Freaks City

Tensor Creaiton and Initialization Basic Tensor Operations import torch # Create tensor from list data_tensor = torch.tensor([1, 2, 3], dtype=torch.float32) # Create tensor with random values (uniform distribution) rand_tensor = torch.rand(2, 3) # Create tensor with normal distribution values normal_tensor = torch.randn(3, 4) # Create tenso ...

PyTorch's Automatic Mixed Precision (AMP) feature allows efficient training by combining FP32 and FP16 precision operations. This technique reduces memory usage and accelerates computation while maintaining model accuracy. Understanding Mixed Precision Deep learning models traditionally use 32-bit floating point (FP32) for all operations. Mixed ...

Attention Mechanisms and Transformers The attention mechanism addresses a fundamental challenge in deep learning: transforming variable-dimensional inputs into fixed-dimensional outputs through a weighted aggregation process. This capability proves essential when dealing with sequences or sets of varying sizes, where traditional fixed-parameter ...

Nearest Neighbor and k-NN Classifiers The Nearest Neighbor classifier stores the entire training set and predicts labels by finding the closest training example using a distance metric like L1 (Manhattan) or L2 (Euclidean). While simple, it suffers from high prediction latency (O(n)) and large memory usage. class KNearestNeighbor: def init(sel ...

Deep learning, a subset of machine learning, relies on neural networks with multiple layers to model complex patterns in data. At its core is the artificial neural network (ANN), inspired by biological neurons, which processes inputs through layered computations to produce meaningful outputs. Structure of a Neural Network A typical feedforward ...

Data Loading with PyTorch PyTorch uses torch.utils.data.DataLoader as the primary interface for efficient data loading. This class enables batched, shuffled, and parallelized data access without overwhelming system memory. from torch.utils.data import DataLoader, Dataset class CustomDataset(Dataset): def __init__(self, features, labels): ...