PyTorch로 적용하는 딥러닝 (제로 투 마스터리)

This lesson introduces PyTorch tensors as the fundamental data structures for deep learning, emphasizing their role in GPU-accelerated computation and automatic differentiation. Students will learn to manage tensor properties like shape, dtype, and device, while mastering essential manipulation techniques for building and training neural networks.

This lesson introduces the standardized six-pillar PyTorch workflow, providing a repeatable blueprint for building, training, and deploying deep learning models. Students will learn how to manage data preparation, tensor alignment, and the core training loop to ensure robust model performance and generalization.

This lesson explores the necessity of non-linear activation functions, such as ReLU, in deep neural networks to overcome the limitations of linear models when classifying complex, non-linear data. Students will learn how to build and train a PyTorch model capable of forming intricate decision boundaries using hidden layers and appropriate loss functions like Binary Cross Entropy.

This lesson introduces Convolutional Neural Networks (CNNs) as an efficient alternative to fully connected networks for processing high-dimensional image data. Students will learn how CNNs utilize local receptive fields, shared weights, and pooling to achieve parameter efficiency, as well as how to format image data into the required (N, C, H, W) tensor structure for PyTorch.

This lesson explores how to build efficient data pipelines in PyTorch by transitioning from simple toy datasets to managing complex, real-world data. Students learn to decouple data processing by using the Dataset class for individual sample retrieval and transformation, and the DataLoader for optimized, parallelized batch delivery.

This lesson explores the transition from experimental Jupyter Notebooks to production-ready modular Python scripts by emphasizing the importance of the Separation of Concerns principle. Students learn to organize deep learning projects into distinct components—such as data setup, model architecture, and training logic—to improve code reproducibility, testability, and scalability.

This lesson introduces transfer learning as a solution to the high resource demands of deep learning by reusing pre-trained models to achieve high accuracy with limited data. Students learn how to freeze feature extraction layers and adapt the classifier head in PyTorch to effectively apply generalized visual knowledge to specific new tasks.

This lesson explores the necessity of systematic experiment tracking in deep learning to overcome the reproducibility crisis and ensure reliable model development. Students learn how to implement automated tracking for hyperparameters, environment states, and performance metrics to facilitate effective debugging, optimization, and project collaboration.

This lesson focuses on the transition from theoretical research to practical engineering by teaching students how to deconstruct scientific papers into modular, high-performance PyTorch code. You will learn to map complex mathematical architectures like the Vision Transformer into functional components while mastering systematic debugging techniques for tensor shapes and data types.

This lesson covers the transition from exploratory research to production-ready deployment by focusing on refactoring code into modular, stateless services. Students will learn how to optimize models for low-latency inference, ensure reproducibility, and properly export model artifacts using state dictionaries and inference mode.