Naive bayes classifiertrabajos

Full detail in notion # Expert Needed — AI Email Classifier Improvements (3 Enhancements) ## Overview We have a working scenario that automatically classifies incoming emails using GPT-4o-mini and moves them to the correct folder in Zoho Mail. The core flow is functional (Webhook → GPT → Router → move email). We need an experienced developer to implement 3 specific improvements directly in the Make visual editor. **Estimated scope:** 3–6 hours total. Fixed price preferred. --- ## Current State - Scenario #4543102 in (team ID: 1835732) - Classifies emails from a single inbox into 7 categories: `cotizacion`

...Python robusto, diseñado para explotar ineficiencias en mercados de Criptomonedas/Forex mediante dos estrategias primarias y con un control de riesgo riguroso. Estrategias a Implementar El bot debe operar con dos módulos paralelos, priorizando la baja latencia:1. Módulo de Scalping de Momentum (Clasificación)Activo Principal: Criptomonedas de alta liquidez (ej. ETH/USDT).Modelo ML: Random Forest Classifier (pre-entrenado), diseñado para predecir la dirección del precio a corto plazo (subir/bajar).Features de Entrada (Input):Volatilidad/Riesgo: ATR (Average True Range, período 14).Momentum: RSI (Relative Strength Index, período 14) y MACD.Lógica de Ejecución: La orden se lanza solo si la predicción de...

Estoy buscando un desarrollador con experiencia en TinyML, TensorFlow, C++, para crear un clasificador de sonidos ambientales en Arduino. El modelo debe ser capaz de reconocer sonidos específicos como timbre de puerta, teléfono y ruido de fondo. El objetivo es implementar este modelo en un microcontrolador. Requisitos clave: - Desarrollar un modelo de TinyML para clasificar sonidos ambientales específicos. - Optimizar el modelo para su ejecución en un microcontrolador. - Asegurar la precisión en la detección de sonidos como timbre, teléfono y ruido de fondo. Habilidades y experiencia ideales: - Experiencia en desarrollo de modelos TinyML. - Conocimiento en procesamiento de señales de audio. - Habilidad para trabajar con microcontro...

¿Cómo se utiliza el teorema de Bayes y la distribución normal? Introducción: ¿Aún recuerdas la campana de Gauss y el teorema de Bayes? En este Reto necesitarás tus conocimientos sobre el teorema de Bayes y la Distribución de la probabilidad. ¡Adelante! Situación 1. Plantea tres ejemplos de la aplicación de la distribución de probabilidad normal. En cada uno de los ejemplos desarrolla: - Grafica una campana de Gauss. - Calcular por fórmula o por tabla y escribir la conclusión del Ejercicio. 2. Investiga dos situaciones de campo (en la industria) en las que se pueda aplicar el teorema de Bayes. Con base en esta investigación se propondrán dos ejemplos ...

Busco personas interesadas en encontrar soluciones a sesgos cognitivos y optimización personal. Por el momento me baso en la teoría de la información. key words matemathical thinking, racionalidade, solución de problemas con bayes, scientific thinking, bayes rule, bayesian network, entropy, mutual information, causal inference

Necesita tener muy buen nivel en los siguientes conocimientos: ESTADIS...ESTADISTICA DESCRIPTIVA - Medidas de localización y de tendencia central - Medidas de dispersión - Técnicas gráficas para explorar datos FUNDAMENTOS DE PROBABILIDAD - Espacios muestrales y eventos - Métodos de conteo - Axiomas de la probabilidad - Propiedades de la probabilidad - Probabilidad condicional FUNDAMENTOS DE PROBABILIDAD - Regla multiplicativa - Probabilidad total - Teorema de Bayes - Independencia Estadística Por favor solo aplicar si tienes muy buenos conocimientos en estos temas a nivel universitario, no es solo como conocimientos matemáticos básicos. Necesita tener disponibilidad de dos horas este lunes próximo de 12 pm de...

Necesito una aplicación para demostrar mineria de datos de una base de datos de donacion de sangre donde se pueda demostrar Árboles de desiciones Bayes naives Regresión lógica Serie temporal Clustering entre otros

Job I want to get done: I am a domain expert and want to train algorithms to extract data from my industries must common documents. Deliverables I expect - A plan on how you will get this job done and a brief explanation on why you would choose some tools, algorithms and techniques over the others. - A hybrid (supervised machine learning / rule based) text classifier. - A guide showing the guidelines and process I should follow to make best use of the tools including a golden standard. - Future support when needed.

Una web que ya está creada utilizando la interfaz de shinny de R. Se centra en la extracción y manejo de datos provenientes de Twitter, tambíen con la aplicación de aprendizaje por algoritmos automáticos tipo SVM y Naive Bayes. Sería para dar soporte para terminar el proyecto. 50€

Necesito un software que realice el teorema de bayes, que reciba un data set y que en base a este data set se pueda clasificar los datos.

Desarrollo de una aplicación usando naive bayes con entrada de bits en cadena de caracteres (100 bits), la cual permita inferir previamente entrenada, una determinada respuesta.

Necesito que se termine un apartado de un proyecto que estoy acabando, urge rapidez y eficacia. Consiste en realizar un clasificado de Naives Bayes con suavizado de Laplace, entrenamiento y clasificación.

Necesito que se termine un apartado de un proyecto que estoy acabando, urge rapidez y eficacia

I need a robust, high-performance automation tool to verify and categorize approximately 300,000 user accounts from two databases. The tool will check login validity on a specific portal and classify accounts by type. Key Technical Challenges: Large Scale Processing: The tool must handle 300,000+ entries efficiently. I need a developer who understands multi-threading and asynchronous processing to ensure the task doesn't take weeks. Smart De-duplication: There are many duplicates across the databases. The tool must include a pre-processing step to clean and de-duplicate the list before starting the verification process to save time and resources. Account Classification: For every successful login, the tool must scrape the account dashboard to identify if it is a Corporate (Enterpri...

...include some missing values * include some borderline/noisy cases * include some contradictory cases * have labels generated logically from feature combinations, not randomly Required outputs for the dataset part: * `dataset/` * `dataset/` * `app/ml/` The ML system must be a **decision support system**, not a blind automatic classifier. The intended flow is: * user enters a case * system stores it in MySQL * ML model gives a preliminary prediction * system shows confidence/probabilities * expert reviews it * expert can confirm or change the final classification * system stores both model output and final expert decision * system stores audit history Classification categories: * Suspected homicide * Suspected suicide * Suspected

...include some missing values * include some borderline/noisy cases * include some contradictory cases * have labels generated logically from feature combinations, not randomly Required outputs for the dataset part: * `dataset/` * `dataset/` * `app/ml/` The ML system must be a **decision support system**, not a blind automatic classifier. The intended flow is: * user enters a case * system stores it in MySQL * ML model gives a preliminary prediction * system shows confidence/probabilities * expert reviews it * expert can confirm or change the final classification * system stores both model output and final expert decision * system stores audit history Classification categories: * Suspected homicide * Suspected suicide * Suspected

...information into a reliable attrition-prediction pipeline. Work starts with careful cleaning and preprocessing: handle missing values, encode categorical variables, standardise or normalise where needed and document every step so the workflow is fully reproducible. A brief exploratory analysis should follow to highlight key attrition drivers and verify data quality before modelling. For the classifier, I’d like you to focus on K-Nearest Neighbours. If you find that another algorithm beats KNN convincingly, feel free to present the comparison—but please include KNN in the final report. Train, tune and validate the model, then evaluate it with accuracy, precision, recall, F1 and ROC-AUC. I expect a concise explanation of hyper-parameter choices and cross-validation re...

...Exploratory Data Analysis (EDA) Performed deep EDA to uncover: Customer behavior trends Churn patterns across geography, age, and balance Correlation between features and churn Created visualizations: Heatmaps, distributions, count plots Identified key drivers of churn: Age, inactivity, low engagement, and account balance Machine Learning Models Implemented Logistic Regression Random Forest Classifier K-Nearest Neighbors (KNN) Support Vector Machine (SVM) XGBoost Gradient Boosting Handling Imbalanced Data Applied SMOTE (Synthetic Minority Oversampling Technique) to: Balance churn vs non-churn classes Improve recall and F1 score for minority class Used class weighting for better model fairness Model Performance Summary Evaluated using: Accuracy Recall F1 Score ROC-AUC Score Key...

I have a large, continually growing collection of emails that needs to be processed automatically. The goal is twofold: 1. Classify each email into predefined business categories with high accuracy. 2. Extract relevant entities (names, dates, IDs, product references, etc.) from the same messages. You will own the entire machine-learning workflow. That means cleaning and exploring the raw email text, crafting useful features, training and tuning your models, and packaging the final solution behind an API that I can call from our existing back-end. Python is a must, and I’m comfortable with either TensorFlow or PyTorch for the deep-learning components—use whichever lets you move fastest. Traditional techniques with Scikit-learn are welcome wherever they make sense. Because t...

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

## **Combined Assignment Question** Using the datasets provided (** and **), apply appropriate data mining techniques to perform classification and clustering analysis. --- ### **Part 1: Naïve Bayes Classification ()** Using the cola preference dataset: 1. Apply the **Naïve Bayes method** to classify the 100 customers into: * **Regular** * **Light** cola preference 2. Based on your model, classify the following new customer: * Male, Married, Income = $42,000, Age = 47 * **State whether the customer prefers Regular or Light**, and justify your answer. 3. Evaluate the overall performance of your model: * How accurate is the classification? * Provide an interpretation of the results and any limitations of the model. --- ### **Part 2:

...discovery time by ~40% for stakeholders by replacing static reports with drill-down filters by location, vehicle type, and accident cause. • Applied DAX-based time intelligence to compare YoY fatality trends, revealing a 15% increase in night-time accidents that guided policy recommendations. AI-Enabled Multi-Disease Detection System | Machine Learning | Python • Built a multi-label ML classifier detecting Diabetes and Heart Disease with 87%+ accuracy using Logistic Regression and Random Forest on a 1,000+ patient dataset. • Reduced false negatives by 18% through feature engineering and threshold tuning — critical for early medical diagnosis applications. • Automated the full pipeline: data preprocessing, EDA, model training, and evaluation ...

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I need a Natural Language Processing solution that accurately classifies social-media posts into predefined categories. The raw text will be provided in CSV format; it comes directly from public platforms and carries the usual noise—emojis, hashtags, abbreviations, and mixed languages—so an effective preprocessing pipeline is as important as the model itself. Here is how I picture the workflow. • Data handling: robust cleaning, tokenisation, and normalisation that respects emojis and common social-media shorthand. • Model building: a modern text-classification architecture (transformers via HuggingFace, or a lightweight scikit-learn baseline if you can justify comparable performance). • Training & evaluation: use train/validation/test splits and report ...

This task involves a certain level of complexity, as it requires accurately distinguishing between similar sound patterns while ensuring the model is not affected by amplitude variations. In addition, this approach should not rely on amplitude-based features. Instead, it may require advanced techniques such as blind source separation/localization and modern signal processing methods to improve robustness and accuracy. Although feature extraction methods such as MFCC and PCEN can be used, the results may still be influenced by amplitude levels, which can affect the inference accuracy. Therefore, I will assign this project to a suitable person with the required expertise. Thank you.

This quote covers the development of a synthetic ventilator waveform dataset for PVA (Patient-Ventilator Asynchrony) classification research. Deliverables: 1. Synthetic Waveform Generator (Python) - Realistic pressure, flow, and volume waveforms for normal breathing - All target asynchrony...(PNG) - Ready for clinical review and validation 3. Full Documentation - Methodology document explaining each asynchrony model - Mathematical parameters and physiological rationale - Literature references for each waveform type - Step-by-step guide suitable for PhD committee presentation Timeline: 7 days from start Revisions: Up to 2 rounds of adjustments based on feedback Note: Neural network classifier and mobile app prototype are separate phases to be quoted after dataset validation by c...

I’m developing a graduate-level research project that merges smart textiles with security screening: an item of electronic clothing able to detect concealed drugs by combining millimeter-wave sensing with an onboard AI classifier. Where I am right now • Concept development for the millimeter-wave chip placement and antenna layout is underway, but I need an experienced hand to transform these early sketches into a fully realised design and working prototype. What I need from you • End-to-end design and prototyping of the garment, selecting suitable fabrics, conductive threads, flexible PCBs and power management solutions that can live comfortably inside everyday clothing. • Integration of both technologies—millimeter-wave chips fo...

I'm looking to develop an image classifier model to detect and classify patient ventilator asynchrony events, specifically trigger asynchrony, cycle asynchrony, and flow asynchrony. Currently, I don't have any data for training the model. I need to generate synthetic waveforms using a combination of mathematical models and rule-based algorithms. Key Requirements: - Generate synthetic waveforms for training - Use a combination of mathematical models and rule-based algorithms - Classify three types of asynchrony events: trigger, cycle, and flow Ideal Skills and Experience: - Proficiency in Python - Experience with synthetic data generation - Knowledge of image classification and machine learning - Familiarity with ventilator waveforms and asynchrony events

I’m developing a Flutter application that must run completely on the user’s device. Using TensorFlow Lite together with MediaPipe, the app should: • accept images taken directly from the camera or selected from the gallery • perform all processing offline, without any server calls • classify the image, return a set of labels, and generate a short auto-caption in real time I will supply UI mock-ups; what I need from you is the full integration of a suitable TFLite model (or a pair of models, if one is better for captioning) and the MediaPipe image pipeline, plus clean Dart code that exposes a simple method such as classifyImage(File img). Final output should include the Flutter project, the model files, brief setup notes, and a README that explains how to re...

I am looking for help to build a cybersecurity pipeline for automated source code vulnerability analysis. I need an end-to-end architecture that handles detection, localization, explanation, and remediation using a combination of Deep Learning and LLMs, rather than a simple demo. Technical scope: DL-Powered Detection & Localization: A multi-class classifier to categorize multi-language code (starting with C/C++) at the function or file level. It must predict whether code is Safe, belongs to specific top CWE classes, or falls into an "Unknown/Other" category. It must also pinpoint suspicious line numbers and code segments. Code Processing: Use sliding window techniques for long code—no simple truncation. LLM Explanation Generation: An LLM pipeline to output d...

...specifications - Dashboards: KPIs and management panels Technical Requirements Backend (Laravel 12) - Experience: Laravel 12, PHP 8.2+, PostgreSQL - Concepts: Multi-tenant, migrations, observers, events - Integrations: REST APIs, webhooks, OAuth authentication - Standards: PSR, SOLID, clean architecture Frontend (Vue.js/Vben) - Experience: Vue 3, TypeScript, Vite, Pinia - Framework: Vben Admin (Naive UI) - Concepts: Componentization, state management, routing - Standards: Composition API, reactivity, optimization Development - Methodology: Agile, incremental deliveries - Versioning: Git, semantic, code review - Testing: Unit, integration, E2E - Deploy: Docker, CI/CD, cloud environment Benefits and Advantages - Real Project: Production ERP system for public management - Mode...

...prompt to impact. NON-INVASIVE ARCHITECTURE FULL FEATURE LIST 1. LLM Observability Engine What it does: Detects hallucinations, contradictions, ambiguity, or timeouts Back-End: FastAPI, Prompt Log DB, Anomaly Classifier Front-End: Table view, risk icons, filters 2. User Behavior & Friction Analysis What it does: Detects frustration, reprompt loops, abandonment Back-End: Event tracker, heatmap aggregator Front-End: Heatmaps, UX breakdown, friction flags 2.1 Failure Detection & Alert System What it does: Real-time alerting for broken behavior Back-End: Classifier (LLM + rules), notifier API Front-End: Risk graphs, alert logs, admin config 3: AI Ethics & Risk Monitoring (Comprehensive) Multi-layer ethics detection pipeline Toxicity det...

I am ready to dive into natur...exploratory analysis, then walks through feature engineering (tokenisation, embeddings, etc.), model selection, training, evaluation and deployment. • Well-commented Python notebooks and sample datasets so I can reproduce every step on my own machine. • Short explanations of the underlying math concepts, delivered in plain language. • At least one mini-project where we build and benchmark a text-classifier end-to-end. • Live or recorded walkthroughs so I can watch your workflow and ask questions. I learn fastest by doing, so each concept should be paired with code I can immediately run and modify. If this format works for you, let me know how you would structure our sessions and what materials you already have that can a...

I need help to reduce overfitting in my hybrid vision transformers/CNN model for prostate cancer classification. Current setup: - Training on medium resolution image data - Data augmentation applied, but only one technique Ideal skills and experience: - Strong background in deep learning, especially with vision transformers and CNNs - Expertise in image data processing and augmentation techniques - Experience with model optimization and overfitting reduction

...treated with equal weight so that any conclusions hold across handwriting and apparel imagery alike. Before training, every image batch must pass through Normalization and Feature Scaling, and I’d like to see creative yet reasonable Data Augmentation (rotations, shifts, noise, etc.) applied consistently to both datasets so we can observe how each model copes with expanded variability. For each classifier, I need precision, recall, and F1-score reported per class and averaged (macro and weighted). Beyond raw numbers, I’m interested in a concise narrative or visual that explains how model complexity—not just depth or number of neighbors but also kernel choice, regularisation, and hidden-layer width—interacts with the distinct characteristics of the two dat...

...scikit-learn, TensorFlow or PyTorch) and will have the freedom to introduce the tools you’re most comfortable with, as long as the final stack is reproducible and easy to maintain. Here’s what I need from you: • Prepare and engineer the time-series dataset so that it’s model-ready, documenting every transformation. • Design, train, and iterate on forecasting or anomaly-detection models that outperform a naive baseline. • Hand over clean, well-commented code—preferably in a notebook plus modular scripts—together with a brief report on feature importance and performance metrics (MAE, RMSE, or other relevant scores). • Package the solution for deployment, for example through a RESTful FastAPI service or a Docker image, and include ...

...Deliver a slide-ready results dashboard and an executive summary that translates findings into actionable recommendations for decision-makers. criteria 1. The notebook/scripts run end-to-end on standard hardware without hidden steps. 2. Diagrams clearly illustrate data ingestion, preprocessing, model training, validation, and deployment/serving. 3. Quantitative results outperform at least one naive baseline, with statistical evidence. 4. The summary shows exactly how insights translate into operational or strategic decisions. Hand-off package: source code, README, diagrams (PNG/SVG), presentation-ready deck, and a brief video walkthrough (optional but welcome). Please provide a portfolio with relevant work....

I am looking for someone to vectorize the important base shell of my logo which unfortunately I was naive to not use vector. The logo that I made belongs to my 3 month old (web) TV station that is broadcasting and doing decent numbers for an underdeveloped program schedle and starter channel. To my best luck, a friend of mine recreated the middle number (8), all I need now is this so that I no longer have to depend on my raster which looks okay as a screenbug, not nearly as great as on the idents. I want the logo to look EXACTLY like the image listed so that it won't look like a logo change. That is not what I want. I want accurate rebuild.

I have already deployed a full Streamlit application that predicts loan approvals in real time (live demo: , source: ). The pipeline currently includes Logistic Regression, K-Nearest Neighbors, and Naive Bayes models with standard scaling and the usual EDA-driven feature engineering. What I want now is a measurable lift in overall model performance, with the F1-score as the guiding metric. Feel free to explore more advanced algorithms (e.g., Gradient Boosting, XGBoost, LightGBM, calibrated ensembles, or even a tuned version of my existing classifiers) as long as they integrate cleanly with the existing Python | Pandas | NumPy | Scikit-learn stack and can be surfaced through the current Streamlit front-end. Key points you should address •

...working optimiser that reproduces those steps in NumPy/SciPy. • EM for a constrained Gaussian Mixture Model – step-by-step derivation of the E and M updates with the specified covariance constraint, plus a clean implementation that converges on synthetic and real data. • Naive Bayes spam classifier – closed-form derivations for the parameter estimates and a vectorised implementation that processes the provided e-mail corpus. Once the above are working, the same dataset will be used to train and compare Naive Bayes, logistic regression, and K-Nearest Neighbours. I need accuracy, precision/recall, ROC where appropriate, and confusion matrices, followed by: • A 2-component PCA projection with each classifier’s decisi...

...Sensitivity/Specificity For Segmentation: Dice Score IoU Generate: Confusion matrix ROC curve Document performance clearly. STEP 8: Treatment Prediction Module Once diagnosis model works: Option A: Feature Extraction Remove last classification layer Extract deep features Option B: Combine with Clinical Data Input: CNN features Age Stage Biomarkers Train: Fully connected neural network OR XGBoost classifier OR Survival regression model Output: Treatment response probability STEP 9: Add Explainability Healthcare requires transparency. Implement: Grad-CAM Attention maps Heatmap overlay on PET image Output: Visual tumor highlight Model attention region STEP 10: Backend API Development Using FastAPI: Endpoint 1: Upload PET scan Endpoint 2: Run inference Endpoin...

I need an experienced computer-vision developer to build a photo-based image classification pipeline using OpenCV. The system will 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. ...

I’m building a product that relies on fast, accurate text 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 st...

...touch—yet remain versatile enough for summer dresses and blouse. Here’s what I’m after: the flowers should be PAINTED BY HAND in gouache, watercolour or a similar medium, then delivered as high-resolution (300 dpi) scans. A transparent background or carefully cropped edges will help my print technician drop them straight into our repeat layouts in Photoshop. The flower motifs should be not too naive. They should be modern and sophisticated. Not too commercial and mainstream. The flower motifs will be used in high end / couture fashion. Not budget fashion. The right designs should be seen on a runway. Final files need to be RGB layered PSD or TIFF so we can tweak colors before sending them off to the mill. I am not interested in computer generated ...

I’m building a unsupervised classifier that learns jointly from audio recordings and accompanying physiological signals. My end-goal is a robust prediction model that can generalise to new subjects, so every modelling choice—from feature pipeline through network architecture and hyper-parameter search—has to be evidence-driven and reproducible. Here is what I already have: raw multichannel wave files, synchronised physiological traces (ECG, EDA and respiration) and a draft protocol for train-test splits. What I still need is the deep-learning firepower to turn this into a working model, coded cleanly in Python with TensorFlow or PyTorch, complete with training scripts, inference wrapper and clear documentation. I’ll share the data dictionary, baseline metri...

...machine-learning model that can automatically flag fraudulent activity. The model must correctly recognise the three problem categories—Phishing, Robocalls and Telemarketing scams—without human intervention. What I expect you to handle: • Pre-processing: clean the audio and extract features (e.g., MFCCs or spectrograms) that capture speaker and content cues. • Modelling: design, train and fine-tune a classifier; CNN, RNN, Transformer or a hybrid approach is acceptable if it improves accuracy. • Evaluation: deliver precision, recall, F1 and a full confusion matrix for each fraud type so I can judge real-world performance. • Deployment assets: an inference script or small REST service that accepts an MP3 file and returns the predicted class wi...

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

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

I have a collection of X-ray studies and I need a robust deep-learning model that can look at each image and instantly tell me which predefined category it belongs to (e.g., chest PA vs. chest lateral, cervical spine, hand, etc.). The job is strictly about classifying the type of X-ray, not diagnosing any pathology. Here is what I already have and what I expect from you: • A curated folder structure with several thousand labelled PNG and DICOM files that you can download from my secure server. • A preference for Python with either PyTorch or TensorFlow/Keras—use whichever framework you feel will achieve the best accuracy and fastest inference on a modern GPU. • Clean, reproducible code (Jupyter notebook or script) plus a short README that explains environment se...

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