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Deep Learning Practice
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PyTorch Fundamentals for Deep Learning Practice
Deep dive into patterns and axioms.
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Data Loading & Pipelines: Dataset, DataLoader, Transformations
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Pandas for Deep Learning: DataFrames, Series, and GPU-Accelerated Data Processing
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Training & Debugging: Gradient Clipping, LR Scheduling, NaN Detection
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Distributed Training: DDP, Gradient Accumulation, and Mixed Precision
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Mixed Precision Training: FP16/FP32, GradScaler, and AMP
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Profiling & Optimization: PyTorch Profiler, FLOPs, Memory
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Model Deployment: ONNX, TensorRT, Quantization, and Pruning
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Experiment Tracking: MLflow, W&B, Metrics Logging, Hyperparameter Sweeps
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NLP Projects: Tokenization, Fine-Tuning, Text Classification, Seq2Seq
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