Deep Learning with PyTorch Glossary

A

• Activation function in Deep Learning with PyTorch
• Adagrad in Deep Learning with PyTorch
• Adam in Deep Learning with PyTorch
• ATen in Deep Learning with PyTorch
• Attention mechanism in Deep Learning with PyTorch
• Autoencoder in Deep Learning with PyTorch

B

• Backpropagation in Deep Learning with PyTorch
• Batch normalization in Deep Learning with PyTorch
• Batch size in Deep Learning with PyTorch
• Binary classification in Deep Learning with PyTorch
• Boltzmann machine in Deep Learning with PyTorch

C

• Cascade training in Deep Learning with PyTorch
• CNN in Deep Learning with PyTorch
• Convolution in Deep Learning with PyTorch
• Convolution Operation in Deep Learning with PyTorch
• Convolutional autoencoder in Deep Learning with PyTorch
• Cross-entropy loss in Deep Learning with PyTorch
• Cross-validation in Deep Learning with PyTorch
• CUDA in Deep Learning with PyTorch

D

• Data augmentation in Deep Learning with PyTorch
• Data loader in Deep Learning with PyTorch
• Data normalization in Deep Learning with PyTorch
• Deep belief network in Deep Learning with PyTorch
• Dimensionality reduction in Deep Learning with PyTorch
• Distributed Data-Parallel in Deep Learning with PyTorch
• Dropout in Deep Learning with PyTorch
• Dynamic Graphs in Deep Learning with PyTorch

E

• Early fusion in Deep Learning with PyTorch
• Early stopping in Deep Learning with PyTorch
• Embedding in Deep Learning with PyTorch
• Ensemble learning in Deep Learning with PyTorch
• Epoch in Deep Learning with PyTorch

F

• Feedforward neural network in Deep Learning with PyTorch
• Fine-tuned model in Deep Learning with PyTorch
• Fine-tuning in Deep Learning with PyTorch
• Focal loss in Deep Learning with PyTorch
• Fully connected layer in Deep Learning with PyTorch

G

• GAN in Deep Learning with PyTorch
• Gated Recurrent Unit (GRU) in Deep Learning with PyTorch
• Generative model in Deep Learning with PyTorch
• Gradient descent in Deep Learning with PyTorch
• Gradient in Deep Learning with PyTorch

H

• Hidden layer in Deep Learning with PyTorch

I

• Inception in Deep Learning with PyTorch
• Inference in Deep Learning with PyTorch
• Input layer in Deep Learning with PyTorch

J

• Jacobian matrix in Deep Learning with PyTorch

K

• K-means clustering in Deep Learning with PyTorch
• Kernel in Deep Learning with PyTorch
• Kullback-Leibler divergence in Deep Learning with PyTorch

L

• L1 regularization in Deep Learning with PyTorch
• L2 regularization in Deep Learning with PyTorch
• Label smoothing in Deep Learning with PyTorch
• LeakyReLU in Deep Learning with PyTorch
• Learning rate in Deep Learning with PyTorch
• Local response normalization in Deep Learning with PyTorch
• Loss function in Deep Learning with PyTorch
• LSTM in Deep Learning with PyTorch

M

• Max pooling in Deep Learning with PyTorch
• Mean absolute error in Deep Learning with PyTorch
• Mean squared error in Deep Learning with PyTorch
• Memory network in Deep Learning with PyTorch
• Metric learning in Deep Learning with PyTorch
• Mini-batch gradient descent in Deep Learning with PyTorch
• Mini-batch in Deep Learning with PyTorch
• MobileNet in Deep Learning with PyTorch
• Momentum in Deep Learning with PyTorch
• Multi-class classification in Deep Learning with PyTorch
• Multi-layer perceptron in Deep Learning with PyTorch

N

• Negative log-likelihood loss in Deep Learning with PyTorch
• Nesterov momentum in Deep Learning with PyTorch
• Non-maximum suppression in Deep Learning with PyTorch

O

• Object detection in Deep Learning with PyTorch
• One-hot encoding in Deep Learning with PyTorch
• Optimizer in Deep Learning with PyTorch
• Overfitting in Deep Learning with PyTorch

P

• Padding in Deep Learning with PyTorch
• Perceptron in Deep Learning with PyTorch
• Pre-trained model in Deep Learning with PyTorch
• Probabilistic graphical model in Deep Learning with PyTorch
• PyTorch in Deep Learning with PyTorch
• PyTorch Lightning in Deep Learning with PyTorch

R

• Radial basis function network in Deep Learning with PyTorch
• Random forest in Deep Learning with PyTorch
• Ranking loss in Deep Learning with PyTorch
• Recurrent neural network (RNN) in Deep Learning with PyTorch
• Regularization in Deep Learning with PyTorch
• ReLU in Deep Learning with PyTorch
• Residual connections in Deep Learning with PyTorch
• ResNet in Deep Learning with PyTorch
• RNN in Deep Learning with PyTorch

S

• Scheduling learning rate in Deep Learning with PyTorch
• Sequence to sequence (seq2seq) model in Deep Learning with PyTorch
• Sigmoid activation function in Deep Learning with PyTorch
• Sigmoid in Deep Learning with PyTorch
• Softmax in Deep Learning with PyTorch
• Stochastic gradient descent in Deep Learning with PyTorch
• Stochastic weight averaging (SWA) in Deep Learning with PyTorch
• Stride in Deep Learning with PyTorch
• Support vector machine (SVM) in Deep Learning with PyTorch

T

• Tensor in Deep Learning with PyTorch
• TorchScript in Deep Learning with PyTorch
• Transfer learning in Deep Learning with PyTorch
• Transformer in Deep Learning with PyTorch
• Triplet loss in Deep Learning with PyTorch

U

• U-Net in Deep Learning with PyTorch
• Unsupervised learning in Deep Learning with PyTorch

V

• Validation set in Deep Learning with PyTorch
• Vanishing gradient problem in Deep Learning with PyTorch
• Variational autoencoder in Deep Learning with PyTorch

W

• Weight decay in Deep Learning with PyTorch
• Weight initialization in Deep Learning with PyTorch
• Word2Vec in Deep Learning with PyTorch

X

• Xavier initialization in Deep Learning with PyTorch
• Xavier normal initialization in Deep Learning with PyTorch

Z

• Zero-padding in Deep Learning with PyTorch
• Zoneout in Deep Learning with PyTorch