and amass an army to liberate and rebuild the country in the way you envision.

(this is meant to be a concise overview - a more exiting and detailed description of features can be found in the offical Interest Check Thread post)

extensive character customization

extensive army customization - both in a strategy and in a dress-up game sense

focus on story over stats - success is determined on the battlefield, not by your character's personality

five distinct regions with a wide cast of characters

complex personality system - for example, how your character actually feels and what they show to the world are separate things

several ways to rule - will you become a traditional Monarch, a Military Dictator, a democratically elected Head-of-State, or maybe proclaim yourself a Living Saint?

choose how much modernization is needed - will you allow women to bear arms, at the cost of offending the traditionalist nobles? Introduce tanks at the cost of foreign powers gaining influence?

how far will you go for victory? A political police, mass executions and the use of special types of weaponry might give you an edge, but is your vision really worth it?

a total of ten romanceable characters

(this naturally might contain slight spoilers)

The ROs

★ Yakov Tymofiyevich Sokolovskiy / Liliya Tymofiyevna Sokolovskaya ★

The Intelligence Director (gender-selectable)

One of your four original companions. As a member of the High Nobility, you've met them before - maybe you've even been childhood friends?

But even if you know them, it's hard to tell what they're truly like, as they seem to switch personalities effortlessly depending on the situation.

Their work is a mystery to seemingly everyone, but they always get results: as long as you let them act freely, no enemy agent has any chance to harm you or your cause.

Age: mid-20s

★ Semyon Ivanovich Orlov / Selena Ivanovna Orlova ★

The Cavalry Officer (gender-selectable)

One of your four original companions. A war hero and renowned expert when it comes to horses, the only reason they were not yet promoted to a lofty position in the War Ministry is their pragmatic approach to new developments, which hasn't mixed well with the typically very traditionalist views of the old Imperial officer corps.

Possessing a subdued but strong charisma and deeply respected by their soldiers as a wise parent figure, they are a solid pillar of support to you, and will reliably get things done - though some people might consider the cost for that too high sometimes.

Age: early 30s

★ Mikhail Pavlovich Voronin / Marina Pavlovna Voronina ★

The Young Visionary (gender-selectable)

One of your four original companions. They shot up through the ranks by impressing the War Ministry with bold new ideas for utilizing modern technologies and are hailed as a genius by many - though the older officers dismiss them as a dreamer at best and incompetent fool at worst.

With you, they hope to have found someone who'll appreciate their visions for the future - plus, their relative eccentrism has left them in dire need of a friend.

Their technical expertise might just prove to be the key to your success - if you can secure the foreign support needed to get the modern equipment needed to utilize it.

Age: early 20s

★ Leon Isayev / Leah Isayeva ★

The Noble Academic (gender-selectable)

One of your four original companions. Born to wealthy nobles, they graduated the Imperial Officer Academy with perfect grades, and feel honour-bound to your family.

They were the one to gather your initial force of loyalists and act as your primary advisor. But their loyalty is to the Imperial system, with you just a symbolic representative - can you convince them that you and your vision deserve their loyalty beyond that?

Age: late 20s

★ 'Little' Semyon/Selena Shvets ★

The Hero (gender-selectable)

A young cavalry officer and leader of your Southern Forces. A protegé of the "other" Semyon/Selena, they lack their cynical pragmatism, but make up for it with a firm belief in the triumph of a better world.

Some may call their optimism naive, and their personality has been mockingly compared to a Golden Retriever, but they have proven time and time again that underestimating them on the battlefield results in a crushing defeat.

Age: early 20s

★ Nikola ★

The Rebel (nb)

Leading an anti-authoritarian peasant uprising in the West, Nikola is more likely to be your enemy than your ally - but they don't seem to care enough about politics to refrain from flirting with you, so... there might be a basis of mutual understanding there?

Their personality is pretty sweet, at least - if you ignore the fact they'll cheerfully gun down prisoners if they feel like it.

Age: mid-20s

★ Rakhmil/Rakhilya Feldman ★

The Logistician (gender-selectable)

A member of the Western Rebel Army and best friend of Nikola's adoptive sibling, they've poured their soul (and countless nights without any sleep) into somehow maintaining the rebels' supply network in the face of their rapidly swelling numbers.

Unhappy with Nikola's carefree attitude, they might end up aligning with you instead in order to save their cause.

Age: late 20s

★ Arseniy Matveyevich Lebedev / Amaliya Matveyevna Lebedeva ★

The Enemy (gender-selectable)

Grand Duke Lebedev, the main leader of the Aristocrat faction, stood by and watched when your family was executed. Arseniy/Amaliya is their younger sibling, and serves as military commander of his personal forces that aid several warlords in their efforts to establish their own petty kingdoms.

But they're already uncomfortable with their brother's methods, and if you can convince them that you're not actually "an incompetent little puppet who's trying to ruin the country out of arrogant delusions", they might become a very valuable ally.

Age: mid-20s

★ Lyudmila Demyanovna Naumova ★ (f)

A minor noble who reluctantly turned into a Warlord in order to protect her territory and her people. All she wants is peace - but she'll not hesitate to fight if she believes it necessary.

Unfortunately, you can't just ignore her - all must choose a side in this war - but you have options how to deal with her. Will you subdue her by force? Or fall back on the age-old option of political marriage to secure an alliance?

Age: late 20s

★ Jan/Jana Novotný ★ (gender-selectable, under certain circumstances)

A member of your Personal Guard who has distinguished themself and eventually rises to become its commander. Others might betray or doubt you, but Novotný only cares about one thing - your continued, unharmed existence.

And they will go to any lengths to guarantee it.

Age: mid-20s

CONTENT WARNINGS

...will be added as they become relevant in the demo.

[argumate]

hithisisawkward said: Master’s in ML here: Transformers are not really monstrosities, nor hard to understand. The first step is to go from perceptrons to multi-layered neural networks. Once you’ve got the hand of those, with their activation functions and such, move on to AutoEncoders. Once you have a handle on the concept of latent space ,move to recurrent neural networks. There are many types, so you should get a basic understading of all, from simple recurrent units to something like LSTM. Then you need to understand the concept of attention, and study the structure of a transformer (which is nothing but a couple of recurrent network techniques arranged in a particularly clever way), and you’re there. There’s a couple of youtube videos that do a great job of it.

thanks, autoencoders look like a productive topic to start with!

[ardellesplace]

A roomsize computer equipped with a new type of circuitry, the Perceptron, was introduced to the world in 1958 in a brief news story buried deep in The New York Times. The story cited the U.S. Navy as saying that the Perceptron would lead to machines that “will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.”

We’ve been here before: AI promised humanlike machines – in 1958 *

[the-picture-of-mixolydian-gray]

Both of those are examples of ai tho...

AI is just a generic term for the automation of decision making. Any situation where a machine chooses from a list of options, where that specific choice is not made by a human is AI.

Like, people in the tags and comments are all saying what words they "like" using for these concepts, but all of them have very specific meanings already.

Machine learning is a subtype of AI, where the machine is punished for bad decisions and hopefully improves over time; Deep Learning is a subtype of machine learning where this "punishment" is done with Neural Networks, ehich are collections of Perceptrons, a mathematical structure which abstracts the working of a neuron; a Transformer is a type of neural network architecture (an arrangement of perceptron layers) that runs operations in parallel on a vectorised form of the data; U-Net is a deep learning model that uses Transformers as encoder-decoder pairs to work with simplified versions of images; Diffusion is a deep learning algorithm (a set of logical steps) that uses U-Nets to predict how much noise has been added to an image, in what "direction"; Stable diffusion is a software that abuses this algorithm to "remove" noise from images of pure noise to "recover" the "oroginal" image.

"Generative AI" is completely arbitrary. StableDiffusion and other shit like it use an algorithm made for cleaning noise in medical scans. ChatGPT is literally just a more powerful version of the "next word predictor" on a phone keyboard.

This is like saying "Transportation is evil because of 9-11. Say 'automated movement' instead"

"Yeah, I used AI to help make this"

"NON-GENERATIVE AI"

[kevinpshanblog]

🌐 Meeting Summary: AI & American National Security Presentation

Presented at the Poughkeepsie Boardman Rd Library Monday June 23rd. Synopsis by ChatGPT using a transcript generated from a recording done using Apple Notes transcription feature.

👤 Presenter: Jean-Claude Fouere

Jean-Claude delivered an in-depth presentation on artificial intelligence, tracing its development, societal impacts, and growing relevance to U.S. national security.

🧪 ChatGPT Demonstration

Showcased ChatGPT with a live example.

Highlighted its power in natural language generation and its accessibility to the general public.

Marked 2022 as the moment AI “came of age.”

🔍 Key Themes

Foundation & Dual-Use Technology

AI is a foundational technology, like electricity.

Dual-use: AI serves both civilian and military applications.

Investment & Acceleration

Billions in private investment��more than the Apollo program.

Used in coding, image generation, audio, video, finance, and logistics.

AI Types

Generative AI: Like ChatGPT and image/music generators.

Autonomous Agents: Systems that can act without human oversight.

Self-Learning Systems: Example: AlphaGo.

🧬 AI Development Timeline

1950: Turing’s “Computing Machinery and Intelligence”

1956: Dartmouth Conference—AI formally recognized as a field

1957: Rosenblatt’s neural network (Perceptron)

2012–2022: Rise of deep learning and large language models (GPT)

⚠️ Challenges & Risks

Misinformation: Deepfakes demonstrated in live examples.

Addiction: Especially in youth—Eliza effect still relevant.

Job Displacement: Automation is changing the labor landscape.

Ethical Issues: Unsupervised AI decisions in finance, warfare, and education.

Knowledge Decay: Risk of losing human critical thinking.

🛡️ National Security Implications

AI enhances:

Combat ops (e.g., drone targeting)

Cyberwarfare (e.g., misinformation, infrastructure attacks)

Surveillance & intelligence

DARPA and U.S. military heavily invested in early AI R&D.

🌍 U.S.–China AI Competition

U.S. leads in AI innovation, but China is rapidly advancing (e.g., DeepSeek).

Export controls aim to limit China’s access to high-end chips (TSMC, ASML).

Taiwan’s chip industry is a strategic vulnerability.

📜 Regulation & Governance

Emphasis on “sensible regulation”:

Transparent, explainable, fair, privacy-respecting.

U.S. legislation (pending): 10-year moratorium on local AI laws (controversial).

EU AI Act enforces strict accountability.

📚 AI Literacy & Public Education

Libraries suggested as trusted centers for AI learning.

Community wants curated, structured programs, not just YouTube tutorials.

Emphasis on learning:

How to use AI (not necessarily build it)

Prompt engineering as an emerging skill

Ethical, critical engagement with technology

💬 Closing Message

“Let’s educate ourselves—and let’s be mindful of what AI can do for us and what AI can do to us.” – Jean-Claude

AI is here to stay. Now is the time for public awareness, responsible use, and democratic oversight.

[styrishai295]

Deep Learning Online Training and Machine Learning Course Syllabus: A Comprehensive Guide

The Importance of Deep Learning Online Training

With the rapid advancements in artificial intelligence, deep learning online training platforms have become invaluable resources for learners worldwide. These courses often provide flexible schedules, hands-on projects, and expert mentorship, making complex topics accessible to learners at all levels. Whether you're a beginner or an experienced professional, online training allows you to learn at your own pace, revisit challenging concepts, and stay updated with the latest trends in AI.

Many reputable platforms such as Coursera, Udacity, and edX offer specialized courses in deep learning, covering foundational concepts like neural networks, backpropagation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning. These courses often include practical assignments, real-world datasets, and capstone projects that help reinforce learning and build a strong portfolio.

Machine Learning Course Syllabus: What to Expect

A comprehensive machine learning course syllabus provides the roadmap for acquiring essential knowledge and skills in this domain. Typically, such a syllabus covers:

Introduction to Machine Learning: Understanding the basics, types of machine learning (supervised, unsupervised, reinforcement), and real-world applications.

Mathematical Foundations: Linear algebra, calculus, probability, and statistics necessary for algorithm development.

Data Preprocessing: Handling missing data, feature scaling, feature engineering, and data visualization techniques.

Supervised Learning Algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines.

Unsupervised Learning Algorithms: Clustering methods like K-means, hierarchical clustering, principal component analysis (PCA).

Model Evaluation and Selection: Cross-validation, bias-variance tradeoff, metrics like accuracy, precision, recall, F1-score.

Introduction to Deep Learning: Transitioning into neural networks, understanding architectures, and training techniques.

Focusing on linear models in machine learning—such as linear regression and logistic regression—is fundamental. These models are simple yet powerful, especially for problems with linear relationships. They serve as the foundation for more complex algorithms and serve as an excellent starting point for beginners.

Deep Learning Roadmap: Navigating Your Learning Path

For those looking to specialize further, developing a deep learning roadmap is essential. This roadmap guides learners from basic concepts to advanced topics, ensuring a structured and efficient learning process. A typical deep learning roadmap includes:

Mathematical Foundations: Master linear algebra, calculus, probability, and optimization techniques.

Machine Learning Basics: Understand supervised and unsupervised learning, along with common algorithms.

Neural Networks: Learn about perceptrons, activation functions, loss functions, and backpropagation.

Deep Neural Networks: Dive into architectures like CNNs for image processing, RNNs for sequential data, and LSTMs.

Advanced Topics: Explore generative adversarial networks (GANs), reinforcement learning, transfer learning, and unsupervised deep learning.

Practical Implementation: Gain hands-on experience with frameworks like TensorFlow, Keras, and PyTorch.

Specializations: Focus on areas such as natural language processing, computer vision, or speech recognition.

Throughout this roadmap, continuous practice through projects, Kaggle competitions, and research papers is encouraged to solidify understanding.

Conclusion

Embarking on learning deep learning online training and understanding the machine learning course syllabus are essential steps toward building a successful career in AI. Whether you're starting with linear models or progressing to complex neural networks, a structured approach guided by a deep learning roadmap will ensure steady progress and mastery of skills.

[giyadesuza]

Dimensional Accuracy Meets Digital Intelligence: The Future of Industrial Metrology

The Industrial Metrology MarketResearch Report is the result of extensive research and analysis conducted by our team of experienced market researchers. It encompasses a wide range of critical factors influencing the Industrial Metrology MarketGrowth from 2025 to 2032, including competitive landscape, consumer behavior, and technological advancements. This report serves as a valuable resource for industry players, helping them make informed decisions and stay ahead of the competition in a rapidly evolving market landscape. With its comprehensive coverage and actionable insights, the Industrial Metrology MarketReport offers unparalleled opportunities for growth and success in the Business.

The Report features a comprehensive table of contents, figures, tables, and charts, as well as insightful analysis. Industrial Metrology MarketSize has been expanding significantly in recent years, driven by various key factors like increased demand for its products, expanding customer base, and technological advancements. This report provides a comprehensive analysis of Industrial Metrology MarketBusiness, including market size, trends, drivers and constraints, competitive aspects, and prospects for future growth.

List of top companies in Industrial Metrology Market:

Hexagon AB

Renishaw PLC

FARO Technologies

Nikon Metrology

Carl Zeiss AG

Jenoptik AG

Perceptron

Automated Precision Inc.

KLA Corporation

Applied Materials Inc.

Download Sample Report: https://brandessenceresearch.com/downloadSample/PostId/3369?utm_source=13&utm_medium=Pluse

Scope of Industrial Metrology Market:

The Industrial Metrology MarketResearch presents a detailed analysis of trends, drivers, and challenges within industry. It includes thorough insights into market segmentation by product type, application, and geography. The report highlights major players and their competitive strategies, as well as emerging opportunities for growth. It also investigates consumer behavior and preferences that affect market dynamics. Forecasts for market size and growth potential in the upcoming years are included, backed by quantitative data. It also addresses regulatory factors and technological advancements influencing the market, making this report a valuable resource for stakeholders looking to make informed business decisions.

Global Industrial Metrology Market Segmentation:

Offering Outlook

Hardware

Software

Services

Equipment Outlook

Coordinate Measuring Machine (CMM)

Optical Digitizer and Scanner (ODS)

Measuring Instruments

X-ray and Computed Tomography

Automated Optical Inspection

Form Measurement Equipment

2D Equipment

Application Outlook

Quality Control & Inspection

Reverse Engineering

Mapping and Modeling

Others

End User Industry Outlook

Aerospace & Defense

Automotive

Semiconductor

Manufacturing

Others

Regional Insights:

The regions covered in this Global Industrial Metrology Marketreport are North America, Europe, Asia-Pacific, and Rest of the World. Based on country level, the market of Managed security service is subdivided into the U.S., Mexico, Canada, U.K., France, Germany, Italy, China, Japan, India, Southeast Asia, Middle East Asia (UAE, Saudi Arabia, Egypt) GCC, Africa, etc.

The Global Industrial Metrology MarketReport is recommended for several reasons. Firstly, it offers a detailed examination of the market, considering critical factors such as market size, growth drivers, challenges, and opportunities. This research provides insightful information that aids organizations in formulating effective action plans and making informed decisions. Additionally, the study presents a comprehensive competitive landscape, allowing customers to benchmark their performance against major competitors and identify potential alliances. The report’s geographical analysis helps businesses grasp market dynamics in different regions, enabling them to adapt their strategies accordingly. For companies seeking to understand and thrive in the Global Industrial Metrology industry, this report proves to be an invaluable resource.

Browse In-depth Market Research Report (300 Pages) on Industrial Metrology Market:

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(1) A complete section of the Global Industrial Metrology report is dedicated for market dynamics, which include influence factors, market drivers, challenges, opportunities, and trends.

(2) Another broad section of the research study is reserved for regional analysis of the Global Industrial Metrology Marketwhere important regions and countries are assessed for their growth potential, consumption, market share, and other vital factors indicating their market growth.

(3) Players can use the competitive analysis provided in the report to build new strategies or fine-tune their existing ones to rise above market challenges and increase their share of the Global Industrial Metrology Market.

(4) The report also discusses competitive situation and trends and sheds light on company expansions and merger and acquisition taking place in the Global Industrial Metrology Market. Moreover, it brings to light the market concentration rate and market shares of top three and five players.

(5) Readers are provided with findings and conclusion of the research study provided in the Global Industrial Metrology Marketreport.

Key Questions Answered in the Report:

(1) What are the growth opportunities for the new entrants in the Global Industrial Metrology industry?

(2) Who are the leading players functioning in the Global Industrial Metrology Marketplace?

(3) What are the key strategies participants are likely to adopt to increase their share in the Global Industrial Metrology industry?

(4) What is the competitive situation in the Global Industrial Metrology Market?

(5) What are the emerging trends that may influence the Global Industrial Metrology Marketgrowth?

(6) Which product type segment will exhibit high CAGR in future?

(7) Which application segment will grab a handsome share in the Global Industrial Metrology industry?

(8) Which region is lucrative for the manufacturers?

About Us:

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Brandessence market research publishes market research reports & business insights produced by highly qualified and experienced industry analysts. Brand Essence Market Research report will be best fit for senior executives, business development managers, marketing managers, consultants, CEOs, CIOs, COOs, and Directors, governments, agencies, organizations, and Ph.D. Students. We have a delivery center in Pune, India and our sales office is in London.

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[programmingandengineering]

CS 7643 Deep Learning - Homework 1][5]

In this homework, we will learn how to implement backpropagation (or backprop) for “vanilla” neural networks (or Multi-Layer Perceptrons) and ConvNets. You will begin by writing the forward and backward passes for different types of layers (including convolution and pooling), and then go on to train a shallow ConvNet on the CIFAR-10 dataset in Python. Next you’ll learn to use [PyTorch][3], a…

[dbmrmark]

Laser Tracker Market Companies: Growth, Share, Value, Size, Industry Analsis and Forecast by 2028

"Laser Tracker Market Size And Forecast by 2028

The global Laser Tracker Market study offers a thorough examination of the industry, highlighting the influence of leading companies on market dynamics and growth. These key players set the benchmark for innovation and operational excellence, contributing significantly to the development of the market. The study delves into their strategic initiatives, offering insights into how they navigate challenges and capitalize on opportunities. By focusing on these companies, the report paints a vivid picture of the competitive environment and its evolution.

Laser tracker market is expected to reach USD 911.58 million by 2028 witnessing market growth at a rate of 12.10% in the forecast period of 2021 to 2028.

Get a Sample PDF of Report - https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-laser-tracker-market

Which are the top companies operating in the Laser Tracker Market?

The Top 10 Companies in Laser Tracker Market are known for their strong presence and innovative solutions. These include industry leaders.  Each of these companies has made significant contributions through cutting-edge products, strategic partnerships, and global reach. Their ability to adapt to market trends and consumer demands has helped them maintain leadership positions in the market, driving growth and setting industry standards.

**Segments**

- By Product Type: Hardware (Portable, Fixed), Software - By Offering: Measurement & Inspection, Alignment, Calibration - By Application: Aerospace & Defense, Automotive, Energy & Power, Machinery & Equipment Manufacturing, Others - By End-User: Large Enterprises, Small & Medium Enterprises

The global laser tracker market is segmented based on various factors to provide a detailed analysis of the industry landscape. When considering product type, the market is categorized into hardware, including portable and fixed trackers, as well as software solutions. The offering segment comprises measurement & inspection, alignment, and calibration services. In terms of applications, the market caters to industries such as aerospace & defense, automotive, energy & power, machinery & equipment manufacturing, and others. Lastly, the end-user segment differentiates between large enterprises and small & medium enterprises, each with unique requirements and demands when it comes to laser tracking technology.

**Market Players**

- Hexagon - FARO Technologies, Inc. - API - Automated Precision, Inc. - Renishaw plc - Steinbichler Optotechnik GmbH - Leica Geosystems AG - Nikon Metrology, Inc. - Perceptron, Inc. - VMT GmbH - On-Trak Photonics Inc.

Leading market players in the global laser tracker industry include Hexagon, FARO Technologies, Inc., Automated Precision, Inc. (API), Renishaw plc, Steinbichler Optotechnik GmbH, Leica Geosystems AG, Nikon Metrology, Inc., Perceptron, Inc., VMT GmbH, and On-Trak Photonics Inc. These companies are at the forefront of innovation, offering cutting-edge laser tracking solutions to meet the diverse needs of industries worldwide. Their investments in research and development, strategic partnerships, and product launches play a crucial role in driving the growth of the laser tracker market.

https://www.databridgemarketresearch.com/reports/global-laser-tracker-marketThe global laser tracker market is witnessing significant growth and is expected to continue expanding in the coming years due to the increasing demand for high-precision measurement and inspection solutions across various industries. One of the key drivers of market growth is the growing adoption of Industry 4.0 technologies, which emphasize automation, connectivity, and data exchange in manufacturing processes. Laser trackers play a crucial role in ensuring accurate alignment, calibration, and measurement in the production and quality control processes of industries such as aerospace & defense, automotive, energy & power, and machinery & equipment manufacturing.

Market players such as Hexagon, FARO Technologies, and Renishaw plc are investing heavily in research and development to introduce advanced laser tracking solutions that offer improved accuracy, efficiency, and ease of use. These companies are focusing on developing portable and fixed hardware trackers as well as software solutions that can provide comprehensive measurement and inspection capabilities to meet the evolving needs of end-users. Additionally, strategic collaborations and partnerships with other industry stakeholders are enabling market players to enhance their product offerings and expand their market reach.

The aerospace & defense sector is a significant end-user of laser tracker technology, utilizing it for precision manufacturing, assembly, and quality control processes. The automotive industry also relies on laser trackers for ensuring dimensional accuracy and alignment in vehicle manufacturing. Moreover, the energy & power sector utilizes laser trackers for applications such as turbine alignment and blade inspection in wind energy installations. As industries continue to prioritize efficiency and accuracy in their operations, the demand for laser tracking solutions is expected to rise steadily.

The global laser tracker market is dynamic and competitive, with players constantly striving to differentiate themselves through technological advancements and innovative solutions. Market players are also focusing on offering comprehensive calibration and alignment services to meet the diverse requirements of end-users. The increasing adoption of laser trackers in small and medium enterprises, alongside the continuous innovation by market players, is expected to drive market growth further.

In conclusion, the global laser tracker market is poised for substantial growth driven by technological advancements, expanding applications across various industries, and the focus on precision manufacturing processes. Market players are expected to continue investing in research and development to introduce advanced solutions that cater to the evolving needs of end-users. With the increasing emphasis on automation and quality control in industrial processes, laser trackers are set to play a key role in enhancing operational efficiency and accuracy across different sectors.The global laser tracker market is experiencing robust growth as industries increasingly rely on high-precision measurement and inspection solutions to enhance operational efficiency and ensure quality control. Laser trackers play a crucial role in various sectors such as aerospace & defense, automotive, energy & power, and machinery & equipment manufacturing by providing accurate alignment, calibration, and measurement capabilities. The adoption of Industry 4.0 technologies, emphasizing automation and connectivity, is driving the demand for laser tracking solutions that can streamline production processes and improve overall productivity.

Key market players such as Hexagon, FARO Technologies, and Renishaw plc are leading the way in innovation by investing in research and development to introduce advanced laser tracking solutions. These companies focus on developing portable and fixed hardware trackers, as well as software solutions, to meet the evolving needs of end-users across different industries. By offering comprehensive measurement and inspection capabilities, market players aim to cater to the growing demand for precise and efficient solutions that enable organizations to optimize their manufacturing processes.

The aerospace & defense industry is a significant driver of demand for laser tracker technology, utilizing it for precision manufacturing, assembly, and quality control applications. Similarly, the automotive sector relies on laser trackers for dimensional accuracy and alignment in vehicle manufacturing processes. In the energy & power sector, laser trackers are utilized for turbine alignment and blade inspection in wind energy installations, highlighting their versatility across different applications. As businesses prioritize operational efficiency and accuracy, the adoption of laser tracking solutions is expected to increase steadily across various sectors.

The competitive landscape of the global laser tracker market is characterized by intense competition and continuous innovation among market players to differentiate themselves through technological advancements and innovative solutions. Companies are increasingly focusing on providing comprehensive calibration and alignment services to address the diverse requirements of end-users, driving further market growth. The expanding adoption of laser trackers in small and medium enterprises, coupled with ongoing innovation by key market players, is poised to fuel market expansion in the coming years.

In conclusion, the global laser tracker market is set for significant growth driven by advancements in technology, expanding applications across diverse industries, and the growing emphasis on precision manufacturing processes. Market players are expected to continue investing in research and development to introduce cutting-edge solutions that address the evolving needs of end-users. With the rising focus on automation and quality control in industrial operations, laser tracking technology is poised to play a pivotal role in enhancing efficiency and accuracy across sectors, driving further market growth and innovation.**Segments**

Global Laser Tracker Market, By Component: - Hardware - Software - Services

Application: - Quality Control and Inspection - Alignment - Reverse Engineering - Calibration

Industry: - Automotive - Aerospace and Defence - Energy and Power - General Manufacturing - Architecture and Construction - Transportation - Others

Country: - U.S. - Canada - Mexico - Brazil - Argentina - Rest of South America - Germany - Italy - U.K. - France - Spain - Netherlands - Belgium - Switzerland - Turkey - Russia - Rest of Europe - Japan - China - India - South Korea - Australia - Singapore - Malaysia - Thailand - Indonesia - Philippines - Rest of Asia-Pacific - Saudi Arabia - U.A.E - South Africa - Egypt - Israel - Rest of Middle East and Africa

**Market Players**

The major players covered in the laser tracker market report are Hexagon AB, FARO Technologies, Inc., Automated Precision, Inc (API), SGS SA, VMT GmbH, On-Trak Photonics, Inc., Variation Reduction Solutions, Inc., Brunson Instrument Company, Hubbs Machine & Manufacturing Inc, PLX Inc., Verisurf Software, Inc., OASIS Alignment Services, LLC., Nebula3D Services Private Limited, Mactech On-Site Solutions, East Coast Metrology, LLC, Advanced Dimensional Solutions Pty Ltd., Hiwe SqS, Diverse Dimensions, OR3D Ltd., Novamechanical Design Ltd, among other domestic and global players. Market share data is available for global, North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America separately. DBMR analysts understand competitive strengths and provide competitive analysis for each competitor separately.

The global laser tracker market is experiencing robust growth driven by the increasing demand for high-precision measurement and inspection solutions in industries worldwide. Key drivers such as the adoption of Industry 4.0 technologies and the emphasis on automation are fueling market expansion. Laser trackers are vital for ensuring accurate alignment, calibration, and measurement processes in sectors like aerospace & defense, automotive, energy & power, and machinery & equipment manufacturing. Leading market players like Hexagon, FARO Technologies, and Renishaw plc are investing in R&D to develop advanced solutions that offer improved accuracy and efficiency, meeting the evolving needs of end-users.

The aerospace & defense industry extensively uses laser tracker technology for precision manufacturing and quality control, while the automotive sector relies on it for dimensional accuracy in vehicle manufacturing. Energy & power applications include turbine alignment and blade inspection in wind energy installations. The market is competitive, with players focusing on innovation and differentiation through technological advancements. Collaborations and partnerships are enabling companies to enhance their product offerings and expand market reach. As small and medium enterprises increasingly adopt laser tracking solutions, market growth is further accelerated. In summary, the global laser tracker market is set for substantial growth driven by technological advancements, diverse applications across industries, and the focus on precision manufacturing. Market players will continue to invest in R&D to introduce cutting-edge solutions that cater to evolving end-user needs. Laser tracking technology's role in enhancing operational efficiency and accuracy is crucial as industries prioritize automation and quality control, positioning laser trackers as key tools across sectors for enhanced productivity and process optimization.

Explore Further Details about This Research Laser Tracker Market Report https://www.databridgemarketresearch.com/reports/global-laser-tracker-market

Key Insights from the Global Laser Tracker Market :

Comprehensive Market Overview: The Laser Tracker Market is growing rapidly, driven by technological advancements and evolving consumer preferences.

Industry Trends and Projections: The market is expected to grow at a CAGR of X% over the next five years, with increasing automation and digitalization.

Emerging Opportunities: New market segments, such as sustainable and eco-friendly solutions, are creating significant growth prospects.

Focus on R&D: Companies are investing heavily in R&D to innovate and improve product offerings, ensuring market leadership.

Leading Player Profiles: Major player dominate the market with strong portfolios and strategic partnerships.

Market Composition: The market is diverse, with a mix of large enterprises and emerging startups driving competition and innovation.

Revenue Growth: The market has witnessed a steady increase in revenue, primarily driven by growing demand and product diversification.

Commercial Opportunities: There are considerable opportunities for business expansion in emerging regions and through technological innovations.

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[nomidls]

Perceptron Neural Network: The Foundation of Machine Learning

The Perceptron is the simplest type of artificial neural network, introduced by Frank Rosenblatt in 1958. It is a fundamental building block of modern machine learning, designed to model how neurons in the human brain process information. A perceptron consists of input neurons, weighted connections, an activation function, and an output. It is primarily used for binary classification tasks, distinguishing between two categories based on input features. Though limited in handling complex problems, the perceptron laid the groundwork for advanced perceptron neural networks like deep learning. Its historical significance makes it a crucial concept in understanding artificial intelligence and pattern recognition.

[jcmarchi]

Unlocking New Possibilities in Healthcare with AI

New Post has been published on https://thedigitalinsider.com/unlocking-new-possibilities-in-healthcare-with-ai/

Unlocking New Possibilities in Healthcare with AI

Healthcare in the United States is in the early stages of a significant potential disruption due to the use of Machine Learning and Artificial Intelligence. This shift has been underway for over a decade, but with recent advances, seems poised for more rapid changes. Much work remains to be done to understand the safest and most effective applications of AI in healthcare, to build trust among clinicians in the use of AI, and to adjust our clinical education system to drive better use of AI-based systems.

Applications of AI in Healthcare

AI has been in evolution for decades in healthcare, both in patient-facing and back-office functions. Some of the earliest and most extensive work has occurred in the use of deep learning and computer vision models.

First, some terminology. Traditional statistical approaches in research–e.g. observational studies and clinical trials–have used population-focused modeling approaches that rely on regression models, in which independent variables are used to predict outcomes. In these approaches, while more data is better, there is a plateau effect in which above a certain data set size, no better inferences can be obtained from the data.

Artificial intelligence brings a newer approach to prediction. A structure called a perceptron processes data that is passed forward a row at a time, and is created as a network of layers of differential equations to modify the input data, to produce an output. During training, each row of data as it passes through the network–called a neural network–modifies the equations at each layer of the network so that the predicted output matches the actual output. As the data in a training set is processed, the neural network learns how to predict the outcome.

Several types of networks exist. Convolutional neural networks, or CNNs, were among the first models to find success in healthcare applications. CNNs are very good at learning from images in a process called computer vision and have found applications where image data is prominent: radiology, retinal exams, and skin images.

A newer neural network type called the transformer architecture has become a dominant approach due to its incredible success for text, and combinations of text and images (also called multimodal data). Transformer neural networks are exceptional when given a set of text, at predicting subsequent text. One application of the transformer architecture is the Large Language Model or LLM. Multiple commercial examples of LLMs include Chat GPT, Anthropics Claude, and Metas Llama 3.

What has been observed with neural networks, in general, is that a plateau for improvement in learning has been hard to find. In other words, given more and more data, neural networks continue to learn and improve. The main limits on their capability are larger and larger data sets and the computing power to train the models. In healthcare, the creation of privacy-protecting data sets that faithfully represent true clinical care is a key priority to advance model development.

LLMs may represent a paradigm shift in the application of AI for healthcare. Because of their facility with language and text, they are a good match to electronic records in which almost all data are text. They also do not require highly annotated data for training but can use existing data sets. The two main flaws with these models are that 1) they do not have a world model or an understanding of the data that is being analyzed (they have been called fancy autocomplete), and 2) they can hallucinate or confabulate, making up text or images that appear accurate but create information presented as fact.

Use cases being explored for AI include automation and augmentation for reading of radiology images, retinal images, and other image data; reducing the effort and improving the accuracy of clinical documentation, a major source of clinician burnout; better, more empathic, patient communication; and improving the efficiency of back-office functions like revenue cycle, operations, and billing.

Real-world Examples

AI has been incrementally introduced into clinical care overall. Typically, successful use of AI has followed peer-reviewed trials of performance that have demonstrated success and, in some cases, FDA approval for use.

Among the earliest use cases in which AI performs well have been AI detecting disease in retinal exam images and radiology. For retinal exams, published literature on the performance of these models has been followed by the deployment of automated fundoscopy to detect retinal disease in ambulatory settings. Studies of image segmentation, with many published successes, have resulted in multiple software solutions that provide decision support for radiologists, reducing errors and detecting abnormalities to make radiologist workflows more efficient.

Newer large language models are being explored for assistance with clinical workflows. Ambient voice is being used to enhance the usage of Electronic Health Records (EHRs). Currently, AI scribes are being implemented to aid in medical documentation. This allows physicians to focus on patients while AI takes care of the documentation process, improving efficiency and accuracy.

In addition, hospitals and health systems can use AI’s predictive modeling capabilities to risk-stratify patients, identifying patients who are at high or increasing risk and determining the best course of action. In fact, AI’s cluster detection capabilities are being increasingly used in research and clinical care to identify patients with similar characteristics and determine the typical course of clinical action for them. This can also enable virtual or simulated clinical trials to determine the most effective treatment courses and measure their efficacy.

A future use case may be the use of AI-powered language models in doctor-patient communication. These models have been found to have valid responses for patients that simulate empathetic conversations, making it easier to manage difficult interactions. This application of AI can greatly improve patient care by providing quicker and more efficient triage of patient messages based on the severity of their condition and message.

Challenges and Ethical Considerations

One challenge with AI implementation in healthcare is ensuring regulatory compliance, patient safety, and clinical efficacy when using AI tools. While clinical trials are the standard for new treatments, there is a debate on whether AI tools should follow the same approach. Another concern is the risk of data breaches and compromised patient privacy. Large language models trained on protected data can potentially leak source data, which poses a significant threat to patient privacy. Healthcare organizations must find ways to protect patient data and prevent breaches to maintain trust and confidentiality. Bias in training data is also a critical challenge that needs to be addressed. To avoid biased models, better methods to avoid bias in training data must be introduced. It is crucial to develop training and academic approaches that enable better model training and incorporate equity in all aspects of healthcare to avoid bias.

The use of AI has opened a number of new concerns and frontiers for innovation. Further study of where true clinical benefit may be found in AI use is needed. To address these challenges and ethical concerns, healthcare provider organizations and software companies must focus on developing data sets that accurately model healthcare data while ensuring anonymity and protecting privacy. Additionally, partnerships between healthcare providers, systems, and technology/software companies must be established to bring AI tools into practice in a safe and thoughtful manner. By addressing these challenges, healthcare organizations can harness the potential of AI while upholding patient safety, privacy, and fairness.

[spookysaladchaos]

Metrology Software, Global Market Size Forecast, Top 10 Players Rank and Market Share

Metrology Software Market Summary

Metrology Software is a type of geometries measuring, evaluation, inspection and management software that can increase the performance and production of measuring operations.

According to the new market research report “Global Metrology Software Market Report 2024-2030”, published by QYResearch, the global Metrology Software market size is projected to reach USD 1.11 billion by 2030, at a CAGR of 5.6% during the forecast period.

Figure.   Global Metrology Software Market Size (US$ Million), 2019-2030

Above data is based on report from QYResearch: Global Metrology Software Market Report 2024-2030 (published in 2024). If you need the latest data, plaese contact QYResearch.

Figure.   Global Metrology Software Top 10 Players Ranking and Market Share (Ranking is based on the revenue of 2023, continually updated)

Above data is based on report from QYResearch: Global Metrology Software Market Report 2024-2030 (published in 2024). If you need the latest data, plaese contact QYResearch.

According to QYResearch Top Players Research Center, the global key manufacturers of Metrology Software include Hexagon, Carl Zeiss, InnovMetric, Renishaw, Metrologic Group, FARO Technologies, Ametek, 3D Systems, Quality Vision International (QVI), Perceptron, etc.

In 2023, the global top 10 players had a share approximately 85.0% in terms of revenue.

Figure.   Metrology Software, Global Market Size, Split by Product Segment

Based on or includes research from QYResearch: Global Metrology Software Market Report 2024-2030.

In terms of product type, currently Cloud-based is the largest segment, hold a share of 75.5%.

Figure.   Metrology Software, Global Market Size, Split by Application Segment

Based on or includes research from QYResearch: Global Metrology Software Market Report 2024-2030.

In terms of product application, currently Power and Energy is the largest segment, hold a share of 22.5%.

Figure.   Metrology Software, Global Market Size, Split by Region

Based on or includes research from QYResearch: Global Metrology Software Market Report 2024-2030.

About QYResearch

QYResearch founded in California, USA in 2007.It is a leading global market research and consulting company. With over 16 years’ experience and professional research team in various cities over the world QY Research focuses on management consulting, database and seminar services, IPO consulting, industry chain research and customized research to help our clients in providing non-linear revenue model and make them successful. We are globally recognized for our expansive portfolio of services, good corporate citizenship, and our strong commitment to sustainability. Up to now, we have cooperated with more than 60,000 clients across five continents. Let’s work closely with you and build a bold and better future.

QYResearch is a world-renowned large-scale consulting company. The industry covers various high-tech industry chain market segments, spanning the semiconductor industry chain (semiconductor equipment and parts, semiconductor materials, ICs, Foundry, packaging and testing, discrete devices, sensors, optoelectronic devices), photovoltaic industry chain (equipment, cells, modules, auxiliary material brackets, inverters, power station terminals), new energy automobile industry chain (batteries and materials, auto parts, batteries, motors, electronic control, automotive semiconductors, etc.), communication industry chain (communication system equipment, terminal equipment, electronic components, RF front-end, optical modules, 4G/5G/6G, broadband, IoT, digital economy, AI), advanced materials industry Chain (metal materials, polymer materials, ceramic materials, nano materials, etc.), machinery manufacturing industry chain (CNC machine tools, construction machinery, electrical machinery, 3C automation, industrial robots, lasers, industrial control, drones), food, beverages and pharmaceuticals, medical equipment, agriculture, etc.

[aibyrdidini]

HOW CORPORATES CAN USE THE PARAMETRIC MACHINE LEARNING ALGORITHMS IN A PRATICAL AI APPLICATIONS 

Parametric machine learning algorithms are a class of models that make strong assumptions about the form of the mapping function. They include Logistic Regression, Linear Discriminant Analysis, Perceptron, Naive Bayes, and Simple Neural Networks. These algorithms are characterized by their simplicity, speed, and ability to work well with less training data. However, they are constrained by the chosen functional form, more suited to simpler problems, and may not perfectly fit the underlying mapping function.

Here's a brief overview of the mentioned parametric machine learning algorithms:

1. Logistic Regression: A statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. It is commonly used for binary classification problems.

2. Linear Discriminant Analysis: A method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events.

3. Perceptron: A type of artificial neuron that is used in supervised learning. It helps to classify input data by finding the best linear equation that separates the classes.

4. Naive Bayes: A family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong independence assumptions between the features.

5. Simple Neural Networks: A basic form of neural network with a single layer of input nodes and an output layer.

These algorithms have their own advantages and limitations, and the choice of algorithm depends on the specific problem and the nature of the data.

Here are the Python code snippets for each of the mentioned parametric machine learning algorithms as a proof of concept:

### Logistic Regression

```python

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score, classification_report

# Assuming X_train, X_test, y_train, y_test are the training and testing data

model = LogisticRegression()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, predictions))

print(classification_report(y_test, predictions))

```

### Linear Discriminant Analysis

```python

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score, classification_report

# Assuming X_train, X_test, y_train, y_test are the training and testing data

model = LinearDiscriminantAnalysis()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, predictions))

print(classification_report(y_test, predictions))

```

### Perceptron

```python

from sklearn.linear_model import Perceptron

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score, classification_report

# Assuming X_train, X_test, y_train, y_test are the training and testing data

model = Perceptron()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, predictions))

print(classification_report(y_test, predictions))

```

### Naive Bayes

```python

from sklearn.naive_bayes import GaussianNB

from sklearn.model_selection import train_test_split

from sklearn.metrics import accuracy_score, classification_report

# Assuming X_train, X_test, y_train, y_test are the training and testing data

model = GaussianNB()

model.fit(X_train, y_train)

predictions = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, predictions))

print(classification_report(y_test, predictions))

```

### Simple Neural Networks

```python

import tensorflow as tf

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

# Assuming X_train, X_test, y_train, y_test are the training and testing data

model = Sequential([

    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),

    Dense(64, activation='relu'),

    Dense(1, activation='sigmoid')

])

model.compile(optimizer='adam',

              loss='binary_crossentropy',

              metrics=['accuracy'])

model.fit(X_train, y_train, epochs=10, batch_size=32)

```

These code snippets provide a proof of concept for implementing each of the parametric machine learning algorithms in Python.

RDIDINI PROMPT ENGINEER.

[gretaicom]

In 1958, AI made a promise to create humanlike machines-A roomsize computer equipped with a new type of circuitry, the Perceptron, was introduced to the world in 1958 in a brief news story buried deep in The New York Times. The story cited the U.S. Navy as saying that the Perceptron would lead to machines that...

[myprogrammingsolver]

Deep Learning - Homework 1][5]

In this homework, we will learn how to implement backpropagation (or backprop) for “vanilla” neural networks (or Multi-Layer Perceptrons) and ConvNets. You will begin by writing the forward and backward passes for different types of layers (including convolution and pooling), and then go on to train a shallow ConvNet on the CIFAR-10 dataset in Python. Next you’ll learn to use [PyTorch][3], a…