Deep Learning
7 topics in AI & Machine Learning
Activation Functions
Why neural networks need these tiny mathematical functions — and how ReLU's simplicity accidentally made deep learning possible.
Attention Mechanism
The trick that made ChatGPT possible — how AI learned to focus on what actually matters instead of reading everything equally.
Batch Normalization
The 2015 trick that let researchers train much deeper neural networks — why keeping numbers in the right range makes AI learn 10x faster.
Convolutional Neural Networks
How AI learned to see — the surprisingly simple idea behind face recognition, self-driving cars, and medical imaging.
Dropout Regularization
How randomly switching off neurons during training makes AI models that generalize better — the counterintuitive trick that stopped neural networks from memorizing everything.
Generative Adversarial Networks
How two AI networks competing against each other created the technology behind deepfakes, AI art, and synthetic data — the forger vs. the detective.
Recurrent Neural Networks
How AI learned to process sequences — the memory trick that powered speech recognition and translation before transformers took over.