Alexnet tensorflow jobs
...must be available both as a responsive web application and as native or cross-platform mobile apps, all drawing from a single codebase whenever practical to streamline future maintenance. Core expectations • Modern, engaging UI/UX that feels consistent across web, iOS and Android. • A secure, multi-tenant architecture so each school’s data remains isolated. • An AI layer (think Python, TensorFlow/-Lite, or similar) that plugs into the learning workflow—for example adaptive content or real-time feedback—without locking us into a specific vendor. • Real-time sync between devices, even on spotty school Wi-Fi, and offline caching for mobile. • Admin dashboard with role-based access, permissions and exportable analytics. • C...
...fast delivery (within 24 hours) - Long-term work possible if successful Scope of Work: - Diagnose deep learning pipeline issues - Fix model execution errors - Debug training / inference workflow - Resolve dependency or environment conflicts - Optimize pipeline stability - Ensure end-to-end execution works correctly - Provide brief documentation of fixes Technical Stack: - Python - PyTorch / TensorFlow - HuggingFace / Transformers - CUDA / GPU acceleration - Docker / Linux environment - API integration & Data preprocessing pipeline Requirements: - Strong experience in Deep Learning production workflows - Experience debugging complex AI pipelines - Comfortable working under urgent timelines and ability to start immediately Timeline: Start: Immediately. Expected turnaround: ...
... so I need someone who is comfortable scoping requirements, selecting the right approach—whether that ends up being NLP with transformers, image recognition with convolutional networks, or a recommendation engine—and then turning that plan into working Python code. You should be able to help shape the data strategy, handle preprocessing and feature engineering, train and fine-tune models in TensorFlow or PyTorch, and wrap everything in a clean API or lightweight web service for deployment on AWS or another cloud platform. Clear, well-commented code, version-controlled in Git, and concise documentation are must-haves so I can maintain and iterate on the solution after delivery. If you enjoy tackling green-field AI projects and can move quickly from idea to demo to li...
Looking for a highly skilled retail, merchandising, and pricing Data Scientist with deep expertise in AI, Generative AI, and NLP, who has hands-on experience building the following and can walk through in-depth examples and solutions they have implemented across diverse real-world scenarios to drive scalable retail, merchandi...accuracy. Deployment & Integration: Integrate solutions with applications and data systems via APIs and web services, ensuring scalability and reliability. Research & Development of Emerging Technologies: Stay updated on the latest AI/ML advancements and explore opportunities to incorporate innovations into merchandising and pricing transformation initiatives. Frameworks & Tools: TensorFlow, PyTorch, OpenAI, LangChain, and other mod...
...methodology into clean, reproducible code. The core help I’m after is coding itself—covering the full pipeline from data preprocessing through model training to final evaluation and visualisation. I need datasets, well-documented Python scripts or notebooks that I can run end-to-end on my own machine (or a Colab instance). Expect to work with common libraries such as pandas, NumPy, PyTorch or TensorFlow, Hugging Face Transformers, plus Matplotlib or Seaborn for charts—use whichever combination best suits the objectives while keeping dependencies manageable. Deliverables
 • Data preprocessing module that loads the provided datasets, cleans them, applies any necessary tokenisation and splits them into train/validation/test sets. • Training script tha...
Looking for a highly skilled Data Scientist with deep expertise in AI and Generative AI to lead cutting-edge retail merchandising and pricing analytics initiatives, driving scalable forecasting, optimization, and intelligent ...-Implement optimization models to improve operational efficiency and maximize customer value. Manage the full machine learning lifecycle, including model monitoring, retraining, experiment tracking, and performance evaluation. -Develop and maintain CI/CD pipelines for reliable and scalable deployment of data science solutions. -Work with modern machine learning frameworks and tools such as TensorFlow, PyTorch, OpenAI, and LangChain. -Collaborate with cross-functional teams and integrate solutions with existing applications and data systems via APIs and web...
...common pesticide residues on fresh fruit and vegetable samples. My current lab setup streams CSV files over USB; if you have dealt with other device protocols, feel free to propose an efficient data-capture approach. Core requirements • A clean Python pipeline that parses the spectra, performs any necessary preprocessing (baseline correction, smoothing, normalization), and feeds the data into a TensorFlow model. • A well-documented training notebook + scripts so the model can be re-trained when new pesticides or produce types are added. • (Optional but welcome) a complementary computer-vision module. If you have experience with object detection, segmentation, or classic feature extraction, show me how you would fuse image cues with the spectral output to boo...
...can 1) recognise each image, 2) detect all relevant objects inside it, and 3) optionally segment those objects when that adds value to the overall result. The primary goal is accurate detection and classification, but I’d like the system to be flexible enough to switch on pixel-level segmentation whenever it boosts performance. Here is what I’m after: • A clean Python pipeline (PyTorch or TensorFlow preferred, with OpenCV for preprocessing) that ingests my custom images, trains suitable models, and exposes an easy inference script. • Clear evaluation: confusion matrices, mAP/IoU scores, and plots of loss/accuracy over epochs so I can judge progress at a glance. • A concise, publication-ready report written in LaTeX. I will supply the template; yo...
I need an AI-driven workflow that ingests raw video (MP4/MOV) and automatically follows a chosen object through every frame, delivering a clean alpha-masked output I can drop straight into my edit. The goal is to save me from manual rotoscoping; accuracy and speed are both critical. Here’s how I picture the collaboration: you build or adapt a model—using tools such as PyTorch, TensorFlow, Detectron2, or a proven OpenCV pipeline—that detects the target, keeps the mask tight even during fast motion, and exports either a keyed video or a PNG sequence. A small sample clip will be provided for you to demonstrate proof of concept; once results look solid we’ll run the system on the full batch. Deliverables • Python or command-line script (with all depe...
...AI-powered image-recognition tool that focuses exclusively on identifying everyday household items. The core requirement is clear: when the model sees a photo, it should reliably detect and classify objects such as chairs, tables, kettles, lamps, and similar items that people typically keep at home. Here is how I picture the workflow and final hand-off: • Model: A well-trained neural network (TensorFlow, PyTorch, or a comparable framework) tuned for object detection/classification. • Dataset handling: Either you assemble and label a suitable open-source dataset or guide me on licensing a ready-made one; in either case, the final dataset or clear reproducibility steps must be included. • Inference pipeline: A simple script or lightweight API endpoint so I can ...
...feeds or historical fault libraries later, so a clean, extensible data pipeline matters. Key deliverables • Cross-platform mobile app (iOS + Android) built with a modern stack—React Native, Flutter, or another framework you are comfortable with. • Fault-tree engine that mirrors Airbus AMM logic and lets me update procedures without redeploying the whole app. • Predictive module (Python/TensorFlow, PyTorch, or similar) that ranks probable troubleshooting branches based on past fixes. • Secure local/remote storage of maintenance logs, plus export in CSV or JSON for MIS upload. • Clear documentation and a short video demo showing the workflow on an A320 use-case. Acceptance criteria 1. Given a sample logbook entry “F/CTL PRIM1 FAULT...
...returns its class. • Documentation: concise README explaining setup, dependencies, and how to retrain with fresh data. Acceptance criteria 1. Minimum F1-score of 0.85 on the hold-out test set I will supply. 2. Reproducible environment ( or ). 3. Code delivered via private Git repository or ZIP file. Tools you might consider include Python 3.11, PyTorch or TensorFlow, HuggingFace Transformers, spaCy, and scikit-learn; feel free to propose alternatives if they achieve equal or better results. I will review interim results as soon as you have an initial baseline, then we can iterate on hyper-parameters, class imbalance handling, and deployment details....
...shots, slight rolls, and images with minimal vertical cues. 2. Batch processing and an interactive before-after comparison are mandatory. 3. Front-end must run in any modern browser; a minimal back-end is fine as long as uploads are secure and temporary. 4. Code must be clean and well-documented so I can extend it later. I have no fixed stack preference, so feel free to propose OpenCV, TensorFlow, or any combination of JavaScript (e.g. WebAssembly-compiled OpenCV), Python (FastAPI, Flask), or other technologies you’re comfortable with. Please outline: • The libraries and frameworks you’ll rely on. • Your approach to vanishing-point detection and homography estimation (e.g., RANSAC line clustering, deep-learning refinement, etc.). • Expected ...
...me: • Interactive graphics and videos must steal the show—they should illustrate key concepts, animate data, and invite the viewer to click, pause, or explore. • Avatars must look realistic, lip-sync flawlessly, and be easy to re-skin for future episodes. • The entire pipeline—from text input to final MP4—should run with minimal manual tweaking, whether you build it in Python with PyTorch/TensorFlow or orchestrate commercial generative-media APIs. Deliverables 1. A working proof-of-concept that accepts a text script, generates the realistic avatar narration, and stitches in AI-created graphics/video to produce a cohesive training module. 2. Source code plus clear setup and usage instructions. 3. A short sample episode built from one of my...
...machine-learning model (anomaly detection or sequence-based classification) optimised for on-device or near-edge execution. 2. Implement a decision layer that selects one of three responses—encrypt, quarantine, or alert—based on the model’s confidence score. 3. Provide clean, well-commented Python code (TensorFlow, PyTorch or scikit-learn are all acceptable) plus a short README explaining data preprocessing, hyper-parameters and how to port the model to an embedded runtime (e.g., TensorFlow Lite, ONNX). 4. Supply a small synthetic data set and demonstrate at least 90 % accuracy in distinguishing normal from suspicious activity during a live demo or recorded notebook. Acceptance criteria • Model trains and runs locally on a laptop within 10 min...
I need a lightweight image-classification prototype that cleanly separates iPads from iPhones. You will work in Google’s Teachable Machine so the end result can be demonstrated live to non-technical stakeholders and, if needed, exported for further use in TensorFlow or a Python pipeline later on. Data I will supply a mixed set of photos—my own device shots plus carefully curated stock images—so the training set covers varied angles, lighting, and backgrounds. Target performance The prototype should reach better than 90 % accuracy on fresh, unseen images. Please incorporate any practical tricks (augmentation, class-balance tweaks, transfer learning, etc.) that help hit this benchmark without overcomplicating the workflow. Workflow & knowledge transfer A...
...into Reinforcement Learning or NLP later, that flexibility will be a plus for the longer roadmap, but the immediate priority is Deep Learning mastery. The sessions will be delivered live (online or hybrid can be arranged), and I’ll rely on you to: • Shape a clear, week-by-week syllabus covering CNNs, RNNs, transformers, optimisation tricks, model interpretability and MLOps basics using Python, TensorFlow or PyTorch • Provide concise slide decks, hands-on notebooks (Jupyter/Colab) and at least three graded mini-projects that mirror industry use-cases • Guide learners through code reviews and Q&A, then wrap up with a capstone evaluation and feedback report All teaching material must be original or properly licensed, and ready for hand-off at the end ...
...training, validation, and test sets, with any necessary feature engineering you judge appropriate. I have no fixed preference on the final algorithm—linear models, tree ensembles, or a small neural network are all acceptable as long as they deliver solid predictive accuracy and are easy to retrain when I add more data. Please build the solution in standard Python tooling (pandas, scikit-learn, TensorFlow or PyTorch only if the accuracy gains justify it) and present the work in a Jupyter Notebook. Your notebook should walk me through: • data import, preprocessing, and exploratory visuals • model selection and cross-validated performance metrics • prediction of W/L ratio on unseen inputs • a short optimisation routine that searches the design space...
...dosage, batch number, expiry date, manufacturer, and any other legible data. • Return the result programmatically as a JSON object so it can be stored or sent to our API. Accuracy is more important than speed, but I still expect real-time feedback on focus and framing. You’re free to leverage mobile-friendly vision libraries (Google ML Kit, Tesseract, Vision Framework, etc.) or a custom TensorFlow-Lite model if that yields better results. Everything must run on-device; no cloud calls. Deliverables: 1. Full source code for the iOS and Android implementation (native, Flutter, or React Native—use what lets you hit the quality bar fastest). 2. A short read-me that explains build steps, dependencies, and the JSON schema. 3. Sample JSON output from at least t...
...machine learning model that reliably classifies each photo into the correct category. Your job is to design, train, and evaluate the full image-classification pipeline. You may build from scratch or fine-tune a proven architecture such as ResNet, EfficientNet, MobileNet, or a vision transformer—as long as the final model meets the accuracy targets we set together. Feel free to work in PyTorch or TensorFlow/Keras; I’m comfortable deploying either. What I’ll provide • A structured folder of training, validation, and test images • Category labels and a brief data dictionary • Access to a GPU instance if you need it What I need back 1. Clean, well-commented code (Jupyter notebook or Python scripts) that handles preprocessing, augmentation, trai...
...machine-learning model (anomaly detection or sequence-based classification) optimised for on-device or near-edge execution. 2. Implement a decision layer that selects one of three responses—encrypt, quarantine, or alert—based on the model’s confidence score. 3. Provide clean, well-commented Python code (TensorFlow, PyTorch or scikit-learn are all acceptable) plus a short README explaining data preprocessing, hyper-parameters and how to port the model to an embedded runtime (e.g., TensorFlow Lite, ONNX). 4. Supply a small synthetic data set and demonstrate at least 90 % accuracy in distinguishing normal from suspicious activity during a live demo or recorded notebook. Acceptance criteria • Model trains and runs locally on a laptop within 10 min...
I’m building a camera-based system that runs on an NVIDIA Jetson and, in real time, detects faces and recognises emotions. The entire solution must be coded in Python. For face localisation I’d like a fast deep-learning detector—SSD or YOLO—so the frame rate stays smooth on Jetson hardware. Once a face is found, a TensorFlow model should assign an emotion label (happy, sad, angry, surprised, neutral, etc.) together with its confidence score. The video stream has to overlay these results live, log every reading with a timestamp, and trigger a visual or audible alert whenever negative emotions are detected repeatedly within a short window. A lightweight dashboard served with either Streamlit or Flask will let me: • watch the annotated video feed •...
...application Assessment & Gap Analysis: (Optional/Phase 2) AI-driven tests or suggestions for courses to help candidates bridge skill RequirementsFrontend: Modern frameworks such as React, , or Angular for a responsive and intuitive : Scalable environments using Python (Django/Flask), Node.js, or Frameworks: Experience with OpenAI APIs, LangChain, TensorFlow, or PyTorch for building matching models and resume : Secure and efficient data handling with PostgreSQL, MySQL, or : Secure login/registration with role-based access control and optional OTP functional web application (responsive for mobile/desktop).Source code with clean, well-documented with third-party
...configure camera (USB/IP) • Implement real-time face detection • Integrate pre-trained emotion recognition model (no training required) • Display emotion + confidence score live • Log events with timestamps • Build a simple dashboard (Streamlit or Flask) • Add alert logic (e.g. repeated negative emotion → warning) ⸻ Required Skills • NVIDIA Jetson (VERY IMPORTANT) • Python • OpenCV • TensorFlow or PyTorch • Real-time video processing • Linux / Ubuntu • Flask or Streamlit Bonus: • GStreamer / DeepStream experience • Previous edge AI or surveillance projects ⸻ Deliverables • Fully working prototype on Jetson • Clean source code • Installation/setup guide • Dem...
...professional monitoring console: two circular gauges for the intensity and effort scores, plus a central sphere whose colour and animation state change with cumulative findings. A scrollable timeline should let me jump straight to any highlighted event and hear a 10- to 30-second trimmed clip without re-encoding the whole file. Technical freedom is yours—if Python libraries such as librosa, PyTorch or TensorFlow serve the detection, great; if you prefer another stack, convince me. The front end can be React, Vue, or a comparable modern framework; D3, or Plotly can drive the visualisation layer. Deliverables • Trained detection model and reproducible inference script • REST or local API that produces per-second labels and WOB metrics • Web dashboard r...
AI/ML Engineer (7+ Years Experience) J...projects and collaborate with cross-functional teams to deliver scalable AI solutions. Key Responsibilities: Develop and deploy machine learning models Perform data analysis, preprocessing, and feature engineering Work with AI/ML frameworks like TensorFlow, PyTorch, or Scikit-learn Optimize models for performance and scalability Collaborate with teams to deliver project requirements Requirements: 7+ years of experience in AI/ML or Data Science Strong knowledge of Python Experience with Machine Learning, Deep Learning Hands-on experience with TensorFlow / PyTorch Knowledge of NLP, Computer Vision, or similar domains Good problem-solving and communication skills Preferred Locations: Egypt, Philippines, Pakistan, Bangladesh, Indonesia,...
...configure camera (USB/IP) • Implement real-time face detection • Integrate pre-trained emotion recognition model (no training required) • Display emotion + confidence score live • Log events with timestamps • Build a simple dashboard (Streamlit or Flask) • Add alert logic (e.g. repeated negative emotion → warning) ⸻ Required Skills • NVIDIA Jetson (VERY IMPORTANT) • Python • OpenCV • TensorFlow or PyTorch • Real-time video processing • Linux / Ubuntu • Flask or Streamlit Bonus: • GStreamer / DeepStream experience • Previous edge AI or surveillance projects ⸻ Deliverables • Fully working prototype on Jetson • Clean source code • Installation/setup guide • Dem...
...create a user-friendly interface for easy interaction Key Features: Automated task scheduling and execution Natural Language Processing (NLP) for understanding user commands Data analysis and decision-making capabilities Integration with third-party tools (email, databases, apps) Real-time monitoring and reporting Technologies Used: Programming Languages: Python / JavaScript AI Frameworks: TensorFlow / PyTorch APIs: OpenAI API, Google APIs Database: MySQL / MongoDB Tools: Automation platforms like Zapier or custom scripts Working Principle: The system collects input from users (text or voice), processes it using AI models, and then performs the required task automatically. For example, it can read emails, categorize them, send responses, or update records in a database witho...
I need an end-to-end solution that can automatically detect marathon bib numbers from footage captured by two fixed ...upload/edit the bib-to-phone spreadsheet – swap the overlay logo. Deliverables 1. Fully working prototype deployed on a cloud VM or local server, ready for race-day use. 2. Source code with clear documentation and installation script. 3. Admin guide (PDF or short video) showing setup, operation, and troubleshooting. 4. One remote handover session for live Q&A. I’m open to your preferred stack—OpenCV, TensorFlow, PyTorch, FFmpeg, or a commercial vision API—so long as licensing fits an event production environment and the admin UI remains dead-simple. Please outline your proposed architecture, expected accuracy, and any hardware specs I s...
...dependencies. 3. Final raster layers align correctly with standard geographic projections and pass spot-checks against in-situ well data supplied later in the project. When you reply, focus on your experience with satellite hydrology, geospatial machine learning, and any prior work that combined stress-recharge assessments. Briefly outline the toolchain you prefer (e.g., Google Earth Engine, xarray, TensorFlow, scikit-learn) and how quickly you can deliver the first milestone of cleaned input data and an initial baseline model....
...detection) • Audio (spectral patterns, voice anomalies, speaker embeddings) Core Requirements: • Multi-modal deepfake detection (Image, Video, Audio) • Clean and scalable Python-based architecture • Support for datasets like FaceForensics++ and DFDC • Training, evaluation, and inference pipeline • Option to run via CLI, Jupyter Notebook, or executable (.exe) Tech Stack (Preferred): Python, OpenCV, TensorFlow/PyTorch, scikit-learn Audio processing tools (LibROSA, etc.) Frontend + Backend integration (optional but preferred) Additional Features (Bonus): • Web interface (FastAPI + frontend) • Runtime learning / feedback system • Lightweight dataset crawler • Performance optimization for low-resource systems Deliverables: &bul...
...research and implement reinforcement learning algorithms, experiment quickly, tune hyperparameters, and evaluate against clear success metrics. • Integration with existing systems – wrap trained models behind REST/GraphQL endpoints, containerise (Docker/Kubernetes), and wire everything into my current Python micro-services stack on AWS. Everything is Python-first, so fluency with PyTorch or TensorFlow, pandas, NumPy, and popular RL libraries (Stable-Baselines3, Ray RLlib, or similar) is expected. Familiarity with CI/CD (GitHub Actions), infrastructure-as-code, and basic DevOps will make collaboration smoother. Deliverables I’m expecting: 1. Reproducible training pipeline with documented code. 2. Baseline RL model that reaches the agreed-upon performance ben...
...appear only partially. The emphasis is on distribution-robust performance under severe occlusion. I am mainly looking for large models to be used e.g., Mamba / hybrid Transformer models for human pose estimation. The approach I have in mind mixes large Transformer backbones with geometric priors think part-affinity refinements, kinematic graph constraints, or similar. Frameworks such as PyTorch, TensorFlow, Detectron2, or MMPose are all fine as long as the pipeline stays fully reproducible on a single GPU. Deliverables • An occlusion-aware pose estimation model with source code and training scripts • Pre-trained weights plus a clear read-me on how to reproduce results end-to-end • A concise tech report detailing architecture choices, training schedule, metr...
...camera streams, runs fast and accurate inference, and pushes results reliably from edge devices. The current target hardware is NVIDIA Jetson, so every design choice—from model architecture to post-processing—must respect its compute limits while still keeping total end-to-end latency under 200 ms. The core work revolves around training, tuning, and deploying YOLO-style detectors in PyTorch (TensorFlow knowledge is welcome if it helps optimisation). You will refine the models for two challenging scenarios that matter most to our roadside installations: low-light environments and high-speed vehicle movement. Image enhancement, motion-blur compensation, and clever data-augmentation strategies are all fair game as long as they translate into measurable accuracy gains af...
...demonstrates how a Django web app can integrate a simple machine-learning model for product detection. The goal is strictly educational, so the ML portion only needs to prove the concept—high accuracy is not required. Core workflow Users will sign up or log in, upload an image of a product, and immediately see the detected product name. Behind the scenes a lightweight, preferably pre-trained, TensorFlow or OpenCV model can handle the detection. In the admin panel, product detection should sit front-and-center so I can verify images, correct labels if needed, and keep a record of the results. Inventory management Once a product is detected, I should be able to add or edit its description, adjust stock counts, and toggle status between In Stock, Out of Stock, or Pending...
I have a fully curated dataset and need an AI engineer who can turn it into a production-ready model that detects and classifies people, vehicles, and animals. The plan is to build a custom detector using YOLO and optimise it for low-latency inference with TensorFlow RT/TensorRT so it can run reliably on edge hardware as well as GPUs in the cloud. Here is what I’m expecting: • End-to-end training pipeline: data augmentation, transfer learning on the latest YOLO variant, and fine-tuning until we hit solid precision/recall numbers. • Exported weights plus a clean inference script (Python) that loads in under a second and returns bounding boxes, class labels, and confidences. • Clear documentation of your environment and commands so I can reproduce the resul...
My online learning cohort needs steady guidance across several Computer Science pillars at the intermediate level. The immediate focus is on: • Programming languages (think Python, Java, C++, or similar) • Data Structures & Algorithms • Database Management Systems (SQL-based a...Live or recorded instruction (60–90 minutes per topic). 3. Two graded exercises per session with solution walkthroughs. 4. A concise progress note after every fourth meeting highlighting strengths and next steps. Punctuality, conversational English, and familiarity with common tools—Git/GitHub, Jupyter, VS Code, MySQL or PostgreSQL, plus a mainstream ML library such as scikit-learn or TensorFlow—will keep lessons smooth. If this aligns with your expertise, let...
I ...or datasets—so students can practise between classes. • Timely feedback on assignments and concise progress reports so learners know where they stand. All students already have foundational knowledge; your role is to help them level up to industry-ready skills through real-world examples and project-based teaching. Familiarity with commonly used tools—Google Analytics, Figma, React, Python, TensorFlow or similar—is a plus, but what matters most is your ability to explain concepts clearly in both languages. If you’re passionate about teaching and can commit to a structured, results-oriented programme, let’s talk. Please share a brief overview of your teaching experience, preferred subject track, and a sample curriculum or recorded lecture s...
...systems Create reusable AI components, tools, and frameworks Support end-to-end AI system design across training, inference, monitoring, and lifecycle management Contribute to deployment reliability, reproducibility, CI/CD practices, and model monitoring Required Skills - 5+ years of experience in the AI/ML domain Strong Python skills Strong experience with AI/ML frameworks such as PyTorch, TensorFlow, or JAX Experience with LLM fine-tuning, prompt engineering, adapters, RAG, vector databases, and multimodal pipelines Strong grounding in classical ML, deep learning, NLP, CV, applied ML, and MLOps Experience designing end-to-end AI systems from training to inference to monitoring Experience with APIs, microservices, cloud platforms, and containers Strong problem-solving ability,...
I want to plug an AI engine directly into my Windows-based proprietary program so it can make sense of the data we already collect—primarily log/transcript text and sensor-generated numbers. At the moment the application stores everything locally; what is missing is a bridge that feeds this information to a model built in TensorFlow or PyTorch and then returns the predictions, classifications, or anomaly scores back into the software in real time (or near real time). Here is the outcome I’m aiming for: • A clean interface layer—DLL, API service, or direct Python-C# binding—that my program can call with raw records and receive structured results. • A working model (or framework for training one) capable of handling combined text + numerical inpu...
...the job is therefore twofold: • build a reliable face-detection pipeline specialised for personal-album style images (no security-camera angles, mostly smartphone shots in varied lighting), and • structure the results so I can analyse them later inside Python or export them to CSV for further data analysis. Please use a mainstream deep-learning framework you are comfortable with—OpenCV, TensorFlow, PyTorch, or a comparable library is fine—as long as the final code runs on Windows and can be triggered from a simple command line. The model can be pre-trained (e.g., a RetinaFace or MTCNN backbone) provided you fine-tune or post-process it so false positives stay minimal. Acceptance criteria 1. A script or notebook that takes an input folder path, pr...
I need a Convolutional Neural Network that can reliably detect targets within images and output consistent, reproducible results. You are free to recommend the most suitable framework—PyTorch or TensorFlow is fine—as long as the final solution can be trained and run on a standard GPU and easily re-trained with new data. Provide full documentation
...strategy, clear README, licence file and issue templates. • Example Python code or notebooks that demonstrate text-classification and sentiment-analysis pipelines I can extend. • Guidance on best practices for commits, pull requests, and versioning so I can manage future generative-AI features smoothly. We can use the frameworks you are most comfortable with—Hugging Face Transformers, PyTorch, TensorFlow, spaCy or similar—provided installation remains straightforward for someone with basic Python skills. When you respond, please share past work that shows similar repository structures or NLP projects you have delivered. That will help me judge fit quickly. Once we agree on the structure, I’m happy to give you collaborator access and schedule a sh...
...(Finacle, Oracle, Temenos, or similar) │ ├── Developed reconciliation engines for payments │ ├── Implemented fraud detection systems │ └── Deployed systems in regulated environments (finance, healthcare, government) NICE TO HAVE: ├── Experience with Africa's Talking SMS gateway ├── Knowledge of Levenshtein distance or fuzzy matching algorithms ├── Experience with ML model deployment (TensorFlow, PyTorch) ├── Previous work with microservices architecture ├── Experience with Elasticsearch, Kibana (ELK stack) ├── Familiarity with banking regulations (data protection, PCI-DSS) └── Existing relationships with telco or bank technical teams WHAT WE ALREADY HAVE (The Assets You'll Integrate) We are NOT starting from scratch. The following assets are already bui...
...(CSV or spreadsheet) Change the overlay logo Deliverables Fully functional prototype (cloud VM or local server) ready for race-day use Complete source code with clear documentation and installation steps Admin guide (PDF or short video) covering setup, usage, and troubleshooting One remote handover session for live Q&A Additional Notes Open to your preferred tech stack (e.g., OpenCV, PyTorch, TensorFlow, FFmpeg, or commercial APIs) Licensing must be suitable for event/commercial use The system must be extremely simple to operate for non-technical users When Applying, Please Include Proposed architecture Expected detection accuracy Recommended hardware specifications (for smooth race-day performance) Important: This system must be ready-to-use and designed for non-IT operator...
I need an end-to-end solution that can ...bib-to-phone spreadsheet – swap the overlay logo. Deliverables 1. Fully working prototype deployed on a cloud VM or local server, ready for race-day use. 2. Source code with clear documentation and installation script. 3. Admin guide (PDF or short video) showing setup, operation, and troubleshooting. 4. One remote handover session for live Q&A. I’m open to your preferred stack—OpenCV, TensorFlow, PyTorch, FFmpeg, or a commercial vision API—so long as licensing fits an event production environment and the admin UI remains dead-simple. Please outline your proposed architecture, expected accuracy, and any hardware specs I should budget for when you bid. READY TO USE SYSTEM NEEDED FOR NON IT SKILLED PER...
I need a lightweight and efficient Vision-Transformer-style model for brain tumor detection and classification using CT scans. The model should achieve high performance on accuracy, sensitivity, specificity, and F1 score. Requirements: - Develop a Vision-Transfo...a Vision-Transformer-based model - Use CT scans for training data - Output should include both tumor detection and classification - Optimize for accuracy, sensitivity, specificity, and F1 score Ideal Skills and Experience: - Expertise in deep learning and computer vision - Experience with Vision Transformers and histopathological image analysis - Proficiency in Python and relevant libraries (TensorFlow, PyTorch) - Strong background in medical imaging and classification tasks Please provide a portfolio showcasing similar...
