Freelancers using TensorFlow in Lahore
Freelancers using TensorFlow in Lahore
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Yasir Basheer
Lahore, Pakistan
Web Designer & Developer | Framer, Webflow & Figma
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Hired
14
Followers
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Web Designer & Developer | Framer, Webflow & Figma
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O-A: Predictive Demand Dashboard
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4
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Vela Bottle — Framer Product Landing Page
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3
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Kava by Mitra9: Shopify Ecommerce
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4
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EM Cosmetics — Shopify CRO Investigation & Fix
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4
TensorFlow
(1)
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Hammad Bin Sajjad
Lahore, Pakistan
Software Engineer with a commitment to excellence
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Software Engineer with a commitment to excellence
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Pixel Character Sprites Generation
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26
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Global Pixel Chat
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11
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E-Commerce Website Clone
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20
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Basic Social Media App
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8
TensorFlow
(1)
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Zohaib Khalid
Lahore, Pakistan
Transforming Data with AI and Full Stack Solutions
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Transforming Data with AI and Full Stack Solutions
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AI-Powered Customer Service Chatbot
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9
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Data-Driven Healthcare Insights Dashboard
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14
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Betting-Project-Using-Arb-Finder
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10
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Hazard-Assessment-To-Make-Communities-Fire-Smart
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10
TensorFlow
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Ali Jafar
Lahore, Pakistan
Affordable Web Developer, Web Scraper & Automation Expert
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Affordable Web Developer, Web Scraper & Automation Expert
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Project-Trojan
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6
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alijafarkamal/IMDB-clone
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5
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Data_analysis
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4
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GitHub - alijafarkamal/Trojan-Scraper: It contains my scraping …
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5
TensorFlow
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Usman Haider
Lahore, Pakistan
AI/ML & Data Solutions Engineer
New to Contra
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AI/ML & Data Solutions Engineer
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Trained a DreamBooth LoRA model to generate high-quality, personalized image outputs with consistent subject identity across different prompts and styles. The project involved dataset preparation, image captioning, and fine-tuning diffusion models using LoRA for efficient training and deployment. The solution enables fast generation of customized visuals while preserving subject consistency, style control, and high fidelity, suitable for creative, branding, and content generation use cases.
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Retail Knowledge Graph In this project, we built a semantic knowledge graph tailored to the retail industry. The pipeline involved developing AI agents to transform heterogeneous data into standardized formats. Ontologies were created to represent domain knowledge accurately. Using Gemini models and LangChain, user queries were converted into Cypher queries to retrieve insights from a Neo4j database. We utilized an MCP server for orchestration and LangSmith for secure login and audit trails. This system enhances complex data exploration for non-technical users.
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Student Medical Chatbot Built a chatbot to assist MBBS students in navigating medical literature. Leveraged Llama Index and fine-tuned language models to ensure accuracy. Embeddings were stored in OpenSearch, hosted on AWS. The Django backend included secure authentication and session management for a robust user experience.
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Prompt Engineering Mini-Academy is a digital learning product built using Kajabi. It helps users learn how to write better AI prompts and use AI tools for daily tasks such as writing, research, summarization, and productivity. The problem it solves is that many people use AI tools without a proper structure, which leads to weak or generic results. This product gives users a clear learning path, practical prompt templates, and workflow examples to improve the quality of their AI outputs. I used Kajabi to create the landing page, email capture form, downloadable prompt resource, product offer, checkout page, and course structure. A sample video is attached to demonstrate the product flow and user experience.
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71
TensorFlow
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Fariha Muazzam
Lahore, Pakistan
I build production-ready AI systems that actually ship
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I build production-ready AI systems that actually ship
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At Formulatrix, I led the development of a computer vision pipeline for RockMaker, a biotech product used in crystallization experiments. I built and deployed object detection and image classification models that improved scoring accuracy by 13%, reducing manual effort for scientists and increasing customer satisfaction. The solution was productionized with Docker and CI/CD pipelines, ensuring scalability and reliability across client sites.
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Satellite imagery is the ultimate stress test for Computer Vision. It is not just about detection; it is about architecting precision across massive, high-resolution datasets where every pixel counts. I have been building AI pipeline to segment parking lots, walkways, and structures from aerial data. The challenge is not just the mode, it is the Infrastructure: - Tiling & Inference: Processing massive dimensions without losing small-object context. - Robust Pre-processing: Using OpenCV to handle varying lighting and atmospheric "noise." - Cloud Orchestration: Turning heavy segmentation models into responsive, scale-ready tools. It is about turning raw pixels into a reliable data layer for the business. Currently opening 20 hours/week for long-term partnerships in Computer Vision & AI Systems.
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AI-based Video Generation Pipeline
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I designed and delivered an end-to-end MVP for a real-time AI talking avatar system, where live browser speech is converted into an AI-generated response and rendered through a lip-synced avatar video on GPU hardware. The objective was to validate technical feasibility, latency characteristics, and perceived real-time interaction before committing to production hardening. The system integrates speech-to-text, LLM-based reasoning, text-to-speech, and video synthesis into a single, runnable pipeline, deployed on an A100 GPU.
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301
TensorFlow
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Ali Hamza
Lahore, Pakistan
Industrial Engineer: Data Scientist & Web Developer
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Industrial Engineer: Data Scientist & Web Developer
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Identification of fabric defects using Deep Learning
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12
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Made a Ecommerce WordPress Website for Real Estate Business
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1
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A Website for the best choreographer in California, JOEY L DOWDY
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37
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Custom WordPress Website for World of Truffle
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17
TensorFlow
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Zuha Junaid
Lahore, Pakistan
AI Engineer & Creative Strategist create Immersive Solutions
New to Contra
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AI Engineer & Creative Strategist create Immersive Solutions
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Facial Emotion Recognition AI Designed and trained a custom CNN to classify human facial expressions into 7 categories using the FER-2013 dataset. Built an interactive Streamlit dashboard for real-time emotion prediction from images. Implemented class weighting to address dataset imbalance and improved model performance. Custom CNN Architecture Real-time Dashboard 84% Accuracy Technologies Python TensorFlow Keras CNN Streamlit
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Zuha Junaid - 3D AI & Software Engineer Portfolio A modern, immersive 3D portfolio website showcasing AI/ML projects, full-stack development expertise, and technical skills. Built with cutting-edge web technologies and designed with glassmorphism aesthetics. 🌐 Live Portfolio: https://zuha3dport-s3994me9.manus.space ✨ Features 🎨 Design & Aesthetics Dark Glassmorphism: Frosted glass cards with backdrop blur effects 3D Animations: Animated particle background with mouse interaction Responsive Design: Fully optimized for desktop, tablet, and mobile devices Smooth Transitions: Elegant page transitions and hover effects Premium Color Palette: Dark blue (#0a0e27) with teal (#1dd1a1) and purple (#a29bfe) accents 📄 4 Complete Pages Home - Hero section with animated 3D background and quick stats About - Technical skills showcase, education, and interests Projects - 4 major projects with detailed descriptions and technologies Contact - Contact form and social media links 🎯 Interactive Elements Animated particle background system Glass card hover effects Staggered entrance animations Smooth page navigation Responsive contact form Social media integration
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Executive Summary The primary objective of this project was to identify and mitigate security vulnerabilities within a custom-built academic portal. By implementing a defense-in-depth strategy, the assessment utilized multiple security testing methodologies to ensure a robust security posture. The evaluation revealed moderate security risks, primarily involving server misconfigurations and missing security headers.
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Al-Insaan Care: A Supportive App for Illiterate Needy People Al-Insaan Care is a mobile application specifically designed to bridge the accessibility gap for illiterate and needy individuals. By removing traditional text-based barriers, the platform empowers users to request and receive essential financial aid and material donations—including food, clothing, electronics, and blood—with dignity and ease. Project Overview The primary goal of Al-Insaan Care is to provide an inclusive, user-friendly experience for populations that struggle with literacy. The application leverages multimedia tools to ensure that navigating the path to assistance is as intuitive as possible. Key Features Accessibility-First Design: Information is presented through visuals, audio instructions, and videos rather than text. Voice-Activated Requests: Users can submit vital information (CNIC, address, house details) via voice messages or direct calls. Thorough Verification: An administrative department validates all requests to ensure aid reaches those truly in need. Streamlined Distribution: Integrated delivery services handle the distribution of material goods, while money and blood are managed through direct channels. Social & Community Engagement: A social feed allows for community interaction and updates on humanitarian efforts. UI & User Experience The interface is designed to be clean and minimalist, focusing on high-contrast iconography and simple workflows. Main Components Section AND Description Donation Portal: Uses icons (cutlery for food, shirt for clothing, etc.) to help users identify categories without reading. Checkout Flow: A simple process with a 100% discount model for needy recipients, resulting in a total cost of Rs.0. Dashboard: Provides administrators and donors with a visual overview of contributions and user growth through line graphs. Chat Interface: Includes a microphone option for instant voice commands and communication with support staff. Activity Feed: Tracks real-time user interactions, likes, and comments to foster a supportive community. How It Works Request: A user in need accesses the app and submits their details using voice commands or multimedia tools. Verify: The administrative team reviews the voice-submitted data and validates the request. Fulfill: Donors or the organization provide the requested items. Deliver: The delivery department ensures the items reach the verified address via courier or direct distribution.
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TensorFlow
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