AI Models
Neural networks, transformers and how machines learn to think
In the beginning...
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Lessons
Learning from examples
Practice: Labelled or unlabelled?
Inside a neuron
Practice: Compute the weighted sum
Activation functions
Practice: Match the activation
Layers and the forward pass
Practice: Trace the signal
Measuring mistakes with loss
Practice: Fit the line
Gradient descent
Practice: Update a weight
Practice: Read the loss curve
Backpropagation
The training loop
Convolutional networks
Practice: Apply the filter
Recurrent networks
Attention
Practice: Memory and attention
Transformers
Training vs inference
Practice: Training or inference?
Course Complete
Up nextRecommended first Launch Lesson
What is an AI model?
Start from individual neurons and build up to full transformer architectures. Learn activation functions, loss, gradient descent, backpropagation, CNNs, RNNs, and attention.
