YouTip-- Learning not just technology, but dreams!
\\n- \\n
- Home \\n
- HTML \\n
- JavaScript \\n
- CSS \\n
- Vue \\n
- React \\n
- Python3 \\n
- Java \\n
- C \\n
- C++ \\n
- C# \\n
- AI \\n
- Go \\n
- SQL \\n
- Linux \\n
- VS Code \\n
- Bootstrap \\n
- Git \\n
- Local Bookmarks \\n
- \\n
- Vue3 Tutorial \\n
- Vue2 Tutorial \\n
- \\n
- Bootstrap3 \\n
- Bootstrap4 \\n
- Bootstrap5 \\n
- \\n
- Machine Learning \\n
- PyTorch \\n
- TensorFlow \\n
- Sklearn \\n
- NLP \\n
- AI Agent \\n
- Ollama \\n
- Coding Plan \\n
PyTorch Tutorial
\\n- \\n
- PyTorch Tutorial \\n
- PyTorch Introduction \\n
- PyTorch Installation \\n
- PyTorch Basics \\n
- PyTorch Tensors \\n
- PyTorch Neural Network Basics \\n
- PyTorch First Neural Network \\n
- PyTorch Data Processing and Loading \\n
- PyTorch Linear Regression \\n
- PyTorch Convolutional Neural Networks \\n
- PyTorch Recurrent Neural Networks \\n
- PyTorch Datasets \\n
- PyTorch Data Transforms \\n
- Pytorch torch Reference Manual \\n
- PyTorch torch.nn Reference Manual \\n
- Transformer Model \\n
- PyTorch Transformer \\n
- PyTorch torch.optim \\n
- PyTorch torchvision \\n
- PyTorch Model Deployment \\n
- PyTorch Model Saving and Loading \\n
- PyTorch Image Classification \\n
- PyTorch Text Sentiment Analysis \\n
- PyTorch Autograd \\n
- PyTorch GPU / CUDA Acceleration \\n
- PyTorch Loss Functions \\n
- PyTorch Learning Rate Scheduler \\n
- PyTorch Transfer Learning \\n
- PyTorch Batch Normalization \\n
- PyTorch LSTM / GRU \\n
- PyTorch Word Embedding \\n
- PyTorch Generative Adversarial Networks \\n
- PyTorch Autoencoder \\n
- PyTorch Model Evaluation and Debugging \\n
- PyTorch torchtext \\n
- PyTorch Mixed Precision Training \\n
- PyTorch TorchScript/ONNX Export \\n
- PyTorch Distributed Training \\n
- PyTorch Attention Mechanism \\n
PyTorch torch.nn Reference Manual
\\nPyTorch torch.linalg.lstsq Function
\\n\\n
Pytorch torch Reference Manual
torch.linalg.lstsq is a function in the PyTorch linear algebra module used to solve linear least squares problems. It computes argmin_x ||AX - B||_F.
Function Definition
\\ntorch.linalg.lstsq(A, B, rcond=None, driver=None)
Parameters:
\\n- \\n
A(Tensor): Coefficient matrix. \\nB(Tensor): Right-hand side matrix or vector. \\nrcond(float, optional): Condition number used to truncate small singular values. \\ndriver(str, optional): Solver selection, options are 'gels', 'gelsd', 'gelsy'. \\n
Return Value:
\\n- \\n
torch.Tensor: Returns the least-squares solution. \\n
\\n
Usage Example
\\nExample
\\nimport torch\\n\\n# Create Coefficient Matrix and Right-Hand Side Vector\\n\\n A = torch.tensor([[1.0,1.0],[1.0,2.0],[1.0,3.0]], dtype=torch.float64)\\n\\n B = torch.tensor([1.0,2.0,3.0], dtype=torch.float64)\\n\\n# Solve Least Squares\\n\\n X = torch.linalg.lstsq(A, B).solution\\n\\nprint("Coefficient Matrix A:")\\n\\nprint(A)\\n\\nprint("nRight-Hand Side Vector B:")\\n\\nprint(B)\\n\\nprint("nLeast Squares Solution X:")\\n\\nprint(X)\\nThe output result is:
\\nCoefficient matrix A: tensor([[1., 1.], [1., 2.], [1., 3.]], dtype=torch.float64)Right-hand side vector B: tensor([1., 2., 3.], dtype=float64)Least squares solution X: tensor([0.0000, 1.0000], dtype=float64)
\\n\\n
Pytorch torch Reference Manual
AI is thinking...
\\n\\n\\n