Concepts starting with K

18 concepts, each with a plain-language explanation, examples and diagrams. Pick a letter or open a concept.

k Nearest Neighbors Algorithm — The k-NN rule maps a training sample and a chosen k to a label for each point under consideration.
k-Means and Other Cost Minimization Clusterings — The objective function and the iterative algorithm are related but not identical ideas.
k-Means Clustering — Dictionary learning creates a feature vocabulary for data that may not have an obvious vocabulary.
k-NN Classification — k-NN regression predicts a real-valued target by combining the targets of the k nearest neighbors.
Kamin Blocking — The Rescorla-Wagner model links learning to surprise.
Keras — The convnet progressively exchanges spatial detail for deeper feature representations.
Keras and Deep Learning Framework — Part 1 is a staged introduction rather than a single isolated topic.
Keras datasets — MNIST is a classic handwritten-digit classification dataset.
Keras Framework — The basic prerequisite is Python programming experience.
Keras layers — SimpleRNN is an actual Keras layer.
Keras Model Architecture — TensorBoard is a browser-based visualization tool packaged with TensorFlow for inspecting Keras models using the TensorFlow…
Kernel Methods for Support Vector Machines — Regularizing the norm of w helps both sample complexity and computation.
Kernels for SVM — Soft-SVM with kernels is the focus of the section.
Key Components of Samuel's Checkers Player — Samuel's checkers player is associated with a backup diagram.
Key Features of Python — Due to its open-source nature, Python has been ported to (i.e. changed to make it work on) many platforms. All your Python…
Key Questions for Evaluation — The diagnostic question asks whether the company's FDEs ship code into the vendor's own product.
Keyword Arguments — When you use positional arguments, you must remember the exact order in which the function expects them. If a function takes…
Kullback-Leibler Divergence — The PAC-Bayes theorem bounds an expected-loss quantity using training-set loss and a square-root term.