01Module 6 - Sequences and Time SeriesFrom vanilla RNNs to production anomaly detectors - how neural networks learn order, memory, and time.02RNNs and the Vanishing Gradient ProblemHow recurrent neural networks process sequential data through shared hidden states, and why vanishing gradients cripple their ability to learn long-range dependencies.03LSTM and GRU Deep DiveMaster Long Short-Term Memory and Gated Recurrent Units - the architectures that solved vanishing gradients and powered a decade of sequence modeling breakthroughs.04Seq2Seq and Encoder-Decoder ArchitecturesHow encoder-decoder networks with attention solve variable-length sequence-to-sequence problems - from machine translation to summarization and code generation.05Time Series Forecasting PatternsMaster the core patterns, classical methods, and deep learning approaches for time series forecasting - including the most critical mistake practitioners make with train/test splits.06Temporal Convolutional Networks (TCNs)Master Temporal Convolutional Networks - causal and dilated convolutions, receptive field math, residual blocks, and when TCNs outperform LSTMs and Transformers in production sequence modeling.07Anomaly Detection in SequencesMaster 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.
