Master anomaly detection for sequential data - from statistical baselines to LSTM autoencoders. Learn why standard methods fail on time series, how to pick thresholds, and how to build production-grade systems that catch real anomalies without drowning your team in false alarms.
Master Long Short-Term Memory and Gated Recurrent Units - the architectures that solved vanishing gradients and powered a decade of sequence modeling breakthroughs.
From vanilla RNNs to production anomaly detectors - how neural networks learn order, memory, and time.
How recurrent neural networks process sequential data through shared hidden states, and why vanishing gradients cripple their ability to learn long-range dependencies.
How encoder-decoder networks with attention solve variable-length sequence-to-sequence problems - from machine translation to summarization and code generation.
Master Temporal Convolutional Networks - causal and dilated convolutions, receptive field math, residual blocks, and when TCNs outperform LSTMs and Transformers in production sequence modeling.
Master the core patterns, classical methods, and deep learning approaches for time series forecasting - including the most critical mistake practitioners make with train/test splits.
