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The MNIST Loss Function
The MNIST Loss Function Sigmoid SGD and Mini-Batches The MNIST Loss Function We already have our independent variables x —these are the images themselves. We’ll concatenate t... -
Model Interpretation
Model Interpretation Tree Variance for Prediction Confidence Feature Importance Removing Low-Importance Variables Removing Redundant Features Partial Dependence Data Leakage ... -
Core
Tabular core Initial preprocessing make_date [source] add_datepart [source] add_elapsed_times [source] cont_cat_split [source] df_shrink_dtypes [source] df_shrink ... -
Layers
Layers Basic manipulations and resize module [source] class Identity [source] class Lambda [source] class PartialLambda [source] class Flatten [source] class V... -
The Forward and Backward Passes
The Forward and Backward Passes Defining and Initializing a Layer Gradients and the Backward Pass Sidebar: SymPy End sidebar Refactoring the Model Going to PyTorch The Fo... -
Building a Modern CNN: ResNet
Building a Modern CNN: ResNet Skip Connections A State-of-the-Art ResNet Bottleneck Layers Building a Modern CNN: ResNet We now have all the pieces we need to build the mode... -
What is torch.nn really?
torch.nn 到底是什么？ MNIST数据安装 神经网络从零开始(不使用torch.nn） torch.nn.functional的使用 使用nn.Module进行重构 使用nn.Linear进行重构 使用optim进行重构 使用Dataset进行重构 使用DataLoader进行重构 增加验证集 编写fit()和get_data()... -
torch.nn 到底是什么？
torch.nn 到底是什么？ MNIST数据安装 神经网络从零开始(不使用torch.nn） torch.nn.functional的使用 使用nn.Module进行重构 使用nn.Linear进行重构 使用optim进行重构 使用Dataset进行重构 使用DataLoader进行重构 增加验证集 编写fit()和get_data()函...