Pytorch jobs

**Project Title:** Source Detection using Human Behaviour Dynamics using Machine Learn...also novelty is must **Scope of Work:** * Dataset preprocessing and exploration * Model development using ML/DL techniques * Training and testing the models * Performance evaluation (Accuracy, Precision, Recall, Confusion Matrix) * Comparison of different models * Clear documentation of the workflow **Preferred Tech Stack:** * Python * Machine Learning / Deep Learning models * TensorFlow / PyTorch / OpenCV * Data visualization libraries **Deliverables:** * Complete source code * Trained model * Results and performance comparison * Brief documentation of the methodology **Additional Notes:** If you have suggestions for improving the model or methodology, feel free to include them in your...

...key. On top of that, the whole site needs a polish pass: tighter UI visuals, faster page loading, and the elimination of a handful of known bugs that occasionally break the player or freeze the waveform display. You’ll receive the full codebase (front-end, back-end, and the existing transition logic) along with sample track data so you can test locally. I’m open to any AI stack—TensorFlow, PyTorch, or whatever framework you feel will get the best results—as long as the final experience feels flawless to the end user. Deliverables 1. Refactored autopilot module that uses AI/ML to analyse BPM, key, and energy to pick the next song, cue it perfectly, and apply beat-synced effects. 2. Updated UI with cleaner layout and responsive styling across deskto...

I am looking for an exp... * Logistic Regression * Random Forest * Support Vector Machine * XGBoost * Deep Learning models (CNN/LSTM if required) 4. Evaluate models using performance metrics such as: * Accuracy * Precision * Recall * F1-score * Confusion Matrix * ROC-AUC 5. Provide clear Python code using libraries such as: * NumPy * Pandas * Scikit-learn * TensorFlow or PyTorch * Matplotlib / Seaborn Deliverables: * Well-documented Python code * Model comparison results * Evaluation metrics and visualizations * Brief explanation of methodology This project is intended for research purposes, so clean code and proper documentation are important. Also I want novelty based topics Please include examples of similar AI/ML work in your...

...technical platform troubleshooting, I specifically need someone who can step in, finish the required tasks, and submit clean, well-commented solutions on my behalf. This will be an ongoing project and will span an year. Typical exercises range from data preprocessing and feature engineering to training and evaluating models with libraries such as Python, NumPy, pandas, scikit-learn, TensorFlow or PyTorch. Code must run flawlessly in the course’s Jupyter-based environment and meet the rubric laid out in each brief (accuracy thresholds, narrative explanations, and any visualisations the instructors request). What I’d like to see in your offer is a short note about your relevant experience—previous AI/ML coursework you’ve completed, projects you’ve ...

...download a suitable, well-labelled dataset from Kaggle. Feel free to compare a few candidates, but the final choice should give good class balance and enough samples per disease category. Once the data is in place, walk through exploratory data analysis, preprocessing, and augmentation inside a Jupyter notebook. From there, build and tune a convolutional neural network (TensorFlow / Keras or PyTorch are both fine) and report the usual metrics plus a confusion matrix so I can judge class-wise performance. When the model is satisfactory, save it and wrap inference in a clean Streamlit app where a user uploads a single image and instantly sees the predicted disease name along with a confidence score. Additionally, integrate AI-based recommendations (e.g., treatment or prevention s...

...execution to let them control key in-app actions hands-free. • Language identification that automatically detects whether the incoming audio or text is Amharic or English before processing. The finished app should run smoothly on Android phones and as a responsive web application; a shared code-base or robust API that feeds both fronts is ideal. I’m open to your preferred stack—TensorFlow, PyTorch, Whisper, or any other modern NLP/ASR tools—as long as model performance stays fast and the architecture can be retrained with additional corpora later. Deliverables 1. Source code with clear build instructions. 2. A runnable Android APK and a hosted web demo. 3. Documentation covering model training steps, data requirements, and how to modify voice comm...

...labeled by timestamp; frame-level annotation may still be required for optimal accuracy. Scope • Design and train a deep neural network—CNN, transformer, or hybrid model—that detects all three defect categories directly from video streams. • Implement preprocessing (frame extraction, augmentation, ROI isolation) and post-processing (tracking, alert generation) in Python using libraries such as PyTorch/TensorFlow and OpenCV. • Optimise for inference on an on-premise GPU; latency under 200 ms per frame is the target. • Provide clear metrics: precision, recall, F1, and confusion matrices on a held-out validation set. • Package the final solution with a lightweight REST or gRPC endpoint so the in-house engineering team can call it from ...

...pipeline that must reliably spot nuts and bolts in a live camera feed and publish their positions to the rest of my stack in real time. Your job is to create the complete vision-detection module—from model training or fine-tuning through to a clean ROS 2 node that subscribes to an image topic and spits out the detected objects with bounding boxes (or masks) and a confidence score. OpenCV, TensorFlow/PyTorch and any of the common ROS 2 image-transport plugins are all fine as long as the final node runs on Humble and stays GPU-agnostic (CUDA acceleration is a bonus, not a requirement). I already have a test rig with a standard USB camera; if you need specific calibration images I can capture them for you. Please deliver: • Source code for the detection model and ROS 2 ...

...environment. Explanations will be detailed and actionable, so the platform can be maintained independently without guesswork. Functionality is non-negotiable, with every component—from AI modules to smart contract execution—fully operational and integrated. This project is ideal for a technical collaborator who is experienced in AI/ML integration using Python frameworks such as TensorFlow or PyTorch, proficient in blockchain development and smart contracts, and skilled in full-stack web development with JavaScript and Python. Familiarity with responsive design, conditional logic, and building scalable, secure applications is essential. The goal is not just to build a functioning product, but to create a robust, maintainable, and brand-aligned AI + blockchain web plat...

More details: Which deep learning framework do you prefer for this project? TensorFlow Do you have a preferred dataset for brain MRI image segmentation? Please use a publicly available dataset Which style of output visualization do you prefer? 2D slices with segmentation overlay

...graphics, illustrations Adobe Photoshop / Illustrator SEO articles, landing pages, marketing copy Digital Marketing & Social Media SEO, SEM, conversion optimization Paid ads, audience targeting, analytics Video Production & Editing Promotional and social videos Premiere Pro / After Effects AI & Machine Learning Integration Chatbots, NLP tasks, model fine-tuning Python / TensorFlow / PyTorch Cloud & DevOps Infrastructure AWS / Azure / GCP CI/CD pipelines, Docker, Kubernetes Cybersecurity & Security Audits What You Should Include in Your Bid Relevant experience with one or more of the above skills Portfolio or links to previous work Estimated timeline & deliverables Hourly rate or fixed price quote...

...Adobe Photoshop / Illustrator SEO articles, landing pages, marketing copy Technical documentation Digital Marketing & Social Media SEO, SEM, conversion optimization Paid ads, audience targeting, analytics Video Production & Editing Promotional and social videos Premiere Pro / After Effects AI & Machine Learning Integration Chatbots, NLP tasks, model fine-tuning Python / TensorFlow / PyTorch Cloud & DevOps Infrastructure AWS / Azure / GCP CI/CD pipelines, Docker, Kubernetes Cybersecurity & Security Audits What You Should Include in Your Bid Relevant experience with one or more of the above skills Portfolio or links to previous work Estimated timeline & deliverables Hourly rate or fixed price quote ...

...machine-learning models. The goal is to give my team a sandbox where we can move seamlessly from data preprocessing and feature extraction through model training, evaluation, deployment, and monitoring—without touching the physical board until we are ready. Here’s what I need: • A reproducible simulation that mirrors Jet Nano’s CUDA-enabled GPU, memory constraints, and I/O. • Containerised tool-chain (PyTorch, TensorRT, cuDNN, etc.) with scripts that cover the full life-cycle: preprocessing, training, hyper-parameter sweeps, evaluation metrics, and a mock-deployment stage that tracks resource usage and latency. • Clear documentation so any teammate can spin up the environment, run the sample pipelines, and swap in new datasets or model archi...

...quickly score each candidate’s depth of knowledge across Python, Scala and SQL. Our stack centres on Azure and Databricks, so practical insight into large-scale Spark/PySpark jobs, data-model design, ETL orchestration and cloud performance tuning is essential. Candidates frequently discuss streaming, optimisation strategies and modern AI/ML add-ons, so any hands-on exposure to libraries such as PyTorch, NumPy, SciPy or TensorFlow will help you challenge them at the right level, though it is not mandatory. Availability is limited to two focused hours per weekday; I will share the interview schedule at least 24 hours in advance. After each session you will file a concise written assessment noting technical strengths, gaps and a simple hire/no-hire recommendation. Consis...

...pipeline focused on image generation, and I want the results to be crisp, large-format visuals rather than the low-res outputs many models settle for. Your task is two-fold: first, curate or assemble a fit-for-purpose dataset (I don’t yet have one), and second, train a state-of-the-art z-image diffusion model that consistently produces high-resolution renders. You’ll have freedom in tool choice—PyTorch, TensorFlow, DreamBooth, LoRA or any other modern techniques are fine—as long as the final model can be reproduced from the training scripts you deliver. I expect the usual artefacts: cleaned dataset (with clear licensing notes), training code, model checkpoints, and a concise README outlining hyper-parameters, compute used, and instructions to run inferenc...

I need a purpose-built CRM for the strata industry that leans heavily on AI to wipe out repetitive admin and give committees, owners and managers instant insight. The solution must weave in predictive analytics to flag maintenance and arrears before...and AI-driven alerts Deliverables: • End-to-end technical architecture and stack proposal • Iterative builds in a visible repo • Production deployment with monitoring • Training materials and a clean hand-over pack • Acceptance demo on a sample building plus a load test simulating 10,000 concurrent users If you’ve previously merged CRM frameworks with machine-learning tools such as TensorFlow, PyTorch or Azure AI, and understand Australian privacy requirements, walk me through your approach and timeline...

...production-ready features. Mentor junior engineers and contribute to technical strategy and architecture decisions. Champion best practices in MLOps, model governance, and ethical AI. What You Bring Required: Candidates must be reside in Philippines and other South East Asia. 3+ years of hands-on experience building and deploying ML models in production. Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn, Hugging Face). Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code (Terraform, CloudFormation). Solid understanding of data structures, algorithms, and software engineering best practices. Familiarity with containerization (Docker, Kubernetes) and CI/CD for ML workflows. Excellent communication skills and ability to expl...

...need a deep-learning solution that watches a driver’s face through a standard camera feed, tracks eye-closure patterns and yawning frequency, then translates those cues into a clear fatigue score that updates continuously. Over a journey the model should also plot a time-based curve so I can see how alertness rises or falls. Please build and train the full pipeline in Python, preferably with PyTorch or TensorFlow paired with OpenCV for video handling. The system must be completely vision-based; no wearables or contact sensors. I will supply sample clips for initial testing, but the code should accept any 30 fps video stream so I can later attach it to an in-car webcam. The final hand-off should include: • Inference script that ingests a live or recorded feed, detec...

I need a lightweight AI application that runs smoothly on a standard Windows laptop and lets me create animations and cartoon-style...app (trim video, adjust colours, crop/resize images). What I expect from you • Complete source code, clearly commented. • An installer or one-click setup for Windows that handles every dependency. • A brief “getting started” guide so anyone on my team can install and use it without prior AI knowledge. Feel free to leverage open-source frameworks such as Stable Diffusion, AnimateDiff, PyTorch, TensorFlow, or similar, provided the final package remains budget friendly and easy to maintain. I’m open to your suggestions on the tech stack so long as the end result meets the goals above and keeps resource use mod...

...прогнозы денежных потоков, выявлять сезонность и потенциальные кассовые разрывы, визуализировать их интерактивными графиками. • На основании поведения, целей и риск-профиля выдавать полностью индивидуализированные планы: от ежемесячных лимитов до долгосрочных целей (ипотека, образование и т. д.) с объяснением логики модели. Технологии и требования Python/TypeScript, современные ML-фреймворки (PyTorch, TensorFlow или аналог), LLM/ChatGPT-style слой для диалогового интерфейса, а также шифрование данных at-rest и in-transit. Допускаю cloud-развёртывание (AWS, GCP, Azure) либо on-prem при условии соблюдения GDPR/ISO 27001. Конкретный результат 1. Рабочий прототип (web или mobile) с подключением к тестовым данным. 2. Репозиторий с чистым, документированным кодом и u...

...need a deep-learning solution that watches a driver’s face through a standard camera feed, tracks eye-closure patterns and yawning frequency, then translates those cues into a clear fatigue score that updates continuously. Over a journey the model should also plot a time-based curve so I can see how alertness rises or falls. Please build and train the full pipeline in Python, preferably with PyTorch or TensorFlow paired with OpenCV for video handling. The system must be completely vision-based; no wearables or contact sensors. I will supply sample clips for initial testing, but the code should accept any 30 fps video stream so I can later attach it to an in-car webcam. The final hand-off should include: • Inference script that ingests a live or recorded feed, detec...

...data, timeline, and performance targets. Once we lock the scope, I’ll provide sample images plus any domain knowledge you need. You’ll handle the end-to-end pipeline: dataset preparation, model architecture selection (CNN, transformer, or whatever you judge best), training/tuning, and deployment in a lightweight API or on-device model. I’m comfortable with common stacks like Python, TensorFlow, PyTorch, and OpenCV, so feel free to propose the combination that will deliver both speed and accuracy. Deliverables • A working, tested model meeting agreed accuracy/F1 benchmarks • Inference script or REST/GraphQL API for easy integration • Brief setup guide and commented code I’m ready to move quickly and can review prototypes as soon as y...

...data, timeline, and performance targets. Once we lock the scope, I’ll provide sample images plus any domain knowledge you need. You’ll handle the end-to-end pipeline: dataset preparation, model architecture selection (CNN, transformer, or whatever you judge best), training/tuning, and deployment in a lightweight API or on-device model. I’m comfortable with common stacks like Python, TensorFlow, PyTorch, and OpenCV, so feel free to propose the combination that will deliver both speed and accuracy. Deliverables • A working, tested model meeting agreed accuracy/F1 benchmarks • Inference script or REST/GraphQL API for easy integration • Brief setup guide and commented code I’m ready to move quickly and can review prototypes as soon as y...

My production AI app suddenly responds with a plain “HTTP ERROR 500” whenever the inference endpoint is called. Deliverables • Root-cause analysis with a brief summary of the offending module or call • Patched code committed to a feature branch and merged after revie...summary of the offending module or call • Patched code committed to a feature branch and merged after review • Confirmation that all existing unit or integration tests pass, or new tests added where coverage is missing • A short hand-off note so I understand what was fixed and why If you have strong debugging skills and are comfortable profiling AI pipelines—whether they rely on TensorFlow, PyTorch, Scikit-learn, or something entirely different—you’...

...passages, and then respond as naturally as a real teacher would. The core objective is therefore enhanced understanding of context; raw recognition accuracy and voice quality are already acceptable. You’ll be working with curriculum-heavy material, so domain-adapted language models, embeddings or custom transformers that can digest educational phrasing are welcome. Whisper, DeepSpeech, Kaldi, PyTorch- or TensorFlow-based pipelines are all fine as long as latency stays low for mid-range devices. To move forward I need a detailed project proposal rather than a generic CV. In it, outline: • Your plan for collecting / formatting training data drawn from textbook chapters and past exam papers • The fine-tuning methodology and evaluation metrics you’ll use to...

...data, timeline, and performance targets. Once we lock the scope, I’ll provide sample images plus any domain knowledge you need. You’ll handle the end-to-end pipeline: dataset preparation, model architecture selection (CNN, transformer, or whatever you judge best), training/tuning, and deployment in a lightweight API or on-device model. I’m comfortable with common stacks like Python, TensorFlow, PyTorch, and OpenCV, so feel free to propose the combination that will deliver both speed and accuracy. Deliverables • A working, tested model meeting agreed accuracy/F1 benchmarks • Inference script or REST/GraphQL API for easy integration • Brief setup guide and commented code I’m ready to move quickly and can review prototypes as soon as y...

...dataset from raw form through model design, training, validation, and deployment, then document the entire pipeline so it can be maintained by my in-house team. You should be comfortable choosing the most appropriate algorithms, handling feature engineering, tuning hyper-parameters, and explaining trade-offs in accuracy, speed, and resource use. Typical tools in my stack include Python, TensorFlow, PyTorch, scikit-learn, and Docker, so experience with these will be valuable. Deliverables • Clean, well-commented code in a Git repo • Reproducible training scripts and environment files • A trained model (or models) saved in a portable format • Evaluation report with key metrics and error analysis • Deployment-ready API or packaged module •...

...wrapped in a full MLOps backbone that integrates MLflow for experiment tracking of retrieval metrics, PPO reinforcement learning rewards, and parameter tuning, exposes Prometheus metrics for latency and retrieval monitoring compatible with Grafana dashboards, and supports Airflow DAG orchestration for scheduled indexing and policy training workflows. Reinforcement learning is implemented using a PyTorch based PPO policy network that treats retrieval selection as an action space, assigns rewards based on relevance heuristics, updates via policy gradients, and logs training metrics for continuous optimization, positioning the system not merely as a static RAG but as an adaptive retrieval intelligence engine. All components are configuration driven, CLI operable, fail safe with retr...

...data, timeline, and performance targets. Once we lock the scope, I’ll provide sample images plus any domain knowledge you need. You’ll handle the end-to-end pipeline: dataset preparation, model architecture selection (CNN, transformer, or whatever you judge best), training/tuning, and deployment in a lightweight API or on-device model. I’m comfortable with common stacks like Python, TensorFlow, PyTorch, and OpenCV, so feel free to propose the combination that will deliver both speed and accuracy. Deliverables • A working, tested model meeting agreed accuracy/F1 benchmarks • Inference script or REST/GraphQL API for easy integration • Brief setup guide and commented code I’m ready to move quickly and can review prototypes as soon as y...

...recommendation, or a mix as needed) and handle model deployment orchestration. What I have • Clear product requirements and user flows. • A clean slate on tech choices—I’m open to MERN, MEAN, LAMP, or another stack you can justify. What I’m looking for • Senior-level full-stack experience shipping production applications. • Hands-on knowledge of at least one mainstream AI framework (TensorFlow, PyTorch, or a hosted alternative) and cloud deployment (AWS, GCP, or Azure). • Ability to iterate quickly, communicate clearly, and document your work so hand-off is smooth. Initial deliverables 1. Technical architecture plan with stack and AI component recommendations. 2. Proof-of-concept demonstrating model integration through a minimal UI. 3...

...neural fusion layer; I’m happy to discuss which choice achieves the best balance of speed and robustness. The finished model must be benchmarked on four fronts that matter equally to me: Accuracy, Precision, Recall, and False-Alarm Rate. Because the project will eventually guard a sensitive site, I’m aiming for a solution that can move from laptop to field hardware without major rework. Python, PyTorch, and standard CV/RF libraries are expected; please keep your code clean, well-commented, and reproducible. Deliverables: • Trained YOLOv8 weights and training notebook • Trained RF-CNN model and training notebook • Fusion module with documented logic or architecture • End-to-end inference script that ingests live or recorded video plus I/Q da...

...implement data solutions and work with databases. 3. Data Preparation & Visualization Pandas NumPy Matplotlib Seaborn Why: Before modeling, you need to clean, manipulate, and visualize data effectively. 4. Business Intelligence Tools Tableau Power BI Why: Understanding dashboards and reporting helps communicate insights to stakeholders. 5. Machine Learning Scikit-learn TensorFlow PyTorch Why: Learn supervised, unsupervised, and reinforcement learning for predictive modeling. 6. Deep Learning Neural Networks Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) Why: For advanced pattern recognition, image, text, and sequence modeling. 7. Specialization Natural Language Processing (NLP) Computer Vision Social Network Analysis Why: Focus on...

...well-structured draft without tinkering with settings or prompts. Key expectations • End-to-end workflow: upload → automated review (clause extraction, issue spotting, citation check) → editable draft formatted for court. • Modern, intuitive interface—web-based or desktop—where the heavy lifting happens behind the scenes through NLP, machine learning or large language models (Python, TensorFlow, PyTorch, or similar frameworks are fine). • Secure handling of sensitive material; on-prem or private-cloud deployment is acceptable. • Modular architecture so accuracy improvements or additional document types can be added later. Acceptance criteria 1. A working prototype that processes at least three sample files per document cat...

I’m putting together an academic-grade project that demonstrates how supervised learning can spot common web attacks—s...concise PDF explaining logic, workflow, and how to rerun everything (screenshots welcome) 4. Optional extras: a short report (~10 pages) and a slide deck to support a final-year viva—include these if you’re comfortable producing them, otherwise let me know so we can adjust milestones accordingly. Keep the solution native to widely used libraries (pandas, scikit-learn, TensorFlow/Keras or PyTorch) so reviewers can reproduce results without exotic dependencies. Accuracy matters, but clarity and reproducibility are paramount—I want to be able to hand this over, have someone install requirements, press “Run,” and immediat...

...difference. You will receive several high-quality recordings of my voice in different moods and volumes to train the model. The finished solution must run locally (Windows is ideal, macOS acceptable), route through a virtual audio device or similar method, and add no more than about one second of latency before the sound reaches WhatsApp. If you plan to leverage libraries such as RVC, So-Vits-SVC, PyTorch, TensorFlow, torchaudio or any comparable real-time voice conversion stack, please mention that; privacy of the raw data and final model is essential. Deliverables • Trained model of my voice, plus all scripts or notebooks used in training • Real-time conversion module/driver ready for WhatsApp input • Clear, step-by-step installation and user guide &bul...

...the final hiring decision quickly. Technical Stack You Must Understand: PHP / WordPress: You need to know what WordPress hooks/filters are, the difference between a theme and a plugin, and basic PHP syntax to tell if a candidate is lying. Machine Learning: You need a conceptual understanding of ML (what is a neural network, supervised vs unsupervised learning, Python libraries like TensorFlow/PyTorch) to gauge the candidate's experience level. Requirements: Native U.S. Speaker: You must sound professional and natural on the phone. This is non-negotiable. Technical Knowledge: You must have a background in development or computer science to understand the jargon. High Emotional Intelligence (EQ): You need to be able to read people during interviews. Professional Setup:...

...decks, code notebooks, and any visual aids required for smooth, engaging live delivery. • At least two industry-specific application projects per course—think finance anomaly detection for LSTMs, medical-image segmentation, or manufacturing quality-control models—complete with datasets, step-by-step guidance, and evaluation metrics. • Clear instructions on the environment and tools you use (PyTorch, TensorFlow, OpenCV, Jupyter, or similar) so participants can replicate results during the sessions. By the end of our collaboration I should have three ready-to-run, instructor-led courses that advanced learners can dive into immediately, each capped with practical projects they can showcase at work. If you’ve taught or built curricula at this level ...

...classification (Cancer / No Cancer) OR Multi-class classification (Cancer types) OR Tumor segmentation (locating tumor regions) Treatment Prediction Goal Predict response to therapy (Responder / Non-responder) Predict survival category Predict recurrence risk For MVP: Start with diagnosis, then add treatment prediction. STEP 2: Setup Development Environment Install Dependencies Python 3.9+ PyTorch MONAI pydicom numpy scikit-learn FastAPI or Flask Example: pip install monai torch torchvision pydicom fastapi uvicorn scikit-learn Setup GPU Local CUDA GPU OR Cloud (AWS/GCP/Azure) STEP 3: PET Scan Dataset Preparation Collect Dataset Public PET database (e.g., TCIA) Research partnership dataset Must include: PET images Diagnosis labels (Optional) treatment outcome labels ...

...video data are fully pre-processed, you can head straight into fine-tuning. Accuracy and a low CER are the only performance metrics that matter for this round; inference speed can be dealt with later. I will provide the dataset via cloud storage along with any scripts I have used so far. You’re free to keep, improve, or completely replace those scripts as long as everything remains reproducible in PyTorch (Lightning welcome) and CUDA-ready. Deliverables • Training code or notebooks that run end-to-end on a clean environment • Checkpoints for each model after training • A concise evaluation report comparing CER and overall accuracy on the test split, plus a brief note on the hyper-parameters and design choices you made • (Optional) short recommenda...

I am preparing my final-year submission on “Financial-Market Forecasting with Deep Reinforcement Learning” and need the complete project package built around precious-metal markets—specifically gold and silver. The study must demonstrate short-term price prediction and volatility prediction, trained solely on historical market data. The technical build is up to you: Python with PyTorch, TensorFlow, Stable-Baselines or a comparable stack is fine, provided the code is clean, reproducible and includes clear benchmarking against a simple baseline. Choose and justify any suitable RL algorithm (e.g., DQN, PPO, A2C), handle data ingestion and preprocessing, run back-tests on a hold-out period, and present performance metrics and visualisations (equity curve, volatility ...

...parameter in real time, trigger autonomous adjustments, predict equipment failures before they happen, and continually fine-tune operating set-points so we hit tighter yields with less energy and waste. Data comes from existing PLCs, SCADA historians, PAT probes, and IoT sensors already on the plant network; the solution should layer on top without forcing us to rip and replace. Python, TensorFlow/PyTorch, OPC UA, MQTT, and a modern time-series database are the likely tool-chain, but I’m open to alternatives if they mesh with GMP requirements. Deliverables • Architecture and data-flow design • AI agents for monitoring & control, predictive maintenance, and process optimisation, each deployable in Docker or Kubernetes • Integration scripts/connector...

...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, processes eve...

...that can handle large-scale language-model training and experimentation. • Stand up a complementary cloud stack—-preferably on AWS or Azure—-for elastic training jobs, model versioning, and low-latency inference behind an API. • Build the data pipeline: secure transfer from my on-prem sources, cleaning, tokenisation, labelling, and continuous updates. • Implement model fine-tuning (e.g., PyTorch or TensorFlow, Hugging Face transformers) with clear scripts so I can retrain as business content evolves. • Deliver a chat/FAQ service that plugs into my website and internal tools, complete with fallback rules, confidence scoring, and logging so I can review answers. • Document every step— hardware diagrams, infra as code, setup scrip...

...solution—Stable Diffusion, Midjourney, or similar—works as long as results are coherent and label-ready. 4. Front-end demo: a lightweight web page or notebook showcasing the two input modes and the polished, illustrated output. 5. Documentation: succinct setup guide plus comments in code so I can iterate later. Tech flexibility I’m comfortable with Python, so staying within that ecosystem (PyTorch, TensorFlow, OpenAI APIs, etc.) keeps hand-off smooth. If another stack dramatically improves performance, let’s discuss. Deliverables • Clean, well-structured code repository • Deployed or easily runnable demo • Sample output package for at least three furniture types (e.g., chair, shelf, custom concept) proving the system can genera...

...automatically generate end-to-end test cases for a WhatsApp-like application whose source code is unavailable. The app supports text messaging, voice calls, and rich media sharing. Although I only have high-level user journeys at the moment, I can describe them in detail when we begin. The solution must run entirely on a local machine—no cloud services—so please lean toward frameworks such as TensorFlow, PyTorch, or any comparable on-premise library you can justify. Your approach should ingest the observable UI flows and user interactions, apply AI/ML techniques to infer test intent, and output executable test scripts that can be replayed through Appium, Selenium, or another open-source driver you recommend. Deliverables: • A repeatable pipeline (code + model) ...

...long-term value. • Sentiment analysis module that mines social channels, news headlines and forums to gauge crowd mood. • Unified scoring layer so users can quickly compare all three viewpoints before making a decision. • Support for BTC, ETH and XRP from day one, with a framework that lets me plug in more assets later. • A lightweight web UI plus REST/GraphQL endpoints; Python (TensorFlow / PyTorch), Node or similar mainstream stacks are fine—use what you’re best at. • Clear documentation and a short demo clip showing the system in action. I’ll consider the work complete when live price feeds flow through each model, the dashboard updates without manual refresh, alerts can be triggered via API, and your read-me lets my own de...

...ingest still photographs taken at live events and automatically tag each shot into predefined categories (for instance crowd, stage, speaker, logo, VIP, etc.). The core requirement is accurate, fast classification of photos only; we are not dealing with video or live camera feeds right now, though I may extend in that direction later. You are free to choose the underlying framework—TensorFlow, PyTorch, scikit-learn—so long as OpenCV is used for image handling and preprocessing. Here is what I expect: • A well-documented training script that reads my labeled dataset, performs augmentation where helpful, and outputs a reproducible model. • An inference module (CLI or small API) that accepts a folder of JPEG/PNG files and returns class labels with con...

...labeled according to the specific disease or healthy state shown. I need a robust image recognition model trained to spot those diseases quickly and accurately so the system can be deployed in a real-time monitoring pipeline for aquaculture. The job revolves around: • Cleaning and augmenting the dataset . • Selecting or designing the most suitable CNN/transformer architecture in TensorFlow or PyTorch. • Training and validating the model, then fine-tuning until it meets or exceeds 92 % F1 on my held-out test set. • Packaging the finished model plus reproducible training scripts and clear inference instructions (Docker image or notebook is fine). .If possible integrating back end Acceptance criteria 1. Trained model file(s) ready for GPU or CPU ...

...identification, exercise classification, and rep counting logic • Sample outputs on provided video files • Short README explaining how to run the pipeline and describing the architecture Required experience: • Strong Python skills • Computer vision fundamentals • Experience with video processing and OpenCV • Experience with pose estimation • Experience with deep learning frameworks such as PyTorch or TensorFlow • Ability to design clean, modular systems Nice to have: • Facial recognition or embedding-based identity systems • Human activity or action recognition experience • Prior work in fitness, sports, or biomechanics • GPU inference or model optimization experience Engagement details: • Start on M...

...classification and I need a bespoke natural-language-processing algorithm developed from scratch. The goal is to input raw text and have the model return reliable category labels with clear confidence scores. Here’s what I’m expecting from you: • End-to-end code (Python preferred) that trains, validates, and serves the classifier • A well-commented model architecture using mainstream libraries such as PyTorch, TensorFlow, or scikit-learn—whichever best fits the task • Reproducible training pipeline: data pre-processing, tokenisation, hyper-parameter tuning, and evaluation metrics (precision, recall, F1) • A lightweight inference script or API endpoint so the model can slot straight into my existing backend • Concise documentatio...