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Learn AI with Python · Lesson

Building and Training CNNs in PyTorch

nn.Conv2d, nn.MaxPool2d, nn.Linear, training loop, optimizer, loss, accuracy tracking.

Why CNNs for Images?

Convolutional Neural Networks learn spatial patterns: edges, textures, then shapes and objects. Convolutions share weights across the image, making CNNs efficient and translation-aware, the backbone of computer vision.

import torch
import torch.nn as nn

The nn.Module Base Class

Models subclass nn.Module. You define layers in __init__ and the data flow in forward. PyTorch tracks parameters and gradients automatically.

class CNN(nn.Module):
    def __init__(self):
        super().__init__()

All lessons in this course

  1. PyTorch Tensors and Autograd
  2. Custom Datasets and DataLoaders
  3. Building and Training CNNs in PyTorch
  4. Object Detection with YOLOv8
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