Pytorch jobs

I need a set of well-researched, technically accurate articles that dive into Artificial Intelligence within the broader Software & IT space. Each piece should explain complex concepts—think neural network architectures, model training workflows, real-world deployment challenges, or recent b...Any diagrams or charts you create, provided as separate image files • A concise reference list for each article Before final approval, I’ll review for technical depth, clarity, and originality. Plagiarism or surface-level summaries will be rejected—each article needs to add genuine value to the AI community. If you have hands-on experience with machine learning frameworks such as TensorFlow or PyTorch and can demonstrate prior technical writing samples, we’ll...

...squeeze out the best accuracy—but the end result must reliably spot both the vehicle and the plate in a single pass. What I will provide • Training, validation and test splits already annotated in COCO-style JSON • A small script that shows the folder layout I use for images and labels What I expect back • The fine-tuned weights • All training and evaluation scripts (Python preferred; PyTorch or TensorFlow are both fine) • A quick-start readme so I can reproduce your results on my machine (GPU) • Basic metrics report—precision, recall and mAP on the held-out test set Acceptance criteria 1. Model reaches balanced precision/recall suitable for real-time inference (we can finalise the exact threshold together). 2. Infe...

I need to fine-tune an existing computer-vision model for object detection so it performs reliably on my custom dataset. The base architecture is already selected; the task now is to prepare the data, set up transfer-learning, run hyper-parameter sweeps, and deliver a set of weights that reach an agreed-upon mAP on a held-out validation split. You may use PyTorch, TensorFlow, or another mainstream framework as long as the final model can be exported to ONNX. Please share examples of past work where you have successfully fine-tuned or trained object-detection networks—links to repos, papers, or short demos are ideal. Once we decide on the starting checkpoint and training environment, I’ll supply labeled images, class definitions, and any additional annotations you need. ...

I need a fully-functional AI movie-making app that feels as slick and intuitive as EPNOVA yet stands on its own merits. The finished product must cover three fronts—iOS, Android and a...Acceptance criteria • A user can import raw clips and receive a polished video in under five minutes on mid-range hardware • Feature parity with EPNOVA’s flagship functions, plus at least one incremental improvement (e.g., smart B-roll suggestions or AI-generated captions) • Consistent performance and UI across all three platforms If you have a proven track record with tools like TensorFlow, PyTorch, Flutter, React Native or similar, and can outline a clear roadmap with milestones, I’d like to see your approach and timeline. Let’s build an editor that bea...

...fine if they give better performance). Before and after optimisation, benchmark accuracy, size on disk, inference latency and peak RAM so we can see exactly what we gained. Finally, package a lightweight demo: a Python script or micro-API that loads the exported model and runs live inference on the target device. Please send a detailed project proposal describing the approach, toolchain (e.g. PyTorch + Hugging Face Transformers, ONNX Runtime, TensorFlow Lite), and the timeline you foresee for data creation, training, compression and final validation....

...the frontier of Artificial Intelligence through truly exploratory research. The programme is research-and-development first, so there is no pressure to ship a commercial product; instead, you will be encouraged to formulate your own questions, test bold hypotheses, and document everything openly. You should already be comfortable reading recent AI literature, writing reproducible code (Python, PyTorch, JAX—use what feels right), and explaining complex ideas clearly. During the fellowship we will meet weekly to refine research directions, share findings, and make sure the work stays original and rigorous. Acceptance criteria & key deliverables • A one-page research proposal with clear questions, prior art, and a rough methodology • Bi-weekly progress note...

...talking-head segments where appropriate. • Subtitled versions (SRT) and concise chapter markers to aid navigation. • Source project files so I can update content later. Acceptance criteria 1. Each module includes: a 10-15 min concept overview, a 20-30 min coding demo in Python, and an assignment or quiz. 2. Code runs end-to-end on a fresh environment using widely-available libraries such as PyTorch, TensorFlow, Hugging Face, or OpenAI Gym. 3. Visuals meet basic WCAG readability guidelines. 4. All media passes a quick plagiarism scan and copyright check. When you reply, please link to at least one previous lecture or tutorial video you produced at an advanced technical level and outline your typical workflow—from scripting and storyboarding through po...

I need a working reinforcement learning model trained and validated on a real-world dataset that I will supply. Your task covers everything from choosing a suitable algorithm, preprocessing the data, setting up the training loop, to delivering a reproducible codebase with clear instructions so I can retrain or fine-tune later. You are free to work in Python with frameworks such as PyTorch or TensorFlow—whichever you feel is the best fit—provided the final solution runs on a standard GPU workstation. I will provide the dataset and explain the reward structure; from there I expect you to handle environment creation (if needed), hyper-parameter tuning, logging, and evaluation metrics that demonstrate learning progress and final performance. Deliverables will include: &bul...

Project Title Machine Learning Engin...Core Language: Advanced, production-level Python. • ML Frameworks: PyTorch, Hugging Face Transformers, PEFT, TRL, or Accelerate. • Inference Optimization: Experience with vLLM, TensorRT-LLM, or DeepSpeed. • Infrastructure: Familiarity with training models on GPU cloud platforms (AWS, RunPod, , or Lambda Labs). 3. What You Need to Provide in Your Proposal (Mandatory) To be considered for this role, you must explicitly state: 1. Duration: How many weeks will it take you to deliver a trained, deployable model and a working inference pipeline? 2. Total Cost: What is your exact fixed price for this custom ML development? 3. Experience: Please detail your past experience with training or fine-tuning open-source LLMs using PyTorch...

...once payment is released. High-quality video lessons are the top priority, so please be comfortable scripting, recording, and editing professional screen-casts with voice-over. Deliverables required in 7 days: • Structured course curriculum that maps objectives, outcomes, and estimated study time • Studio-grade video lessons covering theory and hands-on demos (Python, Jupyter, TensorFlow/PyTorch, real datasets, etc.) • Slide decks (PPT) that mirror each video for classroom or self-study use • PDF study notes derived from the slides and demonstrations • Auto-graded quizzes, practical assignments, and a capstone project with solution files • A final assessment and marking rubric • LMS-ready package arranged into modules, lessons, ...

...targets, view pipeline health, and drill into individual deal histories with a single click. • Offer clean APIs or webhooks so the engine can plug seamlessly into our existing CRM, marketing-automation stack, and payment gateway. Tech expectations Python, Node.js, or another modern backend is fine as long as the architecture is secure, modular, and easy to maintain. If you prefer TensorFlow, PyTorch, or similar frameworks for the predictive layer, mention that in your proposal. A microservices approach on AWS, Azure, or GCP is welcomed; containerisation with Docker or Kubernetes is a plus. Deliverables 1. Fully functional web application (front-end, back-end, and AI layer) deployed to our cloud account 2. Source code in a private Git repo with clear commit histor...

DRISHTI AI is a multi-signal identity screening solution designed to automate fake identity detection and document verification. Built using Python, FastAPI, React, OpenCV, PyTorch, and Tesseract, the platform extracts key text fields via targeted OCR, performs forensic checks (EXIF, C2PA, ELA, and AI content detection), and compares live video feeds against document photographs. The system outputs a unified risk score ranging from 0 to 100, placing documents into Verified (Low Risk), Manual Review (Medium Risk), or Rejected (High Risk) categories. DRISHTI AI streamlines customer and student onboarding, prevents impersonation fraud, and maintains audit-ready logging across high-trust industries.

I'm seeking an experienced developer to train PyTorch models for an intelligent traffic violation detection system. " I have the project and trained models but it's not very accurate and I want to fine-tune the models no need of any other work please train the pytorch models so that I can get better accuracy" The system should accurately detect a variety of violations including: - Helmet detection - Wrong lane detection - Accident detection - Triple riding - Seatbelt detection - Mobile usage - Number plate recognition Ideal skills and experience: - Expertise in PyTorch and model training - Strong background in computer vision - Experience with real-time detection systems - Familiarity with traffic regulations and violations Please provide relevan...

...automated video generation; no manual timeline editing should be necessary. The AI should pull together text, images, stock clips, transitions, captions, and royalty-free music to produce engaging social media content that looks human-edited. Scope of work • Build or fine-tune the underlying AI models (computer vision, NLP, text-to-speech where helpful) in Python or Node, leveraging libraries such as PyTorch / TensorFlow, OpenAI, ffmpeg, MoviePy, or similar. • Design a lightweight web interface (React, Vue, or plain HTML/JS is fine) where I can paste a script or upload assets and immediately preview the generated video. • Implement aspect-ratio presets (9:16, 1:1, 16:9) and automatic resizing so clips are social-platform compliant. • Include basic brand...

I’m looking for someone who can split their time between two intertwined needs. First, I need a supervised-learning model built from the ground up that can handle time-series prediction. I already have historical data; what I’m missing is the full model-development workflow—feature engineering, training, validation, and iteration—implemented in clean, well-documented Python (TensorFlow, PyTorch, or similar). Accuracy, reproducibility, and the ability to improve the model later are essential. Second, my web platform (Node.js + React on a Postgres backend) needs ongoing full-stack enhancements so the forecasting engine can be surfaced through a secure API and intuitive dashboard. You’ll take model outputs, expose them through endpoints, and build the ...

...understand the fundamentals well enough to make confident, informed decisions when I start tackling real-world projects later on. Here’s what I need: • A clear, structured learning path that walks me through the full training workflow—data splits, cross-validation, hyper-parameter tuning, and model selection—using Python and libraries such as scikit-learn (with room to branch into TensorFlow or PyTorch once the basics are solid). • Practical explanations of evaluation metrics—accuracy, precision, recall, F1, ROC AUC—and when each one matters. • Short coding exercises or notebooks after each session, plus feedback on my solutions so I can correct mistakes quickly. • Live voice or video sessions (screen-sharing friendly) where I ca...

...unpredictable outdoor lighting and weather. I am lookng for AI specialist partners who can assist me with european based projects who are maybe in asia or americas. Here’s what I’m after: a fully trained, well-documented solution that I can deploy at scale—think edge devices for low-latency alerts as well as a cloud endpoint for heavier batch analysis. I’m comfortable with mainstream frameworks such as PyTorch or TensorFlow, but I’m open to whatever stack you feel delivers the best accuracy–throughput balance. For clarity, these are the concrete deliverables I need: • A reproducible training pipeline (data loaders, augmentation, transfer-learning strategy, and hyper-parameter notes). • Trained weights that hit state-of-the-art ...

This project centres on building an end-to-end Natural Language Processing system that handles three distinct tasks: text generation, sentiment analysis, and named entity recognition. The solution should be production-ready, modular, and easy to extend, making use of well-maintained Python libraries such as Hugging Face Transformers, PyTorch or TensorFlow, and standard preprocessing tools (spaCy, NLTK, or similar). A typical workflow might involve fine-tuning large language models for generation, training lightweight classifiers for sentiment detection, and integrating an entity recognition component that supports custom taxonomies. Clean code, reproducible experiments, and clear documentation are essential so that future contributors can retrain or swap models as data evolves. I ...

...key-concept summarisation for the articles. On the numerical side I have time-series and tabular metrics that need feature engineering followed by forecasting. Finally, the image component involves basic classification and the option to link visual patterns back to the textual or numeric findings. I expect clean, well-commented Python code—ideally using Pandas for preprocessing, scikit-learn or PyTorch/TensorFlow for modelling, and a Jupyter notebook that shows the full workflow from raw input to final visualised output. Package versions should be pinned in a requirements file, and every model must be serialised so I can reproduce the results later. Deliverables • End-to-end notebook or script for each data modality plus an orchestration file bringing them together...

I’m building a computer-vision solution focused on precise image segmentation of vehicles. Everything will be developed in Python, so familiarity with libraries such as OpenCV, PyTorch or TensorFlow is essential. Scope • Prepare or recommend an appropriate dataset and handle data preprocessing. • Design and train a segmentation model that accurately isolates cars, trucks, buses and other vehicles from varied backgrounds. • Provide inference code that runs locally on CPU/GPU and returns clean masks for each detected vehicle. • Include clear comments, a , and a short README so I can reproduce your results. Acceptance criteria • Mean IoU and pixel accuracy benchmarks clearly reported. • Inference time per image noted on both GPU and CPU. ...

I’m building a computer-vision solution focused on precise image segmentation of vehicles. Everything will be developed in Python, so familiarity with libraries such as OpenCV, PyTorch or TensorFlow is essential. Scope • Prepare or recommend an appropriate dataset and handle data preprocessing. • Design and train a segmentation model that accurately isolates cars, trucks, buses and other vehicles from varied backgrounds. • Provide inference code that runs locally on CPU/GPU and returns clean masks for each detected vehicle. • Include clear comments, a , and a short README so I can reproduce your results. Acceptance criteria • Mean IoU and pixel accuracy benchmarks clearly reported. • Inference time per image noted on both GPU and CPU. ...

...reliable lift comparing accuracy, precision, recall, F1-score, confusion matrix, and ROC curves. Once the champion model is chosen, wrap it in a small Flask API for real-time scoring. A simple React dashboard should display the theft probability, key feature contributions and allow me to filter by account, date range or location. Please keep everything in Python (pandas, scikit-learn, TensorFlow/PyTorch—whichever you prefer) and make all queries database-agnostic so they run cleanly against PostgreSQL. The final repo should include: • Well-commented notebooks that reproduce the exploratory analysis, feature engineering and model training • The trained model artefacts plus a script to retrain with fresh data • Flask service with two endpoints: /score...

...duración que propones. Se aprueba con una demostración en un entorno de pruebas (staging) y evidencia de pruebas. No se dará acceso al repositorio ni se escribirá código hasta aceptar una propuesta a precio fijo. Tras la preselección compartiremos la especificación técnica bajo un acuerdo de confidencialidad (NDA). PERFIL REQUERIDO - Experiencia desplegando modelos de visión por computadora (PyTorch/ONNX) como servicio en producción - FFmpeg, colas de procesamiento asíncrono y evaluación de modelos (precisión/recall) - TypeScript, Node.js, Docker y automatización de implementaciones DOCUMENTO DE PROPUESTA (OBLIGATORIO) Adjunta un PDF o Word con: descripción de la soluci&oacu...

We’re looking for an experienced AI/ML Engineer for a freelance project, with potential for long-term work. Required skills: Python & Machine Learning AI/ML model development & deployment LLM / Generative AI OpenAI APIs, RAG, embeddings & vector databases TensorFlow / PyTorch / Scikit-learn API & backend integration Cloud/Docker experience is a plus Looking for: Someone with real-world AI/ML project experience, not just theoretical knowledge. Please share: Experience & relevant projects Portfolio/GitHub/demo Key AI/ML skills Hourly/project rate Availability Please apply with examples of AI/ML work you have actually built.

We are preparing a pipeline of machine learning projects that require extra hands for both model development and quality control. Your day-to-day tasks will revolve around three core responsibilities: • Data annotation and labeling of raw datasets so they are ready for model consumption • Model training and tuning in a Python environment (most of our codebase relies on PyTorch and TensorFlow) • Performance evaluation and reporting, including clear documentation of metrics and recommended next steps Because these projects feed directly into production systems, I place a high value on precise English communication and the confidence to defend technical decisions during brief stand-ups. Expect a friendly but fast–paced workflow: tickets are logged in Jira, code i...

...thermal, pressure, and acoustic channel continuously so the same code can be pointed at the live rig or at recorded sessions for offline work. • Feature engineering & model training – build a multi-class classifier that separates normal running from four distinct fault categories (bearing, water-pump, radiator, exhaust-valve). You are free to choose the algorithm and Python stack (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) provided the training script is fully repeatable. • Performance reporting – deliver class-wise and overall accuracy, precision, recall, F1-score, plus a 5 × 5 confusion matrix generated from held-out data. • Predictive-maintenance validation – produce a concise technical note (diagram, photo, or CAD screen...

...the same time, I need the same intelligence applied to my cybersecurity stack: log streams, traffic flows, and endpoint data should be analysed with the same anomaly-detection engine so I receive a single, consolidated alert panel instead of siloed dashboards. A clean API, preferably REST or gRPC, is essential so the solution can plug into my existing VMS and SIEM. Python and TensorFlow or PyTorch are fine, but I’m open to purpose-built platforms if they shorten deployment without locking me in. Deliverables • Model(s) trained for video + audio unusual-activity detection • Integration scripts / middleware connecting models to current cameras, mics, and SIEM • A lightweight dashboard (web or Electron) showing live alerts and confidence scores &bu...

...discipline - Required UI & Visualization Tailwind CSS - Utility-first CSS, responsive, data-dense layouts - Preferred Component libraries - Headless / unstyled component primitives (e.g. Radix) - Preferred Data visualization - Charting libraries, dashboards, S-curve and trend charts - Preferred Report generation - PDF, Excel, Word, PowerPoint export - Preferred ML / AI Infrastructure PyTorch & Hugging Face - Model fine-tuning, PEFT/LoRA, Transformers library - Preferred Voice / speech ML - STT, TTS, end-to-end voice pipeline development - Preferred GPU infrastructure - Self-hosted GPU servers, CUDA, resource monitoring - Preferred Inference serving - High-throughput model serving, quantization, optimization - Preferred Evaluation - Standard and custom domain-...

...reference ranges, flags abnormalities, and explains the potential implications in plain language. Where results form recognisable patterns (for example, anaemia profiles or inflammatory markers) the model should surface likely differential diagnoses and recommended next-step investigations. To get there I will need robust OCR or direct-feed parsing, a well-trained ML/NLP pipeline (TensorFlow, PyTorch or similar), and a lightweight cross-platform front-end—web first, with mobile to follow. Clinical accuracy, privacy (HIPAA / GDPR), and clear audit trails for every prediction are non-negotiable. Please send me a detailed project proposal covering architecture, data-set strategy, validation metrics, regulatory safeguards, timeline, and your past experience with comparable me...

I'm seeking an experienced mobile developer to create an AI-powered app for both iOS and Android platforms. The app will leverage natural language processing (NLP) to deliver advanced functionalities. Key Requ...and Android development - Experience with data handling and processing for AI applications - Strong UI/UX design skills for intuitive user experience - Ability to integrate AI seamlessly into mobile platforms Ideal Skills and Experience: - Proven track record in developing AI-driven mobile applications - Strong knowledge of NLP frameworks and libraries - Familiarity with tools like TensorFlow, PyTorch, or similar - Excellent problem-solving skills and attention to detail - Good communication skills for regular project updates Please provide a portfolio showcasing rel...

I need an experienced develop...measurement per region (before/after) using existing pretrained tools or simple image analysis - Identity similarity (ArcFace) and age estimation scores for each output Deliverables: - Clean, documented Python code (GitHub repository) - One inference script: input image + target age + selected region edits -> output images + scores - Short README explaining setup and usage Required skills: Python, PyTorch, Hugging Face Diffusers, Stable Diffusion XL, InstantID/IP-Adapter, inpainting, face parsing. Please include in your bid: - Links to previous Stable Diffusion / InstantID / inpainting projects - Estimated delivery time All edit settings (regions, prompts, edit strength) must be configurable through a config file, so I can adjust them myself af...

...(MMT) module and Cosine-Similarity-Guided Context Attention. Key goals • Produce discriminative spectrogram-based features that alleviate the severe class imbalance of the ZCHSound dataset. • Integrate MMT and the cosine-guided attention cleanly into PyTorch so each component can be toggled on/off for ablation. • Train, validate and test on the official ZCHSound splits, reporting multi-class metrics (accuracy, F1, AUC) and benchmarking against at least two published baselines. Deliverable checklist 1. Fully documented Python/PyTorch codebase (compatible with CUDA) that downloads/reads the dataset, preprocesses PCG audio into spectrograms, builds the Transformer-MMT network, trains, and outputs metrics. 2. Saved best model weights plus an infe...

...APIs, Pydantic models, async endpoints, middleware, and tools like pytest. * LLM / GenAI / Agentic AI: LangChain, LangGraph, Google ADK, Hugging Face ecosystem, prompt engineering, and structured output/tool calling. * RAG & Search: Ingestion pipelines, chunking strategies, embeddings, dense/hybrid retrieval, reranking, and advanced patterns like GraphRAG and agentic RAG. * ML & Deep Learning: PyTorch, TensorFlow, transformer architecture internals, inference optimization (KV cache, quantization), and fine-tuning concepts (PEFT/LoRA/QLORA). * AWS & Deployment: AWS Lambda, IAM, S3, CloudWatch, API Gateway, Docker containerization, and model-serving concepts. * CI/CD & Engineering: Advanced Git workflows, GitHub Actions, clean architecture, SOLID principles, an...

...clearly * Help convert analytical results into practical business or project insights ### Required Skills * Python * Data Analysis * Data Science * Machine Learning (ML) * Deep Learning * Exploratory Data Analysis (EDA) * Data Cleaning * Data Preprocessing * Predictive Analytics * Data Visualization * Statistical Analysis * Pandas * NumPy * Scikit-learn * Matplotlib / Seaborn * TensorFlow or PyTorch ### Nice to Have * Natural Language Processing (NLP) * Computer Vision * Time Series Analysis * Feature Engineering * Model Optimization * AI Model Development * Experience with real-world datasets ### What I’m Looking For I’m looking for someone who can **think analytically, write clean Python code, explain their approach, and deliver reproducible results**. Please...

...chatbots or customer-service flows—so everything you build should focus on ingesting streams of dated records and uncovering the structures that repeat, shift, or fade over time. I will supply the raw data in CSV format along with a short data dictionary. Your task is to: • clean and pre-process the series, • develop or fine-tune an AI model (Python with pandas, scikit-learn, TensorFlow, or PyTorch are all fine) that spots statistically significant patterns, and • generate a clear report and reusable code that reproduces the results on future datasets. Acceptance criteria • Model identifies known historical patterns with a precision/recall above 0.85. • Scripts run from the command line with one config file and no manual tweaks. • ...

I need a complete AI-driven recommendation system that learns directly from image data and returns high-quality, relevant suggestions in real time. The core of the job is to craft a model—preferably using Python, TensorFlow / PyTorch, and modern computer-vision techniques such as CNNs or transformers—that can extract visual features, compare them efficiently, and output ranked recommendations through a clean API. Here is what counts as done: • A reproducible training pipeline that ingests my image dataset, performs preprocessing and augmentation, then trains, validates, and saves the best model. • An inference service (REST or gRPC) that accepts an image or image ID and responds with a ranked list of recommended items plus similarity scores. • Brief ...

...understanding, computer-vision image recognition, or data-driven predictive analytics are all on the table—so I value someone who can help me assess the best path, outline a model architecture, and then build it. You would handle data preparation, model training and tuning, and finally packaging the model behind an API or other production-ready interface. Clean, well-commented Python code (TensorFlow, PyTorch, or a comparable framework) and version control via Git are essential so I can follow progress and maintain the project long-term. Deliverables • Technical outline of the chosen approach • Reproducible training scripts and notebooks • Trained model files and evaluation report • Deployment stub (Docker container, Flask/FastAPI app, or simil...

...building a small-scale image-processing project in Python and need help setting up reliable object-detection code. Before awarding the full job I’d like to see a brief proof-of-concept: a free test run on three sample images that I will provide. Here’s what I expect in that test: • A short script (Python 3.x) that loads each image, runs your preferred detection model (OpenCV, TensorFlow, PyTorch or similar), and draws bounding boxes around the detected objects. • The resulting images saved locally plus a concise console or JSON summary of the detections. • Clear, reproducible setup instructions ( or ) so I can run the test on my machine. If the test meets accuracy and clarity standards, we’ll move straight into expanding the pipeline&m...

...live-chat session on my site into a stream of insights, not just messages. The web app you build will capture conversations in real time, analyse them, and highlight user behaviour patterns that help us improve support, upsell at the right moment, and spot friction points immediately. The core of the job is data analysis, so the heart of the solution should be an AI/ML layer (Python, TensorFlow, PyTorch, or a stack you’re confident with) that ingests the live text feed, segments it, and runs pattern-recognition models. Think intent clustering, navigation sequence tracking, and anomaly detection rather than simple keyword counts. I need this intelligence wrapped in a clean web interface—React or Vue is fine—showing real-time charts plus searchable historical lo...

...tuning, then push recommendations straight into our CMS. Acceptance will be based on: 1. A live demo of the bot handling a full weekly content cycle across the three platforms. 2. An automated email flow that adapts subject lines after at least two iterations. 3. A before-and-after SEO report showing measurable improvements in page speed, meta relevance and keyword ranking. Python, TensorFlow/PyTorch, GPT-based text generation, Zapier/Make or similar for workflow orchestration, plus experience with Meta and YouTube APIs are all welcome skills if you have them—the goal is a single, self-learning system that reduces manual effort and steadily improves our reach and conversion rates....

...by an LLM or other AI models. • Trade execution – automated order placement and management through a broker API with tight latency control and clear error handling. • Performance reporting – instant P&L, risk metrics, and strategy diagnostics pushed into dynamic Excel dashboards for daily review. I’m open on the tech stack as long as it supports Python-based AI libraries (e.g., TensorFlow, PyTorch, LangChain) and connects smoothly to both Excel (via VBA or the Python Excel add-ins) and mainstream brokerage APIs. If you have proprietary shortcuts or pre-trained models that can speed things up, all the better. Acceptance criteria 1. A script or containerised app that runs end-to-end in a test environment, from data pull to trade confirmation....

...The target device is Android only, so everything—from the model optimisation to the demo app—must be tuned for that environment. Languages to be auto-identified and transcribed: English, Mandarin, Thai, Cantonese, Japanese, Korean, Hokkien and Malay. I am aiming for high identification accuracy; false detections must be the rare exception, not the rule. You are free to start from TensorFlow, PyTorch or ONNX models as long as you convert and fine-tune them with the RKNN Toolkit so that real-time performance is achieved on typical Rockchip boards (e.g., RK356x or similar). Quantisation awareness, mixed-precision tricks and any NPU-specific optimisations are all welcome, but latency must remain low enough for conversational use. Deliverables • An optimised RKNN...

...but I need an experienced partner who can turn these concepts into a production-ready solution. What I’m after • Model research & selection: advise on the most suitable architectures (YOLO-v8, Faster R-CNN, DETR, or anything you feel is a smarter fit). • End-to-end development: data preprocessing, augmentation, training, validation, and hyper-parameter tuning using Python with TensorFlow or PyTorch plus essential OpenCV utilities. • Performance optimisation: achieve fast inference on both GPU and edge devices (CUDA / TensorRT knowledge is a big plus). • Deployment wrapper: export to ONNX or TensorFlow-Lite and integrate a simple API or demo app so I can test detections live. • Knowledge transfer: clean, well-commented code in a privat...

I’m building a small end-to-end AI tool that first recognises what it sees in an image and then spins those findings into original science-fiction prose or poetry. Core needs The heart of the project is a solid image-recognition model—think TensorFlow or PyTorch—capable of tagging objects, settings and moods with good accuracy. Once the scene is understood, a text-generation layer (GPT-style or similar) should take those tags and weave them into creative, space-age narratives or concise poems. The writing must sound natural, imaginative and unmistakably sci-fi. What you’ll deliver • Trained image-recognition model with its labelled dataset or clear notes on the dataset used • Fine-tuned creative-writing module focused on science-fiction to...

...frameworks, and architectures - Document the model training process and provide technical guidance Required Skills & Experience - Proven experience as an AI Engineer, Machine Learning Engineer, or Data Scientist - Experience with training and fine-tuning AI models (LLMs, NLP models, or custom ML models) - Strong Python programming skills - Experience with machine learning frameworks such as: * PyTorch * TensorFlow * Scikit-learn * Hugging Face Transformers - Experience building data automation systems and AI-powered workflows - Strong understanding of data preprocessing, feature engineering, and model evaluation Financial Domain Requirements (Important) The candidate should have knowledge or experience in one or more of the following areas: - Financial statement...

Looking for an AI/Deep Learning developer to develop a research project for skin lesion classification using dermoscopy images. The project combines Diffusion Models for hair removal, Vision Transformer (ViT) for classification, and Explainable AI using Grad-CAM/Attention Maps. The dataset will be based on the ISIC Archive. Required: Python, PyTorch/TensorFlow, Deep Learning, Computer Vision, ViT, Diffusion Models, and Explainable AI.

...trading intelligence platform covering all 50 Nifty stocks, designed to bring institutional-level AI analysis to retail investors. The system unifies five distinct AI model families into a single scored decision engine (−5.0 to +5.0): - Classical ML — Random Forest with SHAP-based feature attribution for explainable UP/DOWN probability - Deep Learning — Custom LSTM and causal Temporal CNN (PyTorch) for 5-day return forecasting - Reinforcement Learning — PPO agent (Stable-Baselines3) trained via a custom Gymnasium trading environment for BUY/SELL/HOLD actions - Regime Detection — Gaussian Hidden Markov Model to classify BULL/BEAR market states - Financial NLP — FinBERT sentiment analysis on live Google News RSS feeds These signal...

...deploy a model that can interpret click-stream, dwell-time, and purchase history data, then return ranked suggestions through an easy-to-call API or directly inside the site’s codebase. Here is what success looks like to me: • A behavior-based algorithm (collaborative, content-based, or hybrid—you can advise on the best fit) developed in Python using libraries such as scikit-learn, TensorFlow, PyTorch, or a framework you prefer. • A lightweight service—REST or GraphQL—that exposes “getRecommendations(user_id)” so my front-end team can drop it straight into our existing stack. • Clear documentation covering data schema, model training pipeline, and deployment steps so we can retrain as new traffic patterns emerge. • ...

...for required meetings and working hours • Ability to work independently and meet project commitments • Willingness to represent professional experience accurately and personally complete all interviews Relevant technologies may include: DevOps/Cloud/SRE: AWS, Azure, GCP, Kubernetes, Docker, Terraform, CI/CD, Linux, monitoring, infrastructure automation and DevSecOps. AI/ML: Python, PyTorch, TensorFlow, scikit-learn, generative AI, LLMs, NLP, computer vision, MLOps, model deployment and cloud ML platforms. Data Engineering: Python, SQL, Spark, Databricks, Snowflake, BigQuery, Redshift, Airflow, Kafka, ETL/ELT, data lakes and data warehouses. How to Apply: Send a direct message containing: • Your résumé • LinkedIn profile &...

...Introduction → Literature Review & Research Gap → Methodology → Dataset & Experimental Setup → Experiments & Results → Comparative Analysis → Discussion → Limitations → Conclusion → Future Work → References The paper should include system architecture/workflow, literature comparison table, methodology diagrams, experimental tables/graphs, and relevant visual results. Technology Python, OpenCV, YOLO/PyTorch, ByteTrack/BoT-SORT or suitable alternatives. Pretrained models are acceptable; no need to build a model from scratch. Deliverables -Working prototype + source code -Experimental results -Tables/graphs/visualizations -Architecture/workflow diagram -Complete research paper -Proper academic references Publication ...