Bertpekerjaan

The project centres on...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 prediction Trained embeddings (e.g., BERT, RoBERTa) are acceptable, but if you opt for lighter models please ensure comparable performance. Provide any custom preprocessing utilities you write. When you reply, focus on your experience with similar NLP or text-classification projects—model choices you have implemented, datasets you have handled, and any production dep...

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

...still standing in the way of real-world deployment. Here’s what I need from you: a well-structured document (roughly 4,000–6,000 words, though quality trumps length) that pulls together peer-reviewed studies, recent conference papers, and reputable industry white-papers published within the last three years. Please weave in clear explanations of transformer-based architectures, the progression from BERT to large language models, and any notable advances in multilingual or low-resource scenarios. A short section on responsible AI—bias, fairness, data privacy—will round out the picture. Deliverables • A Word or Google Docs report with section headings, figures or tables where useful, and APA-formatted citations • A concise executive summary (...

...is a clean JSON output per record, so each review comes back with its summary and sentiment label in a machine-readable format. Because the language is highly nuanced, I’d like you to blend both rule-based and machine-learning techniques: think lexicon cues for idiomatic Telugu alongside a fine-tuned transformer or any other classifier that lifts accuracy. Feel free to draw on pretrained Telugu-BERT, FastText, spaCy, custom dictionaries—whatever combination you believe delivers the most reliable hybrid model. Deliverables • Python or notebook script that ingests raw Telugu text and produces the JSON format • Trained model files (and any custom lexicons) with version control • README explaining setup, dependencies, and how to retrain or update th...

Elimde metin tabanlı bir yapay zeka çalışması var ve doğal dil işleme tarafında iki çekirdek ihtiyacım bulunuyor: metin sınıflandırma ve dil modeli eğitimi. Neler yapılacak? • Veri ön işleme: Ham metinlerin temizlenmesi, tokenizasyon ve olası veri artırma adımları • Metin sınıflandırma: BERT, RoBERTa gibi hazır modelleri ince ayar yapmak ya da sıfırdan hafif bir mimari kurmak • Dil modeli eğitimi: İhtiyaca göre yeni bir model eğitmek veya mevcut büyük bir modeli yeniden eğitmek • Eğitim-değerlendirme kodu: Python ortamında, tercihen PyTorch veya TensorFlow kullanarak yeniden üretilebilir script’ler • Performans raporu: Accuracy, precision, recall ve F1 değerlerini içeren özet doküman &...

...candidate will manage both the ongoing support and development initiatives, ensuring seamless execution across teams, alignment with client objectives, and timely delivery of high-quality results. This is a dynamic role that requires strong leadership, technical knowledge, and a hands-on approach to problem-solving. Key Responsibilities: Project Ownership: Own and manage all aspects of the UgMO and BERT platforms, including development, maintenance, and optimization efforts. Cross-Functional Coordination: Work with the development, QA, DevOps, and support teams to ensure smooth execution of tasks and clear communication across all stakeholders. Resource Management: Manage both onshore and offshore teams, ensuring that the right resources are allocated to the right tasks in b...

I’m putting together a lean Retrieval-Augmented-Generation web application that lets users type a natural-language query, semantically searches a private document collection, then returns ranked matches and an auto-generated answer. Everything must stay fully open-source. Core stack • Model: BERT (Hugging Face), fine-tuned or out-of-the-box for text embeddings. • Vector store: FAISS, Milvus, Weaviate or a similarly licence-friendly alternative. • Back-end: Python with FastAPI (or Flask) and PyTorch/Sentence-Transformers. • Front-end: a minimal HTML/React page that submits a query and shows results + generated summary. Scope of work 1. Set up the pipeline that ingests text files/markdown/PDF, chunks them, builds the vector index, and stores met...

End to end help in research paper, until i publish it its related to artificial inteliigence and chatgpt and bert . How to start it what do i write, what is the format, how do we publish it and then how can i go to other workshops and events and conferences for it . i can increase the bidding amount kindly let me know

...pipeline, including: How the model is trained/fine-tuned (if applicable) How the ONNX model is generated and integrated How to update the NVD/CPE data and re-run the matching Optional: A small evaluation report or script that: Measures matching quality on a sample set Shows examples of correct/incorrect matches for validation Required Skills Strong experience with NLP and text similarity (BERT/DistilBERT or similar models) Experience exporting models to ONNX Good knowledge of PowerShell scripting Familiarity with NIST NVD / CPE / CVE data structures Experience with fuzzy matching and string normalization Work needs to be done in the next 9hours, please provide a poc so that we could move on fast....

...list, 2) an ONNX-based embedding inference example and a short PowerShell call snippet demonstrating how to invoke ONNX scoring from , 3) a README explaining preprocessing rules, alias dictionary examples, scoring formula, and how to refresh NVD data, and 4) a short evaluation CSV with example inputs, predicted CPE(s) and confidence. Mandatory qualifications: strong experience with BERT-style embeddings, ONNX export and inference, fuzzy string matching, familiarity with NIST NVD/CPE formats, and PowerShell integration experience. Please include a one-paragraph summary of a similar project you delivered, the core technical challenge you faced and how you solved it, estimated time to complete this 9-hour PoC, and any assumptions you will make. Attachments and reference materials

Preciso transformar a inteligência do BERT em painéis claros que mostrem o andamento de vários projetos. Já descartei etapas de classificação ou análise isolada; meu foco é, exclusivamente, a visualização interativa em formato de dashboards. A ideia é conectar o modelo BERT aos meus conjuntos de dados de gestão de projetos, processá-los e exibir informações dinâmicas — por exemplo, progresso, recursos e prazos — em um painel web responsivo. Estou aberto a sugestões de layout e de ferramentas (Python, Streamlit, Dash, Power BI, Tableau ou similares), desde que o resultado seja intuitivo e fácil de atualizar a partir de arquivos CSV ou de um...

...organizations with an easy-to-use dashboard that displays risk trends, alerts, and daily check-in assessments, ultimately reducing emergency visits, improving patient outcomes, and contributing to the U.S. mental-healthcare infrastructure. Core Features: No EEG, no facial recognition, no complex fusion — only 2 AI modules: 1.Text-Based Mental Health NLP Screener: User types daily mood notes. Model (BERT/RoBERTa small) detects.. stress anxiety hopelessness négative pattern Tone Stress Detector (Optional, simple) Record 5–10 seconds audio Analyze pitch, energy, pauses Output: Low / Medium / High Stress Score Generator: Combine Text Score + Voice Score Output: Low(Green) Moderate(Yellow) High(Red) Dashboard: Shows daily risk level Trend graph (7&ndash...

...Clean and embed text from news using transformer models: FinBERT, Llama-3, or GPT-4-turbo for finance-specific tone. Create features: Sentiment score Price momentum Volume spikes Time of day, volatility index (VIX if stocks) Step 3 — Model Training Use ensemble models: Short-term signals: RandomForest / XGBoost Pattern recognition: LSTM / Temporal Convolutional Network Sentiment fusion: BERT or FinBERT embeddings + numerical data Train, validate, and test on different market phases (bull, bear, sideways). Step 4 — Backtesting Simulate trades using your signal logic: Entry after >2% move with confirmation Dynamic stop-loss (ATR or volatility-based) Measure: Win rate, Sharpe ratio, max drawdown Step 5 — Automation Build the bot in Python: Li...

I’m building a service that pulls breaking stories from Times of India, NDTV, and BBC through their public APIs, runs each headline and body through a fine-tuned BERT model, and instantly tells the reader whether the article is genuine or fake. Every prediction must be accompanied by a SHAP explanation so users can see which phrases drove the decision and how confident the model is. Here’s the flow I have in mind: • A lightweight Python pipeline (Transformers + PyTorch) polls the three APIs, normalises the text, then feeds it to the classifier. • The classifier is a BERT base model fine-tuned specifically on a labelled fake-news dataset; the training notebook, scripts, and final .pt/.bin weights are part of the hand-over. • SHAP values are cal...

Data: I will provide two curated Turkish corpora in plain text: one human-written, one LLM-generated. Goal: Build a transformer-based binary classifier that predicts whether any Turkish passage is AI-generated or human-written with high accuracy. It needs to be evaluated sentence by sentence. Scope • Use Transformers (e.g., BERT, RoBERTa, DeBERTa or a custom Turkish-specific variant) or alternative. • Clean, split and balance the provided datasets, then fine-tune or train the model end-to-end. • Optimise for high F1 and accuracy; include validation metrics and a brief error analysis. • Provide moderate explainability: probability scores plus one or two key attention/feature insights per prediction—enough for me to understand why the model leans huma...

...through Friday. • Follow a short online training that explains our rubric and common edge cases. • Evaluate responses with a focus on quality assessment; you may occasionally flag bias or factual errors when you spot them. • Enter clear, concise feedback so we can refine future model updates. What I’m looking for • Some prior experience using or evaluating AI/LLM systems—if you have worked with BERT-based models or similar, that is a plus. • Consistent internet connection and your own laptop or desktop. • Ability to stay objective while reading a high volume of text. Payment The role is paid at $16 per hour, issued weekly through Freelancer’s tracker. If this sounds like a fit, send a brief note outlining your AI experi...

...Summary: The candidate will build NLP pipelines, train transformer models, and process unstructured text data to deliver intelligent solutions for clients in tech, security, and communications. Responsibilities: Build NLP pipelines using Python (spaCy, Hugging Face Transformers, NLTK). Perform sentiment analysis, entity recognition, and topic modeling. Train and fine-tune transformer models (BERT, GPT, RoBERTa). Manage and clean text datasets using Pandas and Regex. Deploy NLP models via FastAPI or Flask. Requirements: 3+ years of experience in NLP, data science, or machine learning. Expertise in Python and NLP libraries. Understanding of deep learning architectures and vector embeddings. Experience with PyTorch or TensorFlow. Preferred Skills: Familiarity with LangC...

I am working with the VUA20 dataset on Hugging Face and have a baseline metaphor-detection model in place. I now want to refine that system, turn it into a stronger paraphrase-aware model, and benchmark it against two large-language-model backbones—BERT and RoBERTa. The work I need completed is three-fold: 1. Data & Model
 • Re-train or fine-tune BERT and RoBERTa on the VUA20.
 • Integrate a paraphrasing component so the model not only flags a metaphor but can restate the sentence in literal form. 2. Explanation Layer
 • For every detected metaphor, generate a concise textual explanation describing why the phrase is figurative and how the paraphrase conveys the literal meaning. 3. Evaluation & Comparison
 • Report ...

...Clean and embed text from news using transformer models: FinBERT, Llama-3, or GPT-5-turbo for finance-specific tone. Create features: Sentiment score Price momentum Volume spikes Time of day, volatility index (VIX if stocks) Step 3 — Model Training Use ensemble models: Short-term signals: RandomForest / XGBoost Pattern recognition: LSTM / Temporal Convolutional Network Sentiment fusion: BERT or FinBERT embeddings + numerical data Train, validate, and test on different market phases (bull, bear, sideways). Step 4 — Backtesting Simulate trades using your signal logic: Measure: Win rate, Sharpe ratio, max drawdown Step 5 — Automation Build the bot in Python: Libraries: ccxt (for exchanges), backtrader, pandas, TA-Lib Schedule checks every few ...

...aggregator that fetches stories from leading Indian newspapers and TV channel feeds, removes duplicates, and converts each article into crisp bullet-point summaries. The first release must speak to readers in Hindi, English and Gujarati, with the codebase structured so I can plug in more Indian languages later. Key experience I’m looking for includes NLP summarisation (transformer models, GPT, BERT or similar), language detection, and high-quality text-to-speech. The read-aloud feature should trigger from any article screen and play flawlessly for visually-impaired users on slow connections as well. The product has to feel native on iOS and Android and remain fully functional on the web; whether you choose React Native, Flutter or a well-organised trio of Swift, Kotlin ...

I already have the dataset and experimental results for my research project on deepfake detection. I now need a skilled academic/research writer to w...ethical considerations, dataset contribution, and limitations Ensure originality (no plagiarism) and academic tone suitable for submission to journals in AI, NLP, or data science Ideal Freelancer: Background in Computer Science / AI / NLP / Data Science Experience writing or co-authoring academic papers (conference or journal level) Knowledge of text generation and classification models (e.g., GPT, BERT, Hugging Face) Able to interpret experimental results and discuss implications (Optional) Familiarity with Arabic language or Arabic NLP research Deliverables Full paper draft (Word) ready for submission Editable figures, tables, and ...

...Technical Requirements **Frontend:** React.js with Tailwind CSS (existing codebase to be enhanced) **Backend:** Node.js (Express) or Python (FastAPI/Django) **Database:** PostgreSQL or MySQL **Web Scraping:** Scrapy, BeautifulSoup, Puppeteer **APIs:** LinkedIn, Glassdoor, Crunchbase, OpenCorporates, Job Boards (Indeed, LinkedIn Jobs) **AI/ML:** NLP models, XGBoost or Transformer-based models (BERT) **Automation:** Google Sheets API, Puppeteer/Selenium for screenshots **Cloud Storage:** AWS S3 (if needed) ----- ## Key Deliverables **Phase 1:** - Enhanced web application with accurate subdomain scanning - Improved AI ranking system (L1-L5) with detailed evidence - Gap analysis with specific improvement recommendations - Working frontend interface for domain input and an...

... • No unified data pipeline, no MLOps layer, and no clear roadmap tying research spikes to shippable features. Scope of work • Audit the existing repos, datasets, and annotation processes, then define a clean, version-controlled baseline. • Design an integrated architecture that supports multi-modal learning (images + text). • Select or fine-tune state-of-the-art models—e.g., ViTs, CLIP, BERT, or transformer hybrids—balancing accuracy with inference speed. • Set up automated training, testing, and deployment workflows using tools such as TensorFlow/PyTorch, Docker, and a CI/CD stack (GitHub Actions, Kubeflow, or similar). • Establish data governance, monitoring, and retraining triggers so the system stays reliable in production...

...features Audio → Wav2Vec 2.0 for speech features Text → BERT for contextual embeddings Design and implement a Cross-Modal Transformer with cross-attention for fusion of modalities. Integrate a meta-learning framework (MAML/Reptile) for few-shot adaptation. Preprocess datasets (IEMOCAP, CMU-MOSEI, MELD, etc.) and handle data imbalance. Optimize model for real-time and efficient processing (lightweight models, pruning, frame selection). Evaluate performance using metrics like Accuracy, F1-score, and Confusion Matrix. Document the implementation with clear explanation of methodology, results, and novelty. Required Skills: Strong knowledge of Deep Learning (PyTorch, TensorFlow preferred) Experience with Transformer architectures (ViT, BERT, Wav2Vec 2.0) Familiarity w...

Hola! Estoy realizando mi tesis y necesito revisión, consejo y ajustes a un modelo técnico que está dentro del trabajo. Estoy construyendo un proyecto de análisis de sentimiento aplicado a ventas B2B SaaS. ...aplicado a ventas B2B SaaS. El objetivo es predecir si una respuesta por mail de un prospecto es positiva, negativa o neutral, y a futuro estimar la probabilidad de que derive en una reunión o avance comercial. Esto permite a un equipo comercial automatizar la lectura de respuestas de prospectos y priorizar rápidamente cuáles merecen atención inmediata. El proyecto combina datasets sintéticos, BERT en español, y entrenamiento supervisado para crear un clasificador de sentimiento en respuestas de clientes po...

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...pip-tools) • Dockerfile or docker-compose for one-command spin-up • Configuration guide: how to adjust disagreement thresholds, add/remove models, and switch storage back-ends (PostgreSQL or Parquet) • README explaining end-to-end workflow and a quick demo on a sample dataset Acceptance criteria • On a provided benchmark set the pipeline achieves at least the same macro-F1 as a single baseline BERT model within three active-learning iterations • New data dropped into a watch folder is labeled automatically within one minute and the result appears in the output store • All steps reproducible on a vanilla Ubuntu 22.04 instance with Docker installed only If you have prior experience wiring active-learning loops for text — especia...

...cross-validation, and hyperparameter tuning Clustering algorithms: K-Means, DBSCAN Dimensionality Reduction: PCA, t-SNE Association Rule Mining Anomaly Detection Introduction to Neural Networks with TensorFlow/PyTorch Computer Vision with Convolutional Neural Networks (CNNs) Natural Language Processing (NLP) with Recurrent Neural Networks (RNNs) & LSTMs Introduction to Transformer models (e.g., BERT) Introduction to SQL and NoSQL databases Working with Big Data using Spark Model deployment and serving (Flask/FastAPI) Containerization with Docker CI/CD for Machine Learning I already understand the basics; what I need now is structured, project-driven instruction that goes deep into complex data wrangling, exploratory analysis, statistical testing, and clear presentation of re...

...transcribed, analyzed, and answered empathetically. Key Features: Emotion Detection: Uses BERT-based sentiment analysis (nlptown/bert-base-multilingual-uncased-sentiment). Confidence Visualization: Shows model confidence in predictions. Semantic Similarity: Retrieves up to 3 similar texts from the dataset using SentenceTransformer embeddings and FAISS. Empathetic Responses: Synthesizes answers from similar texts to provide emotional support. Audio Support: Upload or record audio; converts speech to text, predicts emotion, and generates responses with TTS. Professional UI/UX: Centered layout, highlighted keywords, and bullet-pointed outputs. Technical Details: Models: Sentiment: nlptown/bert-base-multilingual-uncased-sentiment Embeddings: all-MiniLM-L6-v2 ...

...is transcribed, analyzed, and answered empathetically. Key Features: Emotion Detection: Uses BERT-based sentiment analysis (nlptown/bert-base-multilingual-uncased-sentiment). Confidence Visualization: Shows model confidence in predictions. Semantic Similarity: Retrieves up to 3 similar texts from the dataset using SentenceTransformer embeddings and FAISS. Empathetic Responses: Synthesizes answers from similar texts to provide emotional support. Audio Support: Upload or record audio; converts speech to text, predicts emotion, and generates responses with TTS. Professional UI/UX: Centered layout, highlighted keywords, and bullet-pointed outputs. Technical Details: Models: Sentiment: nlptown/bert-base-multilingual-uncased-sentiment Embeddings: all-MiniLM-L6-v2 Spe...

...materiales eléctricos (SKU, unidad, cantidad). Generar una lista de materiales exportable en Excel o PDF. Exponer todo como un backend con API (preferiblemente Django REST) que pueda conectarse a un frontend Vue.js (ya existe). # Stack de referencia Backend: Django REST Framework Frontend: Vue.js (ya implementado) Visión por Computadora: YOLOv8, OpenCV OCR: Tesseract OCR o EasyOCR NLP: spaCy, BERT (Transformers) Almacenamiento: PostgreSQL Exportación: Excel - Obligatorio: Experiencia comprobada en proyectos de: Detección de elementos en planos (iconografía técnica o blueprint analysis). OCR con planos escaneados (Tesseract, EasyOCR). Visión por computadora: YOLOv8, OpenCV, PyTorch. NLP para extracción de entidad...

...features—prompt-engineering, embeddings, or fine-tuning—whenever a business unit has a new requirement. I build mainly in Python, so your code, tests, and tooling should follow that ecosystem. Familiarity with FastAPI, Docker, and Kubernetes will help because they are already part of our pipeline. While Azure OpenAI is our primary platform, the ability to draw on other families such as native GPT-3/4, BERT, or T5 when the use-case demands would be a plus. Deliverables (acceptance criteria) – A production-ready LLM service running on our internal infrastructure, callable from our existing apps. – RAG including NLP Queries with SQL for adding insights to data by asking questions – Clean, well-commented Python code, unit tests, and step-by-step deploy...

...features—prompt-engineering, embeddings, or fine-tuning—whenever a business unit has a new requirement. I build mainly in Python, so your code, tests, and tooling should follow that ecosystem. Familiarity with FastAPI, Docker, and Kubernetes will help because they are already part of our pipeline. While Azure OpenAI is our primary platform, the ability to draw on other families such as native GPT-3/4, BERT, or T5 when the use-case demands would be a plus. Deliverables (acceptance criteria) – A production-ready LLM service running on our internal infrastructure, callable from our existing apps. – RAG including NLP Queries with SQL for adding insights to data by asking questions – Clean, well-commented Python code, unit tests, and step-by-step...

I need an experienced AI developer to build a complete offline setup of LLM models using open-source AI. The system should be capable of text generation, image recognition, and data analysis. Key Requirements: - Integrate and deploy models like GPT-3, BERT, and YOLO - Custom fine-tuning of models based on specific needs - Ensure the entire system operates offline Ideal Skills and Experience: - Proficiency in AI model deployment - Experience with GPT-3, BERT, and YOLO - Strong background in model fine-tuning and customization - Ability to set up and manage offline AI environments Please provide examples of similar work done.

...offering to develop custom NLP solutions tailored to your needs, leveraging my experience in building sentiment and emotion analysis systems using machine learning and BERT. My team and I specialize in creating text-based AI tools, and we can provide real-time predictions through a Gradio interface. Key Requirements: - Develop text-based AI tools for analyzing social media posts - Focus on Twitter as the primary platform for data analysis - Ensure real-time prediction capabilities via a user-friendly interface Ideal Skills and Experience: - Expertise in natural language processing and machine learning - Proficiency with BERT and other advanced NLP models - Experience in developing real-time data analysis tools - Familiarity with Gradio for creating interactive interfaces...

...unstructured and semi-structured documents. You will work on developing production-grade models and pipelines for: • Named Entity Recognition (NER) using LLMs and transformers • Generative AI applications over structured/tabular data • Document layout analysis using ML, OCR, and transformer-based layout models Key Responsibilities: 1. Named Entity Recognition (NER) • Design and fine-tune models (e.g., BERT, LayoutLM, FLAN-T5, GPT) for domain-specific NER tasks • Build prompt-engineered and/or few-shot NER systems using LLMs (OpenAI, Claude, Mistral, etc.) • Implement evaluation frameworks for entity-level accuracy, precision, recall, F1-score 2. Generative AI with Structured Data • Design structured-to-natural language generation systems (e.g., ...

...using the BERT vectorization method. This data will be trained for generating responses. The second API will be for a chatbot that retrieves the nearest vector and creates conversational responses using different prompts. I need flexibility in the prompts to tailor the chatbot's responses. Key Requirements: - API 1: Upload and process PDFs, videos, and images with OCR capabilities. - Convert uploaded documents to vectors using BERT. - API 2: Retrieve nearest vectors and generate conversational chatbot responses. - Ability to use different prompts for varied responses. Ideal Skills and Experience: - Experience with API development and integration. - Proficiency in OCR technologies and handling various data formats. - Strong understanding of vectorization methods, sp...

...understanding images using advanced techniques in natural language processing and computer vision. Key Requirements: - Develop a VQA system focused on real-world images. - Implement object detection and image analysis using CNN, LSTM, BERT, and Transformers. - The system should handle all types of questions, including color, count, and descriptive queries. - Ensure accurate and efficient processing of visual and textual data. Ideal Skills and Experience: - Strong background in computer vision and natural language processing. - Proficiency in using CNN, LSTM, BERT, and Transformer models. - Experience with object detection and image analysis in real-world scenarios. - Ability to integrate visual and textual data for comprehensive question answering. I am eager to collabo...

I'm looking for an experienced developer to create a script for a transformer model focused on technical writing in the technology domain. Requirements: - The model should generate high-quality technical articles and reports. - Familiarity with transformer models (e.g., GPT, BERT). - Proficiency in Python and libraries like TensorFlow or PyTorch. - Ability to preprocess and fine-tune on a technology-specific dataset. - Experience in natural language processing (NLP) and text generation tasks. Ideal Skills: - Strong background in machine learning. - Previous work with text generation models. - Good understanding of technical writing standards. - Ability to deliver well-documented and maintainable code. Please provide samples of similar work done.

I'm seeking assistance to implement an existing natural language processing model for my final year project, specifically in the domain of sentiment analysis. Requirements: - Expertise in deep learning and NLP - Experience with frameworks like TensorFlow or PyTorch - Familiarity with sentiment analysis techniques and datasets - Ability to document the implementation process and resul...sentiment analysis. Requirements: - Expertise in deep learning and NLP - Experience with frameworks like TensorFlow or PyTorch - Familiarity with sentiment analysis techniques and datasets - Ability to document the implementation process and results Ideal Skills: - Strong programming skills in Python - Background in data preprocessing and model evaluation - Knowledge of current NLP models (e.g., ...

from the info in chat a layout and wiring diagram

I'm a beginner in deep learning looking for a tutor. I want to understand concepts and work on practical projects. Key Areas: BERT CNN BiLSTM Project Focus: Security Ideal Skills: Strong deep learning background Experience with BERT, CNN, BiLSTM Knowledge in security applications Experience with extracting and preparing data from GitHub repositories

dibutuhkan seorang programer untuk membuatkan sebuah program machine learning dengan bahasa python untuk klasifikasi aspek dan sentimen dengan ketentuan berikut: 1. Gunakan Bahasa Python pada Google Colabs 2. Spliting data menggunakan k-fold CV 3. Pembuatan Model dengan IndoBERT 4. Melakukan Klasifikasi Aspek dan Sentimen 5. Melakukan Pengujian dan Evaluasi

...Cross-Platform Applications: - Web Application (React.js): Advanced dashboards, admin panel, charting tools, and live AI chat assistant. - Mobile Application (Flutter): Feature parity with the web app, real-time alerts, RTL support for Arabic. - Desktop Application (): Offline access, local caching, and trading integrations. 2. AI-Driven Capabilities: - Predictive models (LSTM, BERT) for stock price forecasting. - Sentiment analysis of financial news and social media data. - Portfolio optimization tools with AI-generated recommendations. 3. User-Centric Features: - Multilingual support with Arabic localization. - Customizable dashboards and alerts. - Educational resources for traders. 4. Backend Infrastructure & Documentation: - Scalable backe...

...Generative Pre-trained Transformer (GPT) is a real plus. * Speak Portuguese and/or French. Technical Knowledge * Mandatory: Architecture design, C# .NET, Azure PaaS offerings (APIM, Functions, Web Apps, Storage), Azure Cognitive Services (Azure AI search, OCR, or Vision...), Azure DevOps Pipelines or equivalent. * Nice to have: Azure OpenAI, Azure Data Factory (ADF), Generative AI LLMs (GPT-4, BERT, LLaMA, Mistral) * Certifications: (AZ-900, AZ-204, AZ-300, AZ-400, DP-900, AI-102…)....

...theoretical underpinnings and practical deployment aspects of modern AI systems. You will help develop high-quality technical content, organise mathematical formulations, and design academic illustrations that align with IEEE/Elsevier standards. Key Responsibilities: Writing technically accurate and original academic content on: • CNN architectures and optimisation • Transformer models (e.g., BERT, Vision Transformers) • ML model deployment on cloud platforms (AWS, GCP, Azure) • Assisting with mathematical equations and proofs relevant to the paper. • Creating clear, publication-ready diagrams and visualisations (model architecture, flowcharts, etc.). • Formatting the document using LaTeX or Overleaf to meet journal/conference standards. &b...

...materiales eléctricos (SKU, unidad, cantidad). Generar una lista de materiales exportable en Excel o PDF. Exponer todo como un backend con API (preferiblemente Django REST) que pueda conectarse a un frontend Vue.js (ya existe). # Stack de referencia Backend: Django REST Framework Frontend: Vue.js (ya implementado) Visión por Computadora: YOLOv8, OpenCV OCR: Tesseract OCR o EasyOCR NLP: spaCy, BERT (Transformers) Almacenamiento: PostgreSQL Exportación: Excel - Obligatorio: Experiencia comprobada en proyectos de: Detección de elementos en planos (iconografía técnica o blueprint analysis). OCR con planos escaneados (Tesseract, EasyOCR). Visión por computadora: YOLOv8, OpenCV, PyTorch. NLP para extracción de entidad...

...materiales eléctricos (SKU, unidad, cantidad). Generar una lista de materiales exportable en Excel o PDF. Exponer todo como un backend con API (preferiblemente Django REST) que pueda conectarse a un frontend Vue.js (ya existe). # Stack de referencia Backend: Django REST Framework Frontend: Vue.js (ya implementado) Visión por Computadora: YOLOv8, OpenCV OCR: Tesseract OCR o EasyOCR NLP: spaCy, BERT (Transformers) Almacenamiento: PostgreSQL Exportación: Excel - Obligatorio: Experiencia comprobada en proyectos de: Detección de elementos en planos (iconografía técnica o blueprint analysis). OCR con planos escaneados (Tesseract, EasyOCR). Visión por computadora: YOLOv8, OpenCV, PyTorch. NLP para extracción de entidad...

...schedules, hotel inventories, pricing, etc. ________________________________________ PHASE 4: AI INTEGRATION 1. AI Features to Build: Feature AI Component Tech Stack Chatbot NLP + Travel context OpenAI API, Rasa, LangChain Dynamic Pricing Predictive Modeling Python, Scikit-learn Personalized Recommendations Collaborative Filtering TensorFlow or PyTorch Smart Search Semantic search / NLP Elasticsearch + BERT Fraud Detection Anomaly Detection PyOD, Isolation Forest Language Translation NMT (Neural Machine Translation) OpenAI, Google Cloud Translation ________________________________________ PHASE 5: KEY COMPONENTS TO DEVELOP 1. Flight Booking Engine • Auto-suggest for airports • Date filters, multi-city support • Live price feed • AI-predicted best time to boo...

...storage (AWS S3, Google Cloud Storage) or a database (MongoDB, PostgreSQL). 2.4. Schedule data collection Use cron jobs, AWS Lambda, or Google Cloud Scheduler to automate fetching (daily/hourly). 3. Implement Trend Analysis Micro-steps: 3.1. Preprocess data Clean text: remove spam, duplicates, irrelevant info using spaCy or NLTK. 3.2. Extract keywords and topics Use NLP models like GPT, BERT, or RAKE for keyphrase mining. 3.3. Detect trending keywords Apply Time Series analysis and spike detection algorithms (Prophet, custom Python scripts). 3.4. Conduct sentiment analysis Use VADER, TextBlob, or GPT fine-tuned models. 3.5. Rank trends Calculate trend velocity, engagement, and sentiment scores. 3.6. Automate analysis pipeline Use Airflow or Prefect s...