TildAlice – Tech Blog for Developers
Python, AI, Deep Learning, Data Analysis, Computer Vision, Reinforcement Learning โ tips and tutorials
Latest Posts
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Spectral Kurtosis for Bearing Faults: Outperforming FFT
Spectral kurtosis caught bearing failure 3 weeks before FFT showed symptoms. When it works, when it fails, and what runs in production.
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PaddleOCR vs EasyOCR vs Tesseract: Why PaddleOCR Is Slower
Why is PaddleOCR slower than EasyOCR? We benchmark init time, inference speed, model size, and accuracy across PP-OCRv5, EasyOCR, and Tesseract on 10K images.
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Kalman vs Particle Filter for Bearing State Estimation
Kalman vs particle filter for bearing state estimation: real latency numbers on edge hardware, tuning tricks, and when each one breaks down.
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RAFT vs FlowFormer Optical Flow: Speed, Accuracy, and Real Benchmarks
FlowFormer beats RAFT on Sintel by 15% but runs 3x slower. Real optical flow benchmarks with inference time, EPE, and optimization tricks.
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RLHF Review: InstructGPT and Human Feedback Training
How InstructGPT's 1.3B model with RLHF beats 175B GPT-3. Full breakdown of SFT, reward modeling, and PPO training โ with the catches they skip.
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Stock Time Series: Why ARIMA Failed and LSTM Barely Worked
ARIMA just repeats yesterday's price. LSTM hits 52% directional accuracy. Why stock forecasting tutorials hide the real success rates.
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XAI in Practice: SHAP and LIME for Model Interpretation
SHAP vs LIME for XGBoost: when game theory beats local approximations. Interactive tutorial reveals what drives your model's predictions.
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Part 3: Handling Imbalanced Data: SMOTE, ADASYN, and Beyond
SMOTE vs ADASYN vs cost-sensitive learning on Kaggle fraud data. Benchmark shows which method wins at precision, recall, and edge cases.