Aprendizaje Automático (ML) Jobs

Descripción de la Idea La idea consiste en crear una IA capaz de analizar continuamente la información del mercado bursátil y decidir de manera autónoma cuándo comprar y cuándo vender acciones. Para ello, la IA utilizaría datos como: Precios históricos de las acciones. Tendencias del mercado. Noticias económicas y empresariales. Indicadores financieros. Comportamiento de los inversores. Mediante algoritmos de aprendizaje automático, la IA mejoraría sus decisiones con el tiempo, aprendiendo de sus éxitos y errores. Objetivos Los principales objetivos del proyecto son: Maximizar los beneficios de las inversiones. Reducir el riesgo mediante análisis avanzados. Automatizar las operaciones bursátil...

I’m building an AI-driven solution that keeps single-family homes healthy by spotting structural issues, plumbing problems, and electrical faults while the house is in everyday use. The goal is a system that can take inputs such as periodic smartphone photos, CCTV streams, or smart-sensor readings and flag likely defects in real time so owners receive clear, actionable alerts before small flaws turn into costly repairs. Here is what I need from you: • A trained model (or ensemble) able to classify and localise those three defect categories with high precision on typical residential imagery or sensor data. • A lightweight inference pipeline that can run on consumer-grade hardware or be exposed through a cloud API. • A simple web or mobile interface where a homeow...

I have a functioning machine-learning application with a solid core, yet bugs keep surfacing and the overall accuracy still falls short of what I need. My goal is to push every component of the model toward peak performance while stamping out recurring issues. Here’s what I need from you: • Audit the current codebase to identify logic flaws, data-handling mistakes, and any silent failures that degrade predictions. • Refine model architecture and hyper-parameters so we see a measurable boost in accuracy on our validation set. • Strengthen the training pipeline with better data preprocessing, feature engineering, and reproducibility practices. • Implement automated tests and robust error logging so new bugs are caught early. • Document the improvements...

I’m ready to bring an online trainer on board who can guide an intermediate audience through real-world applications of Machine Learning, Deep Learning, Natural Language Processing, and some domain-specific AI tool know-how. Sessions will be delivered live, so I need someone who is comfortable explaining concepts clearly, fielding questions on the spot, and showing hands-on demonstrations in popular frameworks (think Python, TensorFlow, PyTorch, scikit-learn, Hugging Face … whatever fits the topic). Because we’d like to launch the program as soon as possible, you should already have slide decks, notebooks, and practical examples you can adapt quickly. A short diagnostic chat with me will finalise the exact schedule, but plan for multiple interactive sessions, each follo...

I am looking for someone who understands: Python / Pandas / NumPy Financial time series Macro indicators Backtesting Portfolio performance metrics Optimization methods such as grid search, random search, or Optuna Walk-forward testing / avoiding overfitting Questions for Applicants Please answer: Have you built any Python financial backtest or quant research notebook before? Have you worked with FRED or macroeconomic data? How would you test different weights without overfitting? What Python libraries would you use? Can you share a relevant example of your previous work?

I need to turn historical patient-record data into a reliable model that predicts individual outcomes and flags high-risk cases early. The raw material is a sizeable set of anonymised electronic health records: demographics, vitals, lab results, admission notes, discharge summaries—typical structured tables mixed with a handful of free-text fields that have already been de-identified. Here is what I’m after: • A clean, reproducible pipeline in Python and SQL that pulls the records, handles missing values, encodes categorical variables, and outputs a model-ready dataset. • One or more predictive models built in scikit-learn (feel free to benchmark XGBoost or similar) together with well-explained feature importance. AUROC is the key metric; I’d like to see cr...

I have an anonymised set of internal company emails that must be routed automatically to the correct department—Human Resources, Finance or IT Support. I am looking for an engineer who can take the project from raw data through to a Docker-ised REST service. The workflow I expect is: • Data preparation: strip any residual PII, apply consistent labelling for the three target departments, and document the pipeline so it can be rerun when fresh mail arrives. • Modelling: start with a TF-IDF + SVM baseline, then fine-tune transformer models (BERT or RoBERTa via Hugging Face). Compare approaches and capture precision, recall and F1 for each class. An error analysis explaining common misclassifications is essential. • Deployment: package the best model behind a FastAPI...

I run a production Python pipeline that extracts structured data from complex technical PDF documents — detecting objects, tracing linear features, and measuring areas and regions — using a mix of vision LLMs and computer vision. It works and is deployed, but I need someone who can take real ownership and push accuracy, robustness, and cost in directions I can't get to fast enough on my own. The pipeline is staged: render the PDF, read the document's reference key to learn what to look for, detect every instance on each page with a spatial vision model, measure each one (OpenCV contour/skeleton tracing, with an LLM agent fallback), and hand off structured results. What I need help with: - Improving detection accuracy and recall on dense, messy,...

I want to deploy an end-to-end AI agent that oversees my entire plastics line—starting at the raw-material warehouse and finishing on the pallet truck. Scope The system must handle three core domains: 1. Quality control • Real-time defect detection on every part coming off the machines • Continuous consistency checks against dimensional tolerances and colour specs • Material-compliance verification that flags any recipe or lot that drifts outside specification 2. Production optimisation • Extrusion, injection-moulding and thermoforming lines each need parameter-tuning models that minimise cycle time and scrap while maintaining quality • Predictive maintenance recommendations based on machine signals and historical downtime ...

I have a raw set of patient records and want to turn it into a functional medicine-recommendation engine built in Python. The system should read symptom text typed by a user, run it through a pre-trained machine-learning model, and return a ranked list of suitable medications. Scope and key tasks • Data pipeline: remove duplicate records and normalise numeric or categorical values so every observation is model-ready. • Feature selection & training: experiment with at least Random Forest and Logistic Regression; pick the best model using accuracy, precision and recall. • Evaluation report: include confusion matrices and a short note explaining why the chosen model performs best. • Lightweight dashboard: a simple Flask or Streamlit interface with one free-tex...

I want to build an AI-powered solution that automatically enhances and retouches images. The core idea is to feed photographs into the system and receive back professionally polished results—improved sharpness, balanced exposure, refined skin texture, and overall visual pop—without manual intervention each time. Here’s what I need from you: • Design or adapt a suitable model (GAN, diffusion, or other state-of-the-art architecture you trust) focused on image enhancement and retouching. • Implement the full pipeline—pre-processing, inference, and post-processing—so I can drop in a batch of images and retrieve the retouched versions in a predictable folder structure. • Keep quality high: no lost details, no over-smoothing, colors must remain...

I need a fully-functional stock-trading bot that I can adapt to my own strategies. The core requirement is clear: it must connect to a live equities brokerage, execute trades automatically, and let me plug in—or easily edit—my own trading algorithms without touching the underlying execution code. Here’s what I have in mind: • Trading scope: stocks only; no crypto or forex integrations are required. • Broker/API: I’m open to your suggestion (Interactive Brokers, Alpaca, Tradestation, etc.) as long as the API supports real-time order placement and data retrieval. • Strategy layer: the bot should separate the execution engine from the logic layer so I can swap, tweak, or A/B test multiple algorithms. Think simple function hooks, a JSON config, or a...

Pet Intelligence Founder & Concept Creator: Jasna Kuburic Core Platform Pet Intelligence is an AI-powered pet intelligence ecosystem designed for dog and cat owners, breeders, veterinarians, researchers, and animal organizations. The platform combines: * Health tracking * Behaviour tracking * Breed intelligence * Geographic intelligence * Lineage tracking * Genetics and DNA insights * AI-powered guidance Main Modules Pet Profiles Create detailed profiles for dogs and cats. Health Intelligence Track conditions, medications, vaccinations, surgeries, allergies, and wellbeing, owner types symptoms for different level of educational and not medical advice: monitor, visit vet soon or emergency Behaviour Intelligence Track temperament, trainability, a...

I need a reliable, camera-based solution that can automatically detect and count people captured by our existing CCTV network, regardless of whether the footage comes from indoor halls or outdoor entrances. The end goal is clear: turn those raw counts into actionable customer-analytics data I can slice by time, location, and traffic trend. Here’s what I’m expecting: • Accurate real-time or near-real-time counts from standard CCTV streams (no extra infrared hardware). • Robust performance under varying light and weather because some of our cameras sit outdoors. • An easy way for me to view and export daily, weekly, and monthly traffic reports—CSV download and a lightweight dashboard are perfect. • A straightforward deployment path: containerised code (...

I’m looking for an experienced researcher–author to co-develop, write, and successfully submit a skin-disease–detection paper to a genuine, APC-free SCI-indexed journal and takes acceptance guarantee. Scope • Craft a manuscript that introduces and validates a brand-new detection algorithm. • Build experiments around whichever deep-learning model is currently most promising—CNN, transformer, hybrid, or any other trending architecture—so long as the results outperform or clearly benchmark against existing methods. • Handle all stages from dataset curation, model training, result analysis, and figure/table preparation through to journal formatting, cover letter, and submission. • Guide the paper through peer review until acceptance. ...

Udostępnię klaster GPU 10 × RTX 3090 (240GB VRAM) – współpraca, partnerstwo lub projekty B2B Posiadam gotowy, w pełni wyposażony stacjonarny klaster obliczeniowy klasy AI compute / Render Farm i szukam partnera biznesowego, programisty AI/ML, studia 3D lub startupu do podjęcia stałej współpracy (w modelu podziału zysków lub jako podwykonawca). Sprzęt został w pełni przebudowany pod wymagające zadania compute i działa w trybie 24/7. Parametry klastra, który oddaję do dyspozycji: * GPU: 10 × MSI GeForce RTX 3090 SUPRIM X 24GB GDDR6X (łącznie 240 GB pamięci VRAM). * Moc: ~356 TFLOPS FP32 / ok. 2,8 PFLOPS Tensor (FP16). * Przepustowość VRAM: ~9,3 TB/s łącznej przepustowości. * Infrastruktura: Platforma serwerowa AMD EPYC, 256 GB RAM, szybkie dyski ...

Project Overview I am looking for an experienced web developer, data scraping specialist, and AI modeller to build a professional racing website that covers: * Jockey Challenges (Thoroughbred/Gallops Racing) * Driver Challenges (Harness Racing) The platform will track all jockeys and drivers at Australian race meetings, calculate live challenge leaderboards, generate AI-rated prices, compare bookmaker odds, and identify value betting opportunities. ⸻ Challenge Formats Jockey Challenge Predict which jockey will score the most points at a gallops race meeting. Driver Challenge Predict which harness racing driver will score the most points at a harness race meeting. Points System Points awarded per race: * 1st Place = 3 Points * 2nd Place = 2 Points * 3rd Place = 1 Point * Dead He...

- Developed ChurnShield, an end-to-end AI-powered platform for customer churn prediction and retention intelligence, utilizing Python, FastAPI, TensorFlow, and Streamlit. - Created a custom Artificial Neural Network (ANN) featuring three dense layers, batch normalization, dropout regularization, early stopping, and Adam optimization for churn prediction. - Implemented industry-specific machine learning pipelines for telecommunications and banking datasets, incorporating automated preprocessing and feature engineering. - Established intelligent churn risk classification systems that categorize customers into high, medium, and low-risk groups based on probability scoring. - Designed an interactive, cyberpunk-inspired analytics dashboard for real-time KPI monitoring, risk segmentation, cu...

I’m looking for an experienced full-stack AI/ML developer who can dedicate part-time hours during Central Time. Your immediate focus will be hands-on data analysis of well-structured datasets that live in our relational databases and spreadsheets. You’ll be expected to explore, clean, and transform this data in Python using pandas, then turn it into clear insights or prototype models that the rest of the team can act on. Because we operate in CST, I’ll need you available for regular check-ins, code reviews, and the occasional real-time pair-session. Fluent English is a must—we move quickly and rely on concise discussion to scope experiments and share findings with non-technical stakeholders. Typical workflow • Pull data from our existing SQL sources or flat...

Project Overview: We are building a data analysis platform and require a Computer Vision / Digital Signal Processing expert to implement a server-side automated motion analysis engine. The frontend application layer is already complete and successfully uploads a cropped "eye-strip" video (focusing strictly on the eye region) directly to our server backend. Your Task: you will leverage existing open-source frameworks (such as OpenCV contour tracking, MediaPipe Iris, or similar established eye-tracking libraries) to build a highly reliable server-side script (Python preferred). This script must trigger automatically upon video upload to process the cropped video frame-by-frame. Core Technical Deliverables: 1. Object Tracking & Noise Filtering: Utilize MediaPipe Iris or OpenCV...

We intends to implement a comprehensive Electronic Document Management System (EDMS) as part of strategic digital transformation roadmap. The project aims to facilitate a phased yet complete transition from a traditional paper-based archive environment to a centralized, modern, and secure electronic document management platform. The key objectives of this initiative are: Improve the efficiency and speed of document retrieval and information access. Enhance protection of corporate records and information assets against loss, damage, or disasters. Strengthen governance, compliance, regulatory adherence, and audit readiness. Reduce operational costs associated with physical document storage and archive management. Support business continuity and operational excellence through secure...

Machine Learning Framework & Automated Dataset Generation for FANET Jamming Detection (NS-3) Hello, I am currently working on a research project titled: “Intelligent Jamming Detection in Flying Ad Hoc Networks (FANETs) Using NS-3 Simulation and Machine Learning.” I have already completed the NS-3 simulation phase and now need support with the machine learning, automated dataset generation, and data processing phase. ⸻ What Has Already Been Completed * Built FANET scenarios in NS-3 * Implemented the Gauss-Markov mobility model * Implemented multiple jamming attack types: * Reactive jammer * Hybrid jammer * Constant jammer * Random jammer * Executed simulations under multiple network conditions * Generated network performance metrics from NS-3 outputs ...

Responsibilities: Guide me on how to use Snorkel AI or Snorkel Flow for my training task Help design and implement labeling functions Assist with data labeling strategy and weak supervision setup Explain how to prepare data for model training Help train, evaluate, and improve the model workflow Provide hands-on support through screen sharing or step-by-step guidance Review my current dataset and recommend the best Snorkel approach Required Skills: Strong experience with Snorkel AI, Snorkel Flow, or open-source Snorkel Experience with weak supervision and programmatic labeling Python experience Machine learning model training experience Experience with classification, NLP, text labeling, or data annotation workflows Ability to explain clearly and guide someone step by step Deliverables:...

I need an experienced AI consultant / architect to step in and re-shape the core of our SaaS platform so it runs smarter, faster, and at lower cost. The immediate focus is product functionality: I want machine-learning driven logic that streamlines internal workflows, eliminates manual bottlenecks, and raises overall operational efficiency for every customer session. Here is what I expect from our collaboration: • A concise audit of the current codebase, data flows, and cloud setup (AWS/GCP, Kubernetes micro-services, or similar). • A future-proof architecture blueprint that shows exactly where AI components—whether Python micro-services, TensorFlow / PyTorch models, or third-party APIs—plug in. • A phased implementation roadmap with clear milestones, risks...

I am looking to partner with someone who can build AI software in the recruitment/sales space (happy to walk you through the process if needed). The software that you would typically build would aim to cut down admin time based on a specific companies needs, avoiding other generic AI tools. I would pre-prepare you on a call with every detail around the client, alongside a detailed brief for the end product. You’ll guide the entire build: selecting or training the right models, designing, integrating them into a clean front-end (web or desktop, whichever you recommend), and setting up a deployment pipeline so updates are painless. I’m open to popular frameworks such as Python + FastAPI, Node.js, TensorFlow, PyTorch, or any stack you’re confident in, as long as scalabilit...

I need a bot that runs on both Pocket Option and Quotex and looks two minutes ahead to tell me, with high accuracy, whether the next candle should be taken as a CALL or a PUT on forex and crypto pairs. The core logic has to fuse RSI, Moving Averages and Bollinger Bands in real time, evaluate the confluence and immediately print (or speak) the recommended direction before the current candle finishes. I am flexible on the programming language, as long as the final solution is stable, lightweight and easy for me to configure (pair, stake size, risk filter, sound/visual alert). A simple panel or command-line menu is fine; what matters most is the speed of the signal and the reliability of the prediction engine. Deliverables • Source code with brief inline comments • Executable ...

Dont bid indian and pakistan. only seeking American or European I’m looking to connect with experienced AI Engineers who are interested in collaboration opportunities related to the U.S. tech job market. Requirements: - Strong experience in AI, ML, LLMs, RAG, agents, or backend AI systems - Good software engineering foundation - Strong English communication skills, preferably C1 level or above - Reliable, professional, and open to long-term collaboration If you’re interested, please feel free to contact me. I’d be happy to share more details and discuss possible collaboration opportunities.

A new cross-disciplinary build is about to start, combining solid software engineering with practical artificial-intelligence features. The first milestone centres on scoping: together we will decide whether the core product emerges as a web, mobile or desktop application and lock down the primary language stack—Python, JavaScript, Java or a hybrid approach. From there, the work shifts to architecture design, clean code implementation, and the integration of an AI component that demonstrably adds value (machine-learning, NLP, or computer-vision models will be chosen once use-cases are finalised). Version control through Git, test coverage, and concise technical documentation are expected throughout the sprint. The immediate deliverable is a working proof of concept that showcases a...

The heart of this project is machine learning model development. I have a large image-based dataset ready and need a specialist who can turn it into production-grade models while also laying the groundwork for three NLP capabilities—text classification, sentiment analysis, and a simple conversational chatbot. Here is what success looks like for me: • Modular scripts or notebooks showing how the same workflow can be adapted for text classification and sentiment analysis; you are free to leverage Hugging Face Transformers, spaCy, or similar libraries. • A lightweight chatbot prototype (could be RAG-based or fine-tuned) that demonstrates integration of the above text models. • Handshake, Outlier experience is big plus

I am in the middle of my master’s thesis and need a capable partner to handle the coding side of a deep-learning project on mammogram images. The core task is to build a robust model for breast-cancer diagnosis; once the network architecture is in place I can take care of statistical write-ups and broader discussion, but I need you to get the codebase production-ready and well-documented. Here’s what the collaboration will look like: • Data: I will supply the curated mammography dataset and any preprocessing scripts I have so far. • Model building: Using Python with either TensorFlow or PyTorch (whichever you work fastest with), you will design, train and fine-tune a CNN or transformer-based pipeline that targets breast-cancer detection specifically. • Resu...

I need to turn a Large Language Model into a practical, real-time assistant that runs directly on an NVIDIA Jetson board. The model will reason over image data at the edge, so every millisecond saved and every megabyte spared matters. Memory-optimised execution is therefore the single most important constraint, though I still expect you to keep latency low and power draw sensible. Here is what I want to achieve. The LLM must accept a pre-processed visual input, apply prompt-engineering tricks that preserve context, and reply fast enough to be useful on-device—no cloud fallback. Smart caching, selective quantisation, and, when it genuinely pays off, lightweight fine-tuning are all on the table. I will look to you to suggest the right mix of techniques and to implement them. You shou...

I want to learn how to use ai for data engineering project

I’ve built an AI algorithm that shows real promise, yet it has never been put through a proper test run. My end-game is academic: I want to validate the model rigorously, benchmark it against established baselines, and steer it toward a publishable research paper. To get there, I’m seeking a collaborator who thrives on algorithm development. You’ll dive into my existing codebase, stress-test the logic, refine any weak spots, and help translate the results into clear, defensible findings. Along the way we can discuss experimental design, datasets, and suitable evaluation metrics so the work meets journal or conference standards. If you enjoy turning raw, untested ideas into defensible research—and you’re happy to share authorship and intellectual credit&m...

Core required Senior full-stack web — TypeScript, Node, React, Vite, Express, Drizzle, SQLite/Postgres Multi-tenant SaaS architecture — auth (OAuth/JWT), user namespacing, row-level access control, SQLite → Postgres migration Payments and metering — Stripe (or adult-friendly equivalents like CCBill, Segpay, Epoch), subscription webhooks, credit systems, per-feature usage metering GPU infrastructure / ML ops — RunPod, Modal, Replicate, or Kubernetes + bare-metal GPU; queueing, autoscaling, cold-start handling, retry/backoff ComfyUI — workflow JSON editing by node ID, custom node management, model file management, VACE for face swap Media storage and delivery — S3 / R2, CDN (CloudFront / ), signed URLs, retention policies FFmpeg / video pipeline &mda...

I need an interactive and engaging application for both web and mobile platforms. The app will allow users to upload side and front photos of their dogs. An AI will estimate body measurements, which will be mapped to our existing size chart data/thresholds. Users can confirm or edit these measurements before receiving a size recommendation. Key features include: - User photo uploads for measurement estimation - AI body measurement estimation - Size mapping to existing size chart data - User confirmation/editing of measurements - Size recommendation output, ideally 'We recommend a size Small for Daisy in this product' Ideal skills and experience: - Experience in AI/ML, particularly in image processing - Proficiency in web and mobile app development - UI/UX design for interactive...

I am commissioning a full-featured vibration analytics and predictive maintenance platform dedicated to rotating equipment. The end product must run smoothly on both Windows and Linux, interface seamlessly with industrial vibration sensors, and stream data in real time. Core analytics Real-time FFT and spectrum analysis, envelope detection, bearing defect diagnostics, and automatic alarm generation are mandatory. The system should reference a built-in bearing database covering the major bearing types and models we see in the field, so users can jump straight from a fault signature to a likely root cause. Dashboards & visualisation Inside the application, I need rich, configurable dashboards that present data as interactive graphs and charts, heatmaps, and concise tables. Historic...

Machine Learning Framework & Automated Dataset Generation for FANET Jamming Detection (NS-3) Hello, I am currently working on a research project titled: “Intelligent Jamming Detection in Flying Ad Hoc Networks (FANETs) Using NS-3 Simulation and Machine Learning.” I have already completed the NS-3 simulation phase and now need support with the machine learning, automated dataset generation, and data processing phase. ⸻ What Has Already Been Completed * Built FANET scenarios in NS-3 * Implemented the Gauss-Markov mobility model * Implemented multiple jamming attack types: * Reactive jammer * Hybrid jammer * Constant jammer * Random jammer * Executed simulations under multiple network conditions * Generated network performance metrics from NS-3 outputs ...

I have a sizeable set of customer demographic data—age ranges, locations, genders, income brackets and the like—and I want to turn it into clear, actionable business insights. The objective is straightforward: explore the dataset, surface meaningful patterns, and present findings that can guide our marketing and product decisions. You will receive the raw CSVs in a secure S3 bucket. Feel free to work in Python with Pandas, scikit-learn, seaborn—or any comparable stack that lets you move quickly and keeps the process fully reproducible. I’m especially interested in: • Segmenting high-value or high-growth customer groups • Highlighting correlations between demographic traits and purchasing propensity • Visualising trends in a way executives can ab...

I am looking to partner with someone who can build AI software in the recruitment/sales space (happy to walk you through the process if needed). The software that you would typically build would aim to cut down admin time based on a specific companies needs, avoiding other generic AI tools. I would pre-prepare you on a call with every detail around the client, alongside a detailed brief for the end product. You’ll guide the entire build: selecting or training the right models, designing, integrating them into a clean front-end (web or desktop, whichever you recommend), and setting up a deployment pipeline so updates are painless. I’m open to popular frameworks such as Python + FastAPI, Node.js, TensorFlow, PyTorch, or any stack you’re confident in, as long as scalabilit...

I’ve built an AI algorithm that shows real promise, yet it has never been put through a proper test run. My end-game is academic: I want to validate the model rigorously, benchmark it against established baselines, and steer it toward a publishable research paper. To get there, I’m seeking a collaborator who thrives on algorithm development. You’ll dive into my existing codebase, stress-test the logic, refine any weak spots, and help translate the results into clear, defensible findings. Along the way we can discuss experimental design, datasets, and suitable evaluation metrics so the work meets journal or conference standards. If you enjoy turning raw, untested ideas into defensible research—and you’re happy to share authorship and intellectual credit&m...

I’m adding an end-to-end virtual fitting room to my e-commerce site so visitors can upload a full-body photo and immediately see our apparel draped on their own figure—accurate pose, lighting, and cloth dynamics included. The workflow I have in mind combines human-parsing, garment warping, and a lightweight generative model that streams results straight to the browser. Core features I need implemented: • Real-time try-on with latency low enough to feel instant • Save & share so customers can download a composite or post directly to social platforms • Size recommendations that read body proportions from the uploaded image and match them to our inventory’s size chart I’ll provide product photography and size tables; you’ll build the co...

I need a complete Python pipeline that ingests CCTV streams from several in-store cameras and turns them into actionable retail metrics. Using YOLOv8 for detection, ByteTracker plus a solid Re-ID module for cross-camera identity preservation, the system must • detect and track people only (no vehicles, animals, etc.) • avoid double-counting the same shopper when they move between overlapping views • distinguish staff from shoppers so employees are never included in customer statistics • produce reliable foot-traffic counts, crowd-density heatmaps and an “entry-to-purchase” conversion figure (how many visitors become paying customers) Please deliver: 1. Well-structured, documented Python code (YOLOv8 + ByteTracker + ReID) that runs on recorded f...

I want a cross-platform Flutter application that acts as my digital “CA Diary,” focused exclusively on managing client information. The core flow should let me open the app, find any client instantly, and see everything relevant to our work together in one place. Key functional areas I must have: • Client profiles – basic details, PAN/GST numbers, contact data and custom tags. • Document storage – upload, view and categorize files against each client. PDF and image formats are enough for now. • Interaction history – a simple timeline showing calls, emails or in-person meetings so I can track what happened and when. • GST & IT Return modules – record returns filed, due dates and status. • Service tracking –...

I’m moving our AI inference off OpenAI and onto a Tesla P100 16 GB box that already runs qwen2.5 7B/14B on Ollama. The backend is wired to switch between local and remote per-prompt, so the infrastructure work is minimal; the real task is model selection, tuning and validation. Cost reduction is the driving motive, but I will only flip a prompt when the dashboards show zero drop in the accuracy of CV match scores (real interview rate is the secondary check). We have four production prompts; each will run in shadow mode until its metrics are indistinguishable from the current OpenAI baseline. What I need from you • Pick or fine-tune the best qwen2.5 variant—or another model that will fit and perform on a single P100—then set quantisation, context window and batc...

I have a daily stock-price dataset that already contains the open, close, high, and low values for each trading session. I need a fully reproducible AutoGluon time-series model that can forecasts price movement.

The project centers on building a production-ready TensorFlow 2.x model that classifies tabular data delivered to us through an internal API. I have the API specifications and sample payloads ready; you will turn those streams into a clean training pipeline, engineer the right features, and iterate until the classifier meets our performance targets in real-world tests. Scope of work • Data pipeline – pull the API data, handle preprocessing, and produce TensorFlow-friendly datasets for train/val/test splits. • Model development – design, train, and tune a deep learning architecture suitable for tabular inputs (e.g., wide & deep, Transformer, or other proven structures). • Optimization – experiment with hyperparameters, regularization, and callback...