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Introduction to Deep Learning & Neural Networks

Convolutional Neural Networks

Convolution, pooling and CNN architectures — why weight sharing makes images learnable.

Level
Intermediate
Lessons
3
Total time
54 min
Simulations
4
Included in Introduction to Deep Learning & Neural Networks · $9
Tools you'll use
  • Python
  • PyTorch
  • NumPy
Skills
  • Convolution
  • ResNet
  • Transfer learning
Try it now — free

This is the real simulation from the module. No account needed.

What you will be able to do

  • Compute the output size of a convolution layer
  • Fine-tune a pretrained model for a small dataset

Before you start

A suggestion, never a lock — you can start here.

  • training-deep-networks
Modules that help first

Syllabus

3 lessons · 54 min

Every lesson's title and summary is public. The lessons themselves open once you own the track.

Every lesson in Introduction to Deep Learning & Neural Networks

  1. How Convolutions Work

    18 min

    Kernels, stride, padding, and why one filter can be reused across an image.

    Included with the track Simulation

  2. CNN Architectures: from LeNet to ResNet

    18 min

    The ideas that lasted — and the skip connection that made very deep networks trainable.

    Included with the track Simulation

  3. Transfer Learning

    18 min

    Using a pretrained model when you only have a few hundred images.

    Included with the track Simulation
Lifetime access
$9
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What a neural network actually computes, why the nonlinearity is the whole trick, and how the weights learn.

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Training Deep Networks

Optimizers, learning-rate schedules, initialisation, normalisation and regularisation — what makes training actually converge.

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PyTorch for Deep Learning

Tensors, autograd, nn.Module, DataLoader and GPU training — enough PyTorch to work on your own.

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Sequence Models and Transformers

From RNNs and LSTMs to self-attention — the architecture behind every modern LLM.

  • 3 lessons
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Included in Introduction to Deep Learning & Neural Networks· $9