Tag: deep-learning
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Self-Attention from Scratch: NumPy vs PyTorch Implementation
Build self-attention from scratch using NumPy and PyTorch. Compare implementations, understand matrix operations, and master transformer architecture.
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From Q-Learning to DQN: Your First RL Algorithms
Implement Q-Learning from scratch, then scale up to Deep Q-Networks with experience replay and target networks. Complete PyTorch code included.
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Gold Price Prediction: Jupyter Notebook to REST API
Build production gold price API with model versioning, drift detection, and monitoring. From Jupyter notebook to deployment in 7 steps.
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LSTM vs Transformer for Gold Forecasting: Who Wins?
LSTM vs Transformer for gold forecasting: both hit 54% direction accuracy. Benchmark reveals why neither architecture beats the baseline.
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ARIMA vs LSTM on Gold Prices: 7-Day vs 30-Day Forecast
ARIMA beats LSTM on 7-day gold forecasts but collapses at 30 days. Head-to-head comparison shows when each model fails and exactly why.
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Attention Is All You Need: Transformer Architecture Review
Attention Is All You Need dismantled: why self-attention replaced RNNs, how positional encoding works, and the 3 tricks that made it scale.