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Abstract illustration of a lock or seal representing the final keyword in Java, with a class, method, and variable symbol.
Java

Java Final Keyword: Usage and Behavior

Illustration of a Java final variable as a locked reference, with a single assignment arrow and a forbidden second assignment symbol.
Java

Java Final Variable: Syntax, Behavior, and Use Cases

Diagram contrasting explicit Dispose calls with garbage collector finalization timing in C#.
C#

C# Dispose vs Destructor: Key Differences Explained

Illustration of C# variable declaration showing type, name, and value assignment in a code editor.
C#

C# Variable Declaration: Syntax and Practical Usage

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Latest Posts

A side-by-side visual comparison of PyTorch and TensorFlow code structures, highlighting their different API approaches.
Python

PyTorch vs TensorFlow: Which One Fits Your Project

Compare PyTorch and TensorFlow from a developer's perspective: API design, training loops, debugging, deployment, and decision criteria for your project.

PyTorchTensorFlowDeep LearningKeras
A GPU chip with a memory gauge showing reduced usage after applying PyTorch optimizations.
Python

Python PyTorch GPU Memory Optimization

python pytorch gpu memory optimization: Learn practical techniques to reduce GPU memory usage in PyTorch: mixed precision, gradient accumulation, checkpointing, and pr...

PyTorchGPU memorymixed precisiongradient accumulation
Illustration showing FP32 and FP16 data paths converging through a scaling node inside a neural network training loop.
Python

PyTorch Mixed Precision Training with torch.cuda.amp

Use PyTorch's autocast and GradScaler for mixed precision training on CUDA GPUs: reduce memory use, accelerate Tensor Core GPUs, and avoid gradient underflow.

PyTorchMixed PrecisionAMPGPU Training
Diagram showing dropout, batch normalization, and learning rate scheduler components in a PyTorch neural network training pipeline.
Python

Combining Dropout, Batch Normalization, and Learning Rate Schedulers in PyTorch

Learn how to combine dropout, batch normalization, and learning rate schedulers in PyTorch, including layer placement, train/eval modes, scheduler timing, and common pitfalls.

PyTorchDropoutBatch NormalizationLearning Rate Schedulers
Diagram showing a PyTorch model in eval mode with gradient tracking disabled, comparing no_grad and inference_mode contexts.
Python

PyTorch Inference: model.eval() vs torch.no_grad() vs torch.inference_mode()

Learn how model.eval(), torch.no_grad(), and torch.inference_mode() affect PyTorch inference, when to combine them, and which context to choose.

PyTorchinferenceautogradmodel evaluation
Illustration of a PyTorch model state_dict being saved and loaded as a checkpoint file
Python

How to Save and Load PyTorch state_dict and Checkpoints

Learn how to save and load PyTorch models with state_dict and full checkpoints, including optimizer state, epoch, and safe device handling.

PyTorchstate_dictcheckpointsmodel persistence
Diagram of a PyTorch training and validation loop showing forward pass, backward pass, and evaluation phases on a neural network
Python

PyTorch Training and Validation Loops in Python

Learn how to structure PyTorch training and validation loops, handle device placement, manage gradients, and avoid common pitfalls.

PyTorchdeep learningmodel trainingbackpropagation
Illustration of a custom PyTorch Dataset feeding samples into a DataLoader that produces training batches.
Python

PyTorch Dataset and DataLoader for Custom Datasets

Implement custom PyTorch datasets with __getitem__ and __len__, then use DataLoader for batching, shuffling, and parallel loading.

PyTorchDatasetDataLoaderCustom Dataset
Diagram of a PyTorch training loop connecting a loss function to Adam and SGD optimizers with gradient flow
Python

PyTorch Loss Functions and Optimizers: Adam vs SGD

python pytorch loss functions optimizers adam and sgd: How to pair loss functions with optimizers in PyTorch, and how Adam and SGD differ in update behavior, memory us...

PyTorchloss functionsoptimizersAdam
A visual representation of PyTorch neural network layers and activation functions, showing connected nodes and a ReLU curve.
Python

PyTorch torch.nn Layers and Activation Functions: A Practical Guide

Build PyTorch neural networks with torch.nn layers and activation functions. Covers nn.Linear, nn.Conv2d, nn.Sequential, custom nn.Module models, initialization, and the functional API.

PyTorchNeural Networksnn.ModuleActivation Functions