Deep Learning Notebooks

• 00_course_orientation.ipynb
• 01_synthetic_demand_data_generation_and_validation.ipynb
• 02_forecasting_framing_and_baselines.ipynb
• 03_neural_network_basics_from_scratch.ipynb
• 04_pytorch_mlp_forecaster.ipynb
• 05_optimization_regularization_and_initialization.ipynb
• 06_ml_strategy_splits_and_error_analysis.ipynb
• 07_convolutions_for_temporal_patterns.ipynb
• 08_sequence_models_rnn_gru_lstm.ipynb
• 09_attention_and_transformers.ipynb
• 10_probabilistic_forecasting_and_quantiles.ipynb
• 11_global_models_and_entity_embeddings.ipynb
• 12_hyperparameter_tuning_and_experiments.ipynb
• 13_ralph_loops_for_deep_learning_iteration.ipynb
• 14_interpretability_and_diagnostics.ipynb
• 15_robustness_drift_and_monitoring.ipynb
• 16_system_design_and_deployment_readiness.ipynb
• 17_capstone_senior_readiness_project.ipynb
• 18_sql_feature_engineering_and_data_contracts.ipynb
• 19_experimentation_and_causal_inference_for_promotions.ipynb
• 20_hierarchical_forecasting_and_reconciliation.ipynb
• 21_decision_optimization_inventory_and_business_impact.ipynb
• 22_portfolio_interview_readiness_and_case_defense.ipynb