BSDA5013 · Knowledge Base
Deep Learning Practice Notes
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Procedures
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208 - PyTorch Fundamentals for Deep Learning Practice
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209 - Data Loading & Pipelines- Dataset, DataLoader, Transformations
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210 - Pandas for Deep Learning- DataFrames, Series, and GPU-Accelerated Data Processing
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211 - Training & Debugging- Gradient Clipping, LR Scheduling, NaN Detection
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212 - Distributed Training- DDP, Gradient Accumulation, and Mixed Precision
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213 - Mixed Precision Training- FP16-FP32, GradScaler, and AMP
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214 - Profiling & Optimization- PyTorch Profiler, FLOPs, Memory
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215 - Model Deployment- ONNX, TensorRT, Quantization, and Pruning
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216 - Experiment Tracking- MLflow, W&B, Metrics Logging, Hyperparameter Sweeps
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