Models & inference · Glossary term
What is CNN (Convolutional Neural Network)?
A neural network that uses convolution operations (sliding filters over the input) to detect local patterns. Stacking convolutions detects increasingly complex features: edges, textures, objects.
“A neural network for images.”
What is the common confusion about CNN (Convolutional Neural Network)?
Convolutions also work on audio, time series, and other grid-like data.
Learn CNN (Convolutional Neural Network) in the course
Lessons that name CNN (Convolutional Neural Network) in a title or section
- CNNs — LeNet to ResNet
Every major CNN of the last thirty years is the same conv–nonlinearity–downsample recipe with one new idea bolted on. Learn the ideas in order.
- CNNs and RNNs for Text
Convolutions learn n-grams. Recurrences remember. Both are superseded by attention. Both still matter on constrained hardware. TF-IDF and Word2Vec produced flat vectors that ignored word order.
- The Fourier Transform
Every signal is a sum of sine waves. The Fourier transform tells you which ones. Language: Python Implement the DFT from scratch and verify it against the O(N log N) Cooley-Tukey FFT.
- Audio Classification — From k-NN on MFCCs to AST and BEATs
Everything from "dog barking vs siren" to "which language is this" is audio classification. The features are mels. The architecture moves each decade.
Covered in Phase 01: Math Foundations, Phase 04: Computer Vision, Phase 05: NLP: Foundations to Advanced and Phase 06: Speech & Audio.
Related terms
- FeatureAn individual measurable property of the data. In classical ML, you engineer features by hand.
- Inductive BiasStructural or statistical assumptions that favor some functions or representations over others.
- Activation FunctionA function applied after a linear or affine layer that introduces nonlinearity. Without it, composing layers with weights and biases…
- ReLURectified Linear Unit, defined as `f(x) = max(0, x)`. It is inexpensive and has a non-saturating positive branch, though zero gradients on…
More terms in Models & inference
- Attention
- Autoregressive
- CUDA
- Decoder
- Decoding Strategy
- Diffusion Model
- Encoder
- GAN (Generative Adversarial Network)
- GPT
- Inductive Bias
- Inference
- KV Cache
- LLM (Large Language Model)
- Logits
- MoE (Mixture of Experts)
- Nucleus Sampling (Top-p)
- Parameter
- Perplexity
- Quantization
- Self-Attention
- Speculative Decoding
- Stop Sequence
- Streaming
- Temperature
- Time to First Token (TTFT)
- Top-k Sampling
- Transformer
- VAE (Variational Autoencoder)
This entry comes from glossary/terms.md on GitHub. Browse all 250 glossary terms.