The 2014 ImageNet competition saw the emergence of GoogLeNet (Szegedy et al., 2015), a network architecture that achieved remarkable results. Building upon the Network in Network (NiN) concept, GoogLeNet introduced improvements particularly focused on determining optimal convolution kernel sizes. Wh...
Perceptrons and Logical Operations A perceptron is a binary classifier that takes multiple inputs and produces a single output. Each input is weighted, and the output is determined by whether the weighted sum exceeds a threshold — yielding 1 (fire) if true, 0 (no fire) otherwise. Basic Logic Gates A...
Visualizing training and testing metrics through Loss and Accuracy curves provides immediate insight into whether your model is learning effectively. Metric Description **Loss Curve** Represents the model's error during training and evaluation. Lower values indicate better performance. **Accuracy Cu...
Core Concepts of Deep Learning Deep learning represents a class of machine learning algorithms that utilize stacked processing layers to learn hierarchical representations from unstructured data. Unlike traditional approaches requiring manual feature engineering, deep neural networks automatically d...
Core Development Tools dir(): Inspect object attributes help(): Access official documentasion Data Loading Fundamentals import os from torch.utils.data import Dataset from PIL import Image class CustomDataset(Dataset): def __init__(self, base_dir, category_dir): self.base_path = base_dir self.catego...