PyTorch Archives - Coder's Jungle

How to work with tensors using torch.Tensor in PyTorch

July 31, 2025

NumPy limitations in efficiency and scalability for large datasets and GPU operations highlight the advantages of tensors. TensorFlow excels in matrix multiplication, leveraging GPU power for faster computations. Automatic differentiation in tensors supports efficient gradient calculations essential for machine learning, marking a shift towards tensor-based frameworks in numerical computing.

The post How to work with tensors using torch.Tensor in PyTorch appeared first on Python FAQ.

Coding Tips

Implementing Dropout Regularization with torch.nn.functional.dropout

May 17, 2025

Implement dropout regularization in neural networks using PyTorch's torch.nn.functional.dropout to prevent overfitting and enhance model generalization.

The post Implementing Dropout Regularization with torch.nn.functional.dropout appeared first on Python Lore.

Coding Tips

Performing Parallel and Distributed Training with torch.distributed

March 6, 2025

Optimize machine learning efficiency with torch.distributed for parallel and distributed training across GPUs and clusters, enhancing performance and scalability.

The post Performing Parallel and Distributed Training with torch.distributed appeared first on Python Lore.

Coding Tips

Creating Custom Datasets and DataLoaders in PyTorch

February 6, 2025

Maximize data efficiency in PyTorch with custom Datasets and DataLoaders. Learn to create, manage, and optimize your machine learning data workflows seamlessly.

The post Creating Custom Datasets and DataLoaders in PyTorch appeared first on Python Lore.

Coding Tips

Implementing Transformer Models in PyTorch

December 18, 2024

Transformers in PyTorch revolutionize NLP with efficient parallel processing, multi-head self-attention, and advanced encoder-decoder architecture for superior context handling.

The post Implementing Transformer Models in PyTorch appeared first on Python Lore.

Coding Tips

Applying Activation Functions with torch.nn.functional

November 21, 2024

Optimize neural networks with activation functions using torch.nn.functional. Explore ReLU, sigmoid, and tanh for enhanced learning and performance.

The post Applying Activation Functions with torch.nn.functional appeared first on Python Lore.

Coding Tips

Advanced Tensor Operations with torch.linalg, torch.fft, torch.special – Python Lore

October 20, 2024

Unlock advanced tensor operations in PyTorch with torch.linalg, including matrix inversion, determinants, SVD, eigenvalues, and more for high-performance computing.

The post Advanced Tensor Operations with torch.linalg, torch.fft, torch.special appeared first on Python Lore.

Coding Tips

Utilizing torch.jit for TorchScript and JIT Compilation

September 23, 2024

Unlock the power of TorchScript in PyTorch with torch.jit for efficient model compilation. Transform and optimize your models for production deployment, leveraging both Python's flexibility and C++'s performance through scripting and tracing methods. Enhance your machine learning workflow today.

The post Using torch.jit for TorchScript and JIT Compilation appeared first on Python Lore.

Coding Tips

Custom Loss Functions and Advanced Regularization Techniques

August 31, 2024

Unlock the potential of machine learning with custom loss functions and advanced regularization techniques. Explore key types, including regression and classification losses, and learn how they enhance model performance and generalization for improved predictions on unseen data.

The post Custom Loss Functions and Advanced Regularization Techniques appeared first on Python Lore.

Coding Tips

Advanced Memory Management and Profiling with torch.memory

August 5, 2024

Optimize deep learning performance with advanced PyTorch memory management strategies. Explore dynamic memory allocation, caching, and monitoring techniques to minimize fragmentation and enhance model efficiency. Master these tools to elevate your deep learning workflows and utilization of GPU resources.

The post Advanced Memory Management and Profiling with torch.memory appeared first on Python Lore.

How to work with tensors using torch.Tensor in PyTorch

Implementing Dropout Regularization with torch.nn.functional.dropout

Performing Parallel and Distributed Training with torch.distributed

Creating Custom Datasets and DataLoaders in PyTorch

Implementing Transformer Models in PyTorch

Applying Activation Functions with torch.nn.functional

Advanced Tensor Operations with torch.linalg, torch.fft, torch.special – Python Lore

Utilizing torch.jit for TorchScript and JIT Compilation

Custom Loss Functions and Advanced Regularization Techniques

Advanced Memory Management and Profiling with torch.memory

Clustering and Spatial Analysis with scipy.cluster

How to find roots and optimize functions with scipy.optimize in Python

How to export a function from a module in JavaScript

How to terminate a Python script with sys.exit