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machine-learning

Machine learning models and deployment

ProgrammingGLiNER Alternatives for Russian NER: Slang & Typos

Discover top GLiNER alternatives like Slovnet, Slavic-BERT-NER for named entity recognition in Russian text. Handle slang, typos, abbreviations to extract goods and prices from chats effectively.

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ProgrammingKernel ICA in R: Implementation Guide

Learn how to implement kernel independent component analysis in R using the KernelICA package. Step-by-step guide with code examples.

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CareerProgramming for AI Career: QA Testing as Foundation?

Learn the optimal approach to programming for AI careers. Discover if QA testing provides a viable foundation for transitioning into AI development and build your AI career path.

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ProgrammingAI Systems in 2017-2018: Neural Networks and GPT

Comprehensive overview of AI systems during 2017-2018, focusing on neural networks, GPT architecture, capabilities, and development status of artificial intelligence during this period.

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ProgrammingBest Gliner Alternatives for Russian NER: Slang & Typos

Top Gliner alternatives for Russian NER handling slang, typos, abbreviations. Natasha/Slovnet, spaCy NER, RuBERT extract products & prices from chats efficiently on CPU.

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ProgrammingFeature Engineering for Array-Valued Features in ML

Learn standard approaches for handling array-valued features in traditional ML models through feature engineering techniques like statistical extraction, dimensionality reduction, and feature hashing.

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ProgrammingLLM Fine-Tuning Data Formats, RAG & AI Datasets Guide

Discover data formats for fine-tuning LLMs like CSV/JSONL prompts, RAG query-context triples, and top AI datasets from Hugging Face for training mini LLMs. Essential guide for instruction tuning and RAG systems.

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ProgrammingLearn Data Science: Hands-On Projects, AI & Googling

Yes, hands-on mini-projects, effective Googling, and AI for tasks/debugging outperform syntax memorization in data science learning. Get tips for long-term retention, faster problem-solving, and building a portfolio with real projects like Titanic.

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ProgrammingRegenerate Embeddings on Model Updates?

Discover when to regenerate embeddings after embedding model updates. Trade-offs include accuracy vs. compute cost, downtime. Strategies: incremental re-embedding, dual-indexing, versioning for vector databases and RAG systems.

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ProgrammingFix TF_SessionPRun 'Must Run Setup' Error TensorFlow C API

Resolve TensorFlow C API TF_SessionPRun errors: 'Local rendezvous CANCELLED PRun cancellation' and 'Must run setup before partial runs'. Checklist, code patterns, Windows MinGW tips for efficient streaming inference with Keras SavedModel.

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ProgrammingPyTorch Dataset/DataLoader for Multivariate Time Series

Fix PyTorch Dataset and DataLoader for multivariate time series preprocessing from CSV. Ensure (B, V, L) shapes, avoid data leakage with proper scaling, and validate sliding windows for MAMBA models.

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ProgrammingModeling Stepwise Price Time Series: Change Points & HMM

Forecast stepwise price time series with change point detection, regime-switching (HMM), and survival models. Includes pipeline, libraries, and evaluation tips.

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ProgrammingTensorFlow Backprop: Mixed Real & Complex Functions

Learn how TensorFlow handles backpropagation through mixed real and complex-valued functions using Wirtinger derivatives, GradientTape, and complex-aware ops. Covers chain rule, domain boundaries, and practical tips for CVNNs in comms systems.

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ProgrammingTennis Score Encoding for Machine Learning Models

Learn how to convert tennis score strings like '6-4 6-2' into numerical features for Random Forest models. Step-by-step guide with code examples.

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ProgrammingResolve NotFittedError with StackingClassifier Prefit Models

Learn how to fix sklearn StackingClassifier NotFittedError when using prefit models and pipelines. Complete troubleshooting guide.

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ProgrammingData Leakage in Time Series Regression: Closer Snapshots?

Is training a time series regression model on snapshots closer to departure data leakage when predicting earlier? Learn validation strategies, feature rules, and pitfalls to avoid lookahead bias in forecasting final bookings.

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