9 docs tagged with "ml-with-python"

Use the HuggingFace ecosystem end-to-end - transformers, datasets, Trainer API, PEFT/LoRA for efficient fine-tuning, the Hub for sharing models, and tokenizer internals.

Master the complete ML Python stack - NumPy, Pandas, scikit-learn, PyTorch, HuggingFace, and Weights & Biases - the tools every ML engineer uses every day.

Master NumPy for machine learning - broadcasting, vectorization, linear algebra, memory layout, einsum, and the performance patterns every ML engineer needs.

Pandas for machine learning engineers - DataFrame operations, missing data, groupby feature aggregation, time series, memory optimization, and building leakage-free feature matrices.

Build custom PyTorch Datasets and high-performance DataLoaders - batching, num_workers, pin_memory, samplers, WebDataset for streaming, custom collate_fn, and profiling.

PyTorch fundamentals for ML engineers - tensors, autograd, nn.Module, device management, reproducibility, mixed precision training, and the computation graph that makes debugging natural.

Write production-grade PyTorch training loops - learning rate scheduling, gradient accumulation, mixed precision, checkpointing, early stopping, and debugging.

Build production-grade scikit-learn Pipelines - ColumnTransformer, custom transformers, caching, cross-validation without leakage, hyperparameter search, and model serialization.

How W&B's experiment tracking, hyperparameter sweeps, model registry, and artifact management transform chaotic Jupyter notebooks into reproducible, collaborative ML workflows.