MindMap Gallery Pytorch Brain Diagram
This is a mind map about Pytorch brain map. This mind map covers the core components of PyTorch and its main functional modules, which are easy for developers to get started and use quickly.
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This is a mind map about plant asexual reproduction, and its main contents include: concept, spore reproduction, vegetative reproduction, tissue culture, and buds. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about the reproductive development of animals, and its main contents include: insects, frogs, birds, sexual reproduction, and asexual reproduction. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about bacteria, and its main contents include: overview, morphology, types, structure, reproduction, distribution, application, and expansion. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about plant asexual reproduction, and its main contents include: concept, spore reproduction, vegetative reproduction, tissue culture, and buds. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about the reproductive development of animals, and its main contents include: insects, frogs, birds, sexual reproduction, and asexual reproduction. The summary is comprehensive and meticulous, suitable as review materials.
Pytorch Brain Diagram
PyTorch Core Components
1. Tensor
Basic operations (addition, subtraction, multiplication, and division, matrix multiplication)
Shape transformation (reshape/view/squeeze/unsqueeze)
Data type conversion (float32/int64, etc.)
GPU acceleration (.cuda()/.to(device))
2. Automatic differentiation (Autograd)
Requires_grad property
backward() backpropagation
grad gradient calculation
3. Neural Network Layer (nn.Module)
Basic layer
Linear Full Connection Layer
Conv2d Convolutional Layer
RNN/LSTM/GRU loop layer
Dropout/BatchNorm Regularization Layer
container
Sequential Sequential Container
ModuleList module list
4. Data Utilities
Dataset abstract class
DataLoader Data Loader
Batch_size
Shrink data (shuffle)
Multi-process loading (num_workers)
Predefined datasets (torchvision.datasets)
5. Optimization
SGD
Adam
RMSprop
Learning rate scheduler (lr_scheduler)
6. Loss Functions
MSE (mean square error)
CrossEntropy (Cross Entropy)
BCEWithLogits (Binary Classification)
Custom loss function
7. Practical Tools
Model save/load (torch.save()/torch.load())
Device Management (torch.cuda/is_available())
Random seed settings (manual_seed)
8. Distributed training
DataParallel (stand-alone multi-card)
DistributedDataParallel (multiple machine and multiple cards)
9. Deployment related
TorchScript (model serialization)
ONNX Export