Tag: Deep Learning
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JAX vs PyTorch: When JIT Speed Actually Matters
JAX's JIT and vmap deliver 3x speedups on numerical loops, but PyTorch wins in production. When each framework actually matters.
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Reward Engineering for Financial and Robotic RL Tasks
Reward shaping doubled our RL agent's success rate. Compare dense vs sparse rewards, risk-adjusted trading, and curriculum learning with benchmarks.
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Stable Baselines3: Tips for Training Robust RL Agents
Train production-ready RL agents with Stable Baselines3: hyperparameter tuning tricks, TensorBoard monitoring, and debugging NaN rewards fast.
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Part 3: Policy Gradient vs. Q-Learning: Choosing the Right Agent
DQN vs PPO vs SAC: when each algorithm wins. Math foundations, code examples, and a decision tree for picking the right RL agent for your task.
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Deep Learning for Algorithmic Trading: From LSTM to Transformers
Compare LSTM vs Transformer for stock prediction: 2-year backtest on S&P 500 with Sharpe ratios. Complete training and signal generation code.
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Physics-Informed Neural Nets for Bearing RUL Prediction
Physics-Informed NNs cut bearing RUL error 30% vs pure deep learning. How Paris' law constraints work when you only have 20 training samples.
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Real-Time Anomaly Detection on Production Lines with DL
Autoencoder vs transformer for production line defects: edge deployment benchmarks reveal why reconstruction error fails 40% of the time.