面向深度学习的 Python 面向对象教程:从实例到工程 | Python OOP for Deep Learning — 填补 D2L(不讲OOP)与 Fluent Python(不讲DL)之间的空白,从手写 nn.Module 到从零实现 Transformer
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Updated
Sep 5, 2026
面向深度学习的 Python 面向对象教程:从实例到工程 | Python OOP for Deep Learning — 填补 D2L(不讲OOP)与 Fluent Python(不讲DL)之间的空白,从手写 nn.Module 到从零实现 Transformer
NNBuilder is a lightweight, chainable utility class built on top of PyTorch’s `nn.Module`, designed to simplify the construction of sequential, fully‑connected neural networks.
Binary classification of breast cancer using PyTorch. Used StandardScaler, LabelEncoder, Dataset, DataLoader, custom nn.Module model, BCELoss, and SGD. Focused on implementing a complete training pipeline, not optimizing accuracy.
A collection of PyTorch basics and experiments covering tensors, autograd, and neural network components. Ideal for beginners exploring deep learning with hands-on examples.
Two-layer neural network for MNIST digit recognition, implemented in PyTorch (nn.Module + autograd).
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