Pytorchemplois

...pré-traiter, reconstruire la géométrie, nettoyer le maillage et, si possible, produire un format standard (OBJ/FBX/GLB). • le code commenté, les modèles entraînés et un court guide d’installation. • une démonstration sur plusieurs scans fournis pour valider la qualité de sortie (maillage étanche, nombre de polygones raisonnable, artefacts minimaux). Je reste ouvert à vos choix techniques – PyTorch, TensorFlow, Blender Python, MeshLab, Cloud services… tant que le pipeline est reproductible. Si vous avez déjà travaillé avec des réseaux de type Neural Implicit Surface, point-cloud to mesh ou des approches similaires, précisez-le ; c’e...

...orienté « équilibre entre précision et complexité ». • Création d’un générateur de données d’entraînement : swing points + filler points pour reproduire les fluctuations de marché et augmenter le jeu de données. • Intégration des données OHLCV comme indicateur de base, avec la possibilité d’ajouter vos propres features si cela améliore la détection. • Scripts clairs (TensorFlow, Keras ou PyTorch) et commentaires détaillés pour que je puisse ré-entraîner ou ajuster le modèle. • Rapport succinct expliquant la méthodologie : préparation des données, architecture ...

...orienté « équilibre entre précision et complexité ». • Création d’un générateur de données d’entraînement : swing points + filler points pour reproduire les fluctuations de marché et augmenter le jeu de données. • Intégration des données OHLCV comme indicateur de base, avec la possibilité d’ajouter vos propres features si cela améliore la détection. • Scripts clairs (TensorFlow, Keras ou PyTorch) et commentaires détaillés pour que je puisse ré-entraîner ou ajuster le modèle. • Rapport succinct expliquant la méthodologie : préparation des données, architecture ...

...orienté « équilibre entre précision et complexité ». • Création d’un générateur de données d’entraînement : swing points + filler points pour reproduire les fluctuations de marché et augmenter le jeu de données. • Intégration des données OHLCV comme indicateur de base, avec la possibilité d’ajouter vos propres features si cela améliore la détection. • Scripts clairs (TensorFlow, Keras ou PyTorch) et commentaires détaillés pour que je puisse ré-entraîner ou ajuster le modèle. • Rapport succinct expliquant la méthodologie : préparation des données, architecture ...

Je souhaite mettre en place un flux complet qui, à partir d’une simple commande vocale, 1) interprète ma demande via reconnaissance vocale, 2) génère ou récupère un modèle IA approprié (Python / PyTorch++), 3) lance automatiquement une simulation physique, 4) affiche le résultat de façon animée, 5) restitue les valeurs numériques ainsi que les équations de mécanique classique à l’origine du phénomène, et 6) exporte le tout vers un banc de test de type MATLAB ou Octave. Livrables attendus : • Schéma d’architecture et script d’installation reposant exclusivement sur des outils gratuits ou open-source. • Prot...

...seconds for classification. Scalability Handle 10,000+ patient records efficiently. Availability 99.9% uptime, cloud-based deployment. Compliance GDPR, HIPAA, and other medical data regulations. 6. Technology Stack Component Technology Frontend React, Angular, or Vue.js Backend Node.js, Python (FastAPI/Django) Database PostgreSQL, MongoDB (for patient records) AI Model TensorFlow, Scikit-learn, PyTorch Security OAuth 2.0, JWT Authentication EHR Integration FHIR API, HL7 7. Project Timeline Phase Tasks Duration Phase 1: Planning Requirement analysis, system design 2 weeks Phase 2: Development Backend, AI model training, frontend UI 8 weeks Phase 3: Integration EHR system integration, API development 4 weeks Phase 4: Testing Unit testing, AI model validation 3 weeks Phase 5: De...

...Intégrer le modèle IA à l'application mobile via TensorFlow Lite, CoreML ou ONNX. Concevoir une interface utilisateur intuitive et réactive. Configurer le backend pour le stockage des données utilisateur et les mises à jour de modèle. Effectuer des tests pour garantir les performances de l'application sur divers appareils. Compétences Requises IA et Machine Learning : Familiarité avec TensorFlow, PyTorch, YOLO, Fastai ou modèles similaires. Mobile Development : Expertise en Swift (iOS), Kotlin (Android) ou frameworks cross-platform (Flutter/React Native). Back-end Development : Expérience avec Python (Flask/Django) ou Node.js pour les API. Connaissances Open Source : Capacité à t...

...l’analyse. Technologies et Outils TensorFlow : Pour l’analyse vidéo et la reconnaissance d’images. OpenCV : Pour le traitement d’images et la vision par ordinateur. PyTorch : Pour le développement de modèles de deep learning flexibles. Keras : Pour créer et entraîner des modèles de réseaux de neurones. Scikit-learn : Pour l’analyse de données et les prédictions statistiques. Compétences Requises Maîtrise de TensorFlow ou équivalent (par exemple, PyTorch) Maîtrise d’OpenCV ou équivalent (par exemple, SimpleCV) Maîtrise de PyTorch ou équivalent (par exemple, TensorFlow) Maîtrise de Keras ou équivalent (...

...train the algorithm. - **Validation**: Test the algorithm with validation data to evaluate its accuracy. - **Deployment**: Integrate the algorithm into the application for real-time predictions. ## Technologies and Tools for Analytics 1. **Backend** - **Language**: Python is ideal for data processing and machine learning. - **Machine Learning Frameworks**: Scikit-learn, TensorFlow, PyTorch. - **Database**: PostgreSQL or MySQL for storing school data. 2. **Frontend** - **Frameworks**: React.js, Vue.js, Angular for creating an interactive user interface. - **Visualization Libraries**: D3.js, , Plotly for displaying interactive graphs and tables. ## Development Plan for Analytics ### 1. Research and Analysis Phase - Identify available data and specific n...

...fréquentes dans les systèmes autonomes comme les voitures afin de détecter les obstacles (piétons, autres voitures, panneaux de signalisations) et les drones de surveillance. Les algorithmes de machine learning domine le monde d’Intelligence Artificielle récemment. Pour cela, nous allons utiliser un modèle de classification basé sur le machine learning. Pour plateforme logicielle, nous allons utiliser PyTorch. En utilisant cette technique, écrivez deux fonctions temps_cpu_app et temps_cpu_inf pour estimer le temps d'exécuter de l'apprentissage et de l'inférence. N'oubliez pas de commentez les lignes d'affichage. Pour l'inférence, il ne faut considérer que le t...

...then compare each face against a gallery to decide whether it is an identical match. • Serve three environments without extra rewrites: security-grade CCTV feeds, social-media style mobile uploads, and large photo-management archives. • Deliver low latency on a single modern GPU while still running acceptably on CPU-only hardware for lightweight deployments. I’m comfortable with either PyTorch or TensorFlow/Keras; use the framework you know best. A pre-trained backbone such as ResNet, MobileNet, or Vision Transformer is fine as long as you include the full training pipeline so I can continue to improve the model with fresh data. Deliverables 1. Source code with clear, commented modules for detection, embedding generation, and similarity matching. 2. Pr...

...traditional threat-intel techniques with machine-learning pipelines so the system continuously adapts as new data arrives. Here’s what success looks like to me: • A modular data-collection layer that can stream pcap, NetFlow, or similar log formats into a preprocessing engine. • Feature-engineering and model-training code written in Python (feel free to leverage Pandas, scikit-learn, TensorFlow, PyTorch—whatever best suits the task). • A detection component that scores incoming traffic and raises alerts via a simple REST API or CLI output. • Clear documentation covering setup, retraining, and how new data sources—such as endpoint events or social-media threat chatter—could be plugged in later. Because this is time-sensitiv...

...without a rewrite. On the modelling side, I want a workflow that lets me start a new model from scratch, feed in data, train, save, and then reuse that model inside the video generator. Ideally this is handled behind a clean dashboard rather than command-line steps. Core expectations • Intuitive UI/UX for both the video creator and the model-training console • Scalable backend (Python, PyTorch/TensorFlow, or comparable) with GPU support • Real-time preview for voice and style selections • Secure user accounts and storage for datasets, models, and rendered videos • Source code, build instructions, and a short hand-off call on completion If you have a reusable codebase or experience with generative media tools such as Stable Diffusion, FF...

Szukam doświadczonego developera, który stworzy dla mnie bota AI do ...rzeczywiste. Po stronie technicznej liczę na: • Model uczenia maszynowego lub kombinację algorytmów statystycznych reagujących w sekundowym interwale. • Integrację z order book, jeśli pozwala na to przepustowość API. • Dashboard lub prosty panel www/CLI do podglądu metryk, stanu konta i ręcznej interwencji. W zgłoszeniu napisz, proszę: 1. Jakiej dokładnie architektury i bibliotek (TensorFlow, PyTorch, CCXT, itp.) zamierzasz użyć. 2. Szacowany czas realizacji poszczególnych etapów. 3. Koszt wykonania oraz model późniejszego utrzymania i aktualizacji. Zależy mi na długofalowej współpracy i stabilnej rentowności, przy pełnej świadomości ryzyka...

I need a researcher who can build a production-ready model that listens to a baby’s cry, watches the paired video, and decides—reliably—whether the cause is hunger, discomfort, or simple attention seeking. Audio and video must be fused inside one architecture; running them in parallel but independently will not satisfy our accuracy goals. You may use the deep-learning stack you trust most (PyTorch, TensorFlow, Keras, OpenCV, torchaudio, etc.) provided the final network can run in real time on an edge device and be exported to ONNX or TFLite. I will share product constraints and a small proprietary data set; you will expand it through public sources or augmentation, perform rigorous cross-validation, and refine the model until we consistently exceed 90 % precision ...

...available. But definately need a NEW DNN training Dataset. simple UI . Total Project is 250 USD STAGE 1 - 100 USD STAGE 2 100 USD Deplyment, Testing and Document 50 USD STAGE 1 handles intelligence + cloud STAGE 2 handles device + communication + app STAGE- I Backend Engineer (Cloud + Data + API) Deep Neural Networks for classification (imbalanced datasets, SMOTE preferred) Python (TensorFlow/PyTorch) and model deployment via REST API Handling weather/time-series datasets Cloud hosting (Azure/AWS) and database management SMS gateway integration and push notification backend Secure API development and logging system Deliverable: Deployed ML model with working API endpoint and backend system. STAGE 2 — Embedded & Mobile Systems Engineer (LoRa + App + Hardware) Lookin...

I have an ongoing structural-optimization study that will be powered by a large langu...module. Deliverables • Python scripts and MATLAB functions that load data, train the LLM, and call the structural optimization routine. • Clear documentation (inline comments and a short README) so I can rerun the experiments on my workstation. • A brief report summarizing training results and the improvement achieved in the optimized designs. All work must execute on standard Python 3.x with PyTorch (or TensorFlow, if preferred) and MATLAB R2021a+. Provide any additional open-source libraries in a requirements list. Once the code reproduces the baseline accuracy and demonstrates a measurable structural-performance gain, the project is considered complete and ready for the n...

...dispatched quickly. I’m flexible about the imagery source—NASA, ESA, Google Earth, or any other free feed is fine as long as it delivers cloud-free, high-resolution scenes. You can use the tool to capture screenshots by moving in circles around the selected location. The detector has to work at desert scale, so please build it with an established computer-vision framework (e.g., TensorFlow, PyTorch, YOLO, or a similarly robust model) and output the findings in both human-readable (an image with bounding boxes or a simple web map) and machine-readable form (CSV/GeoJSON with lat/long, time stamp, confidence score). Once I apply the tool to a new location and receive a list of car and truck pictures and coordinates automatically reflected on the map, no manual clicks&...

...intuitive dashboards (heatmaps, multi-lottery support). 3. Detailed Implementation 3.1. Data Structure: DB schemas for lotteries (rules/ranges/draw days), full history (indexed), user settings (no credentials). 3.2. Draw Day Changes: internal API with official validation & calendar sync. 3.3. Cost Calculation: dynamic Python functions + jackpot scraping & EV. 3.4. Prediction: train RNN/LSTM (PyTorch/TensorFlow), combinatorial generation (itertools), Genetic optimization. 3.5. Backtesting: parallel scripts (multiprocessing), no bet limits, model cross-validation. 3.6. Automation: as in 2.5, with execution logs and real-time UI feedback. 4. Conclusion System developed for exclusive personal use, integrating data collection, multi-model AI optimization, supervised...

...it must immediately push an alert to my back-end (REST webhook is fine) and simultaneously initiate recording on the camera stream. Speed is critical: I’m targeting sub-100 ms inference per frame on an Nvidia Jetson Xavier, yet I still need accuracy good enough to avoid nuisance alerts in busy scenes. You’re free to choose the framework you prefer—YOLOv8, Faster R-CNN, or a custom TensorFlow / PyTorch implementation—as long as the final package runs headless in Linux and can be containerised (Docker) for deployment. Please include: • A fully trained model with reproducible training pipeline • Real-time inference script that ingests RTSP feeds and exposes JSON alerts • Simple unit test clips proving correct detection and trigger behavi...

...plans Signal history & performance tracking Alerts via Telegram, email, webhook, or mobile push Admin System Signal monitoring dashboard Strategy performance analytics User & subscription management Technical Stack (Flexible) We are open to: Frontend: React / / Vue Backend: Node.js, Python, or C# (.NET) Data: WebSocket market feeds + historical OHLCV storage AI/ML: TensorFlow, PyTorch, or equivalent Infra: Scalable cloud architecture, API-first design Budget High budget — We prioritize quality, performance, and long-term scalability over cost. TRADE LOGIC sample • [TREND] Channel trend: Downtrend • [MSB] 3 out of the last 5 MSBs are Bearish pattern breakouts • [MSB] Price near the last MSB level (0.17%) - Resistance • [OB] Pri...

I have a curated dataset of abdominal X-ray images that needs a robust deep-learning model capable of classifying key clinical findings. The end goal is a production-ready Python solution that can consistently score above 90 % accuracy on an unseen validation set. You’ll start with any mainstream framework you prefer—TensorFlow, Keras, or PyTorch—and handle the full pipeline: data preparation and augmentation, model architecture selection, training, hyper-parameter tuning, and evaluation. Please keep the code modular and well-commented so I can retrain or fine-tune later as new data comes in. A concise report that explains your decisions, metrics, and suggestions for future improvements will also be appreciated. To help me choose quickly, focus your proposal on y...

...plain-film X-rays—and tell me whether each study is of the chest, abdomen, or an extremity. All input files will be standard hospital exports (mostly DICOM, occasionally PNG/JPEG), so the model must handle typical variations in resolution and contrast. What I’m after is a reproducible, well-documented solution: data preparation, augmentation, model architecture (a CNN in TensorFlow, Keras, or PyTorch is fine), training, and evaluation. Please include class-balanced splits, explain any preprocessing you apply, and show the metrics you achieve on an unseen validation set. Deliverables • Python code with clear comments for preprocessing, training, and inference • Trained model weights ready for deployment • A short report (or notebook) summarising ...

...category it belongs to (e.g., chest PA vs. chest lateral, cervical spine, hand, etc.). The job is strictly about classifying the type of X-ray, not diagnosing any pathology. Here is what I already have and what I expect from you: • A curated folder structure with several thousand labelled PNG and DICOM files that you can download from my secure server. • A preference for Python with either PyTorch or TensorFlow/Keras—use whichever framework you feel will achieve the best accuracy and fastest inference on a modern GPU. • Clean, reproducible code (Jupyter notebook or script) plus a short README that explains environment setup, training commands, and how to run inference on a single file or a batch. • A trained model file and a simple inference fun...

...production system. Target Platform (Future Integration) Our production system will be: • Android-based smart mirror • Wi-Fi camera input (custom SDK) • C/C++ (NDK) + Java/Kotlin • Video resolution: up to 4K • P2P local streaming (no cloud) So experience with embedded/mobile video pipelines is important. Preferred Skills • Computer Vision / Machine Learning • TensorFlow Lite / MediaPipe / ONNX / PyTorch • OpenCV / FFmpeg / MediaCodec • Android NDK / Native pipelines • Real-time video processing Project Structure Phase 1: Prototype & validation (this project) Phase 2: Integration with hardware SDK (later stage) We are looking to build a long-term technical relationship. Please let us know: • Your relevant experie...

... • Flag high-risk clients to underwriters with an explanation score so they can override or request extra documentation. • Present all of this via a REST/JSON API that my current React front end can call, and expose a lightweight Python-based admin dashboard where underwriting managers can adjust thresholds and retrain models on fresh data. I work mainly with a Python solution (TensorFlow, PyTorch, or scikit-learn—whichever you feel is best) will plug in cleanly. The customer-facing chatbot or form assistant can be built with a service such as Rasa or a fine-tuned OpenAI model as long as it routes seamlessly into the same risk engine. Acceptance criteria 1. A reproducible training pipeline with clear data-schema docs. 2. Dockerised API that runs locally an...

...continually scouting, testing, and refining state-of-the-art models in three core areas: text generation, sentiment analysis, and machine translation. Scope of work — Track current research and emerging repositories (Hugging Face, arXiv, GitHub) to spot promising architectures and training techniques. - klaud8 / hrm ai / chat gpt / claude — Spin up controlled experiments in Python using PyTorch/TensorFlow, comparing baseline performance with fine-tuned variants on representative datasets. — Optimise inference speed, memory footprint, and prompt-engineering workflows so models transition smoothly from notebook to production API. — Document findings in concise experiment reports and integrate successful models into our existing CI/CD pipeline. De...

I'm seeking an experienced AI developer to create a computer vision model focused on detecting people. The model will need to function effectively in both indoor and outdoor environments. Key Requirements: - Primary function: Object detection with a focus on people - Adaptable to both indoor and outdoor settings - High accuracy an...Adaptable to both indoor and outdoor settings - High accuracy and reliability Ideal Skills and Experience: - Expertise in AI and machine learning - Strong background in computer vision, particularly in object detection - Experience with datasets and training models for varied environments - Proficiency in programming languages such as Python, and familiarity with libraries like TensorFlow or PyTorch Please provide examples of similar projects yo...

...engineers and contribute to technical leadership • Conduct research and implement state-of-the-art AI techniques • Ensure data quality, security, and model performance optimization Required Skills & Qualifications: • 10+ years of experience in AI/ML or Software Engineering roles • Strong proficiency in Python and data processing libraries (NumPy, Pandas) • Hands-on experience with TensorFlow, PyTorch, Scikit-learn • Strong understanding of Deep Learning, NLP, Computer Vision • Experience with Model Deployment & MLOps pipelines • Experience working with Cloud platforms (AWS / Azure / GCP) • Strong knowledge of Data Engineering & Big Data tools • Experience with REST APIs and Microservices • Excellent analytical an...

...overlays, confidence scores, and report generation—all with strict patient privacy, no storage of originals, and human oversight required. Key Requirements: • Clean React/ frontend with drag-and-drop upload, DICOM viewer (e.g., ), annotation overlays & heatmaps. • Python backend (FastAPI preferred) + secure auth, encrypted file handling, and cloud storage (AWS S3/GCP). • PyTorch/TensorFlow ML models (fine-tune YOLO/U-Net/MONAI on open dental datasets) for multi-label detection/segmentation. • Mandatory: Full anonymization on upload (pydicom/deid), end-to-end encryption, audit logs, compliance-ready (HIPAA/GDPR/APP principles), ethical transparency (e.g., explainability features). • Cloud deployment (AWS/GCP/Azure, serverless ideal). NDA r...

...tangible, and a closing block on “Advanced Machine Learning Techniques” that shows them what’s possible beyond the basics. Because the colleges have explicitly asked for hands-on labs rather than slide-only lectures, your material needs to revolve around live coding, interactive notebooks, and short build-and-test cycles. Required expertise • Solid command of Python, scikit-learn, TensorFlow or PyTorch, plus NLP libraries such as spaCy or NLTK. • An educator’s mindset: you can explain core concepts clearly, scaffold complexity, and troubleshoot student code in real time. • Proven history of running workshops—either academic or corporate—within tight timelines. Deliverables 1. Detailed session plan (3 tracks, 8–10 ...

I need an expert to improve the accuracy of a histopathologic cancer detection model. The current model needs enhancement, and I prefer using algorithm enhancement for this task. Key Requirements: - Improve the model's accuracy ...this task. Key Requirements: - Improve the model's accuracy in detecting cancerous tissues. - Use advanced techniques and methodologies for algorithm enhancement. Ideal Skills and Experience: - Expertise in machine learning and deep learning - Strong background in medical image analysis - Experience with histopathological images - Proficiency in Python and relevant libraries (TensorFlow, Keras, PyTorch) - Familiarity with model evaluation and performance metrics Please provide examples of similar work and a detailed approach to how you would ...

...conversion/refactor: exact same model, exact same results, but with modern syntax, clear separation of concerns, and thorough inline comments. You’ll start from my original scripts and checkpoints, preserve every bit of accuracy, and hand back a fully functioning module (including a simple demo script) that can be installed with pip-installable requirements. Feel free to streamline library calls—TensorFlow, PyTorch, OpenCV, or whatever is currently in place—so long as the final inference output matches the reference I provide. Deliverables • Refactored Python package replicating the current predictions on a supplied test set • README covering setup, dependencies, and usage • Quick comparison report showing identical mAP or better against t...

...recognition beyond the basic horizontal/vertical stripes to oblique, curved, or irregular banding the current code ignores. • Optimize performance: refactor the pipeline for faster image loading, GPU-aware inference, and leaner memory use so it remains responsive on large datasets. Everything runs in Python, so please stay within that ecosystem. You are free to introduce OpenCV, scikit-image, PyTorch, TensorFlow, or other libraries, provided the final solution installs cleanly with a and runs from a single entry-point script or Jupyter notebook. Input will be folders of images; no video or live feed integration is required at this stage, but laying groundwork for future expansion is a plus. I will supply a labeled image set for benchmarking and expect a short report sho...

...research-level effort, I’m not looking for an off-the-shelf CNN, RNN, or simple transformer stack. I need a genuinely novel architecture (or a rigorously justified adaptation of cutting-edge multimodal papers) that fuses image, text, and numeric signals into a single forecasting pipeline and demonstrably outperforms strong baselines. Key expectations • End-to-end experimentation code (Python, PyTorch or TensorFlow) with clear data loaders for each modality • Custom model implementation with commented rationale for design decisions • Reproducible training scripts, hyper-parameter configs, and a validation notebook that plots forecast accuracy against standard baselines • Final technical report summarizing methodology, results, and potential publi...

...research-level effort, I’m not looking for an off-the-shelf CNN, RNN, or simple transformer stack. I need a genuinely novel architecture (or a rigorously justified adaptation of cutting-edge multimodal papers) that fuses image, text, and numeric signals into a single forecasting pipeline and demonstrably outperforms strong baselines. Key expectations • End-to-end experimentation code (Python, PyTorch or TensorFlow) with clear data loaders for each modality • Custom model implementation with commented rationale for design decisions • Reproducible training scripts, hyper-parameter configs, and a validation notebook that plots forecast accuracy against standard baselines • Final technical report summarizing methodology, results, and potential publi...

Lead AI / Fullstack Engineer — Project "AZIZA" (Voice-to-Voice AI) ​Project Name: AZIZA Format: Project-based / Remote (with access to local GPU clusters) Tech Stack: PersonaPlex (Moshi-based architecture), PyTorch, TensorRT-LLM, FastAPI, WebRTC, Telegram Mini App (TMA). Hardware Location: Uzbekistan & Turkey clusters powered by NVIDIA L40S ​Project Overview ​AZIZA is an innovative multimodal "Speech-to-Speech" (S2S) ecosystem designed to simulate natural human interaction. We are building an AI assistant that seamlessly transitions between roles: an expert tutor (Chemistry, History, Biology), an empathetic companion, and a simultaneous translator. By processing audio tokens directly, the system achieves unprecedented interaction speeds. ​Current Statu...

I need a production-ready object detection model built, trained, and packaged so it runs smoothly on iOS and Android devices, in a modern web browser, and as a lightweight desktop application. The same model should power every platform to keep accuracy and behaviour consistent. You are free to choose the framework you are most comfortable with—TensorFlow, PyTorch, YOLOv8, Detectron2, or another proven library—as long as the final artefacts meet these requirements: • Mobile: optimised builds (e.g. TensorFlow Lite, Core ML, or ONNX) that hit realtime speeds on mid-range phones. • Web: WebAssembly/WebGL or implementation that loads in under three seconds on a standard connection. • Desktop: a small executable or Python app with GPU support when availab...

...Secondary expectations include light cooking, everyday chores, and fluid, human-like movement so the machine blends safely into family life. I am looking for an end-to-end solution covering: • Hardware: anthropomorphic frame, compliant actuators, force-torque sensors, depth & RGB cameras, and medical-grade vitals sensors. • Software: ROS-based control stack, SLAM for navigation, TensorFlow/PyTorch models for vision and speech, and a secure mobile dashboard. * Ability to learn and adapt. * Chatgpt integration. * Human-like skin and features. * Ability to ship to usa. Ability for owner to pack in a box and ship to Philippines easily. • Safety & compliance: redundant fail-safes, IEC/ASTM child-safety standards, and a hygiene-certifiable mate...

The project centres on building a production-ready text-classification pipeline that leverages modern deep-learning techniques. I have a labelled dataset and need end-to-end code that ingests the text, handles cleaning and tokenisation, and trains an accurate classifier. Python is the preferred language; using PyTorch, TensorFlow or another mainstream framework is fine as long as the solution is reproducible and easy to extend. Key deliverables: • Well-commented source code (data loading, model, training loop, evaluation) • Clear instructions to run training on a fresh machine (README or notebook) • Metrics report showing accuracy, precision, recall and F1 on a held-out set • Exported model weights and a small inference script or API endpoint for batch pre...

...stack/Python developer. You will be helping us create and implement the following: • An adoption roadmap that ties specific AI capabilities to each stage of our workflow and project milestones, showing where automation, prediction, or generative content delivers the most value. • A reasoned “why this, not that” selection of tools—think Hugging Face transformers versus OpenAI GPT-4, TensorFlow or PyTorch for model training, spaCy for NLP, Vision APIs for image tasks—plus rapid prototypes that prove the choice. • Drop-in reference implementations or micro-services that slot straight into our existing Node/Express back end. • Plain-language docs and two-minute screen-share videos so community groups, national NGOs, and global partne...

I need a production-ready object detection model built, trained, and packaged so it runs smoothly on iOS and Android devices, in a modern web browser, and as a lightweight desktop application. The same model should power every platform to keep accuracy and behaviour consistent. You are free to choose the framework you are most comfortable with—TensorFlow, PyTorch, YOLOv8, Detectron2, or another proven library—as long as the final artefacts meet these requirements: • Mobile: optimised builds (e.g. TensorFlow Lite, Core ML, or ONNX) that hit realtime speeds on mid-range phones. • Web: WebAssembly/WebGL or implementation that loads in under three seconds on a standard connection. • Desktop: a small executable or Python app with GPU support when availab...

...levels—Admin, Standard user and Guest—each with appropriate permissions for running detections, reviewing results and managing data. Please structure the code so that REST endpoints are cleanly separated; this will let me expose the following Android-ready APIs later on: live-video analysis, image-file analysis and retrieval of disease-history logs. Deliverables • Python inference engine (TensorFlow/PyTorch + OpenCV acceptable) optimised for Raspberry Pi 5 • Django project with the described role system, templates and REST endpoints • Model-training notebook or script plus labeled dataset reference • Setup script or Dockerfile for one-step deployment on a fresh Pi • Brief README covering install, usage and endpoint documentation Ac...

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...intensity and volume to suit beginner through intermediate levels, and outputs a structured weekly routine (exercises, sets, reps, rest, and optional equipment notes). The system should justify its choices in plain language so the end user understands the reasoning behind each exercise block. I’d like the core to be written in Python, preferably leveraging familiar ML libraries such as TensorFlow, PyTorch, or scikit-learn—happy to hear your recommendation. Data sources for exercise selection can come from public, reputable fitness datasets or a curated ruleset you supply. The end result should be easy for me to integrate into a simple web or mobile front end. Deliverables • Clean, well-commented source code for the workout-plan generation engine • A ...

...blinking, lip-sync errors, or artifacts). * Uniform/Badge Recognition: Detect if the person is wearing a police uniform or showing a badge (using object detection like YOLO). * Real-Time Risk Dashboard: * A simple UI that displays a "Trust Score." If the score drops below a threshold, it shows a "SCAM ALERT" warning. Preferred Tech Stack: * Language: Python * ML Frameworks: TensorFlow / PyTorch / Keras * Computer Vision: OpenCV, MediaPipe * NLP: Hugging Face Transformers (BERT/RoBERTa for intent classification) * Interface: Streamlit or Flask (for the demo dashboard) Deliverables: * Source Code (well-commented). * A file for easy installation. * A short demo video showing the system detecting a scam attempt from a sample video file. * Document...

...caching, and Gunicorn as the application server for production; - Inventory Prediction and Purchasing Optimization Built predictive models using Prophet, XGBoost, LSTM, RNN, and PyTorch to forecast inventory levels and identify the optimal reorder point; Predicted future expenditures based on historical consumption, reducing costs from unnecessary purchases and preventing stockouts; Visualized results in Power BI to facilitate decision-making. - Financial Prediction by Department Built a predictive model to estimate revenue and costs per department for the following month; Utilized XGBoost, Prophet, LSTM, RNN, and PyTorch to model different financial behavior patterns; Implemented a data pipeline with PostgreSQL and ELT processes, ensuring efficiency in processing and in...

...raw mobile photos and automatically clean noise, sharpen details, fix color and convert them to 3d printable files. You’ll choose or design the model, train or fine-tune it, then wrap everything in a lightweight API that my mobile team can call in real time (on-device when feasible, cloud fallback when not). You should be completely comfortable with OpenCV plus deep-learning frameworks such as PyTorch or TensorFlow, and you know the trade-offs between traditional filters, GAN-based approaches, and modern super-resolution networks. Experience packaging models for CoreML, TensorFlow Lite or similar mobile runtimes will set you apart. I’m most interested in seeing what you’ve already shipped, so please include past work that proves you can take an image-processin...

...abstract this behind a clean API layer so either provider can be swapped in later. • Send confirmation and reminder messages automatically, with hooks for SMS and email. Tech preferences I am most comfortable reviewing code written in Python, and I already run Docker in production, so please containerise the service. You are welcome to use libraries such as spaCy, Rasa, Dialogflow, TensorFlow or PyTorch—choose what fits the problem, document your reasoning, and make installation reproducible with a single script. What I will accept as “done” 1. A Docker-ised project that I can spin up locally, connect to my calendar, and watch it schedule a demo meeting end-to-end. 2. Clean, commented source code plus a concise README detailing setup, environment ...

...collaboratively train a powerful intrusion-detection model without ever sharing raw traffic logs. Each embassy keeps its packets local, trains an IDS in Python, then ships only the updated weights to a permissioned blockchain that averages them and sends back the enhanced parameters. Key points I need turned into code: • Dual-framework training loops: the local agent must be able to switch between PyTorch and TensorFlow so each site runs whichever stack its engineers prefer. • Attack coverage baked into the dataset pipeline: the IDS must correctly classify Malware, Phishing, and DDoS patterns. Synthetic generation for edge cases is fine as long as the final validation shows clear detection rates for all three. • Smart-contract aggregator: the chain’s sole...