Neural Networks & Deep Learning
Bridge classical machine learning and modern generative AI by learning how neural networks are structured, trained, and specialized for text, images, and sequences.
Neural networks are the bridge between classical ML and the transformer models behind modern generative AI. Learn how neurons, layers, activations, and backpropagation combine to let a network learn from data.
How Neural Networks Work
Build an intuitive, then mechanical, understanding of neurons, layers, weights, and forward passes.
7 min read →Activation Functions Explained
Compare sigmoid, ReLU, and softmax, and understand why the choice of activation function shapes what a network can learn.
7 min read →Training with Backpropagation
Understand, at a conceptual and mechanical level, how a network learns from its mistakes.
7 min read →CNNs vs. RNNs vs. Transformers
Compare the three major neural network architectures and understand why transformers became dominant for language.
7 min read →Avoiding Common Neural Network Training Pitfalls
Recognize the most frequent, avoidable mistakes that make a neural network fail to train or fail to generalize.
7 min read →