ClearSet.AI

Did you know that every successful analysis and decision-making starts with clean data? 🧹✨ The act of eliminating errors, correcting discrepancies, and making sure all data is accurate, thorough, and usable is known as data cleaning. Consider it your information's spring cleaning; without it, even the greatest technologies or strategies may not work as intended. What makes it so essential? Consider attempting to construct a house out of poor materials; the building just cannot be supported. The same is true for data-driven choices; unreliable data can result in missed opportunities, false insights, and resource waste. You can be sure that every insight is trustworthy, every choice is well-informed, and every plan is based on the truth by cleansing your data. Therefore, start with clean data whether you're working on marketing campaigns, financial projections, or consumer analytics. It's the first step to success, clarity, and confidence! 🌟 #DataCleaning #DataQuality #SmartDecisions #BusinessGrowth #PersonalGrowth

We hope this new year thus far is bringing you excitement of what’s to come. Enjoy your day and focus on the future! Leave 2024 behind because today is a new day and a new year! 🎊🥳 The team at ClearSet.AI says to you all… HAPPY NEW YEAR! 🎆🎈Let’s welcome in 2025! 🤖🦾🥂 #2025 #newyears #happynewyear #artificialintelligence #aisoftwaredevelopers #cheerstothenewyear

Happy New Year! 🎈🎊🎆 We hope this new year brings you joy, growth, and elevation. The ClearSet.AI team is elated to see what this new year has in stored for us and our supporters! Cheers! 🥂🥳🤖🦾 #2025 #newyear #artificialintelligence #happynewyear #aisoftwaredevelopment

Once again, congratulations to ClearSet.AI and to other successful business owners who were apart of the USF Diversity and Skanska 2024 USF Mentor Protégé Program! We are still in awe of achieving this opportunity. 🤖🚧 “Thank you to our supporters. Thank you USF and Skanska. Thank you to my team. I am very grateful and blessed!” - Founder & CEO at ClearSet.AI 🦾💚💙

Thank you to our supporters! We hope you enjoyed your Christmas! 🎁🎄 Happy holidays from the ClearSet.AI team 🤖🦾💚 We are so grateful for the ongoing support and cherish each and every one of you. 👏🏿🤖 #happyholidays #MerryChristmas #artificialintelligence #thankyou

Merry Christmas from the ClearSet.AI team!🎄🎁 We wish you warmth, joy, and love ❤️💚🎁

Thank you FaithTech Tampa Bay chapter for having our founder Yasmine Gardiner as a panelist this Thursday 11/21 at 6pm at Embarc Collective! We are excited to be apart of this fantastic organization as a proud believer in Christ. It is He who gets the glory, and we are blessed to be a vessel. 🙏🏿 Who is FaithTech? FaithTech is a global tech community for Christ. We exist to help people in the tech ecosystem find community and steward their skills to glorify God. ✝️ #speaker #panelist #artificialintelligence #founder #ceo

ClearSet.AI presents our newest generative AI case study... Project "REDS" 🤖🏠 Chapter 5: GAN Image 1! 🏠 Let's recap chapter 4! 😀⬇ In chapter 4, we explained what preprocessing looks like in a programming format. We were able to preprocess the datasets to start training our generative adversarial network (GAN)! Now let's discuss what our GAN outputted for us: We successfully trained the GAN and obtained some intriguing results! In the image, the top row displays the input images, while the bottom row shows the generated ones. The model appears to generalize effectively! 👏 The segmented images produced can be utilized for future applications, such as feature extraction or as inputs to create realistic impressions in Unreal Engine or Unity. The GAN processes RGB images of facades and generates segmentations that can be used for realistic depth in Unity or Unreal Engine, enabling future artistic expressions. Our next step is to train on StyleGAN2, which will allow us to generate RGB images of facades. We've also developed some preliminary code in the Pix2Pix GAN Collab Notebook that stitches together four sides to create a fully segmented 3D building, ready to be imported into Unity or Unreal Engine. We want the community of Tampa Bay and its region to follow along with us on this very cool GenAI case study! 🙂 This is chapter 5 of ClearSet.AI Project REDS! 📖 Follow along the journey, as each week will be a new chapter in our case study. This is a case study dedicated to strengthening our community, environment, and construction professionals! 😃 #tampabay #genai #pinellascounty #hillsboroughcounty #stpete #clearwater #tampafl #community #realestate #realestatedeveloper #construction #casestudy #aicasestudy #tech #ai #environment #simulation #preprocessing #datasets #gans

ClearSet.AI presents our newest generative AI case study... Project "REDS" 🤖🏠 Chapter 4: Behind the "Preprocessing" Scenes 🏠 Let's recap chapter 3! 😀⬇ In chapter 3, we completed preprocessing the architectural datasets (which means adjusting, filtering, or adding to the data before analyzing it, which is an important part of cleaning "wonky" data for precise analysis). Chapter 4! 🤖⬇ Woohoo, we made it to chapter 4. 🙌 Chapter 4 is about understanding what preprocessing looks like in a programming format. Take a look at the video. The video entails how we were able to preprocess the datasets to start training our generative adversarial network (GAN). Let's discuss the video: ▶ Preprocessing in the context of the Delaunay triangulation typically involves preparing your data before applying the Delaunay algorithm (a method for triangulating a set of points such that no point lies inside the circumcircle of any triangle, resulting in a network of well-formed triangles). For geographic information systems (GIS), the Delaunay triangulation is employed to create terrain models and contour maps from elevation data. ▶ We downloaded the SVGtoPNG application to use Java code to read the SVGs into PNGs. ▶ The background of the blueprints will create the foundation for analyzing different scenarios. ▶ The doors by floorplans, parking lots, and footprints in each room by floorplans are part of strengthening the foundation of the GAN. ▶ The GAN will contain xml, png, and gom files to give full in-depth characteristics of the floor plans. ▶ The "combined_points" contained files of landmarks, districts, and historical centers in Pinellas County. ▶ The "combined_polygons" contained files of zoning areas, EMS grids, and areas of sewer and water in Pinellas County. ▶ The conditional GAN should reference the different lines and points to correctly and successfully generate an output. We are now ready to train our GANs! 👏🤖  We want the community of Tampa Bay and its region to follow along with us on this very cool GenAI case study! 🙂 This is chapter 4 of ClearSet.AI Project REDS! 📖 Follow along the journey, as each week will be a new chapter in our case study. This is a case study dedicated to strengthening our community, environment, and construction professionals! 😃 #tampabay #genai #pinellascounty #hillsboroughcounty #stpete #clearwater #tampafl #community #realestate #realestatedeveloper #construction #casestudy #aicasestudy #tech #ai #environment #simulation #preprocessing #datasets #gans 

ClearSet.AI presents our newest generative AI case study... Project "REDS" 🤖🏠 Chapter 4: Behind the "Preprocessing" Scenes 🏠 Let's recap chapter 3! 😀⬇ In chapter 3, we completed preprocessing the architectural datasets (which means adjusting, filtering, or adding to the data before analyzing it, which is an important part of cleaning "wonky" data for precise analysis). Chapter 4! 🤖⬇ Woohoo, we made it to chapter 4. 🙌 Chapter 4 is about understanding what preprocessing looks like in a programming format. Take a look at the video. The video entails how we were able to preprocess the datasets to start training our generative adversarial network (GAN). Let's discuss the video: ▶ Preprocessing in the context of the Delaunay triangulation typically involves preparing your data before applying the Delaunay algorithm (a method for triangulating a set of points such that no point lies inside the circumcircle of any triangle, resulting in a network of well-formed triangles). For geographic information systems (GIS), the Delaunay triangulation is employed to create terrain models and contour maps from elevation data. ▶ We downloaded the SVGtoPNG application to use Java code to read the SVGs into PNGs. ▶ The background of the blueprints will create the foundation for analyzing different scenarios. ▶ The doors by floorplans, parking lots, and footprints in each room by floorplans are part of strengthening the foundation of the GAN. ▶ The GAN will contain xml, png, and gom files to give full in-depth characteristics of the floor plans. ▶ The "combined_points" contained files of landmarks, districts, and historical centers in Pinellas County. ▶ The "combined_polygons" contained files of zoning areas, EMS grids, and areas of sewer and water in Pinellas County. ▶ The conditional GAN should reference the different lines and points to correctly and successfully generate an output. We are now ready to train our GANs! 👏🤖  We want the community of Tampa Bay and its region to follow along with us on this very cool GenAI case study! 🙂 This is chapter 4 of ClearSet.AI Project REDS! 📖 Follow along the journey, as each week will be a new chapter in our case study. This is a case study dedicated to strengthening our community, environment, and construction professionals! 😃 #tampabay #genai #pinellascounty #hillsboroughcounty #stpete #clearwater #tampafl #community #realestate #realestatedeveloper #construction #casestudy #aicasestudy #tech #ai #environment #simulation #preprocessing #datasets #gans