• #algorithms(4) • #applied-ml(1) • #computational-graph(3) • #computer-vision(2) • #deep-learning(2) • #distributed-computing(1) • #distributed-systems(1) • #machine-learning(11) • #optimisation(4) • #probability(3) • #probability-theory(1) • #ranking(1) • #search(1) • #statistics(1) • #stories(2)

#algorithms

[notes] Knapsack
[notes] Quick Select
[posts] Finding Median
[posts] Binary Search

#applied-ml

[notes] Movie Reviews and Gradient Descent

#computational-graph

[notes] Gradient Through Concatenation
[notes] Gradient Through Addition with Broadcasting
[posts] Differentiable Computations

#computer-vision

[notes] Canny Edge detector
[notes] Histogram of Oriented Gradients

#deep-learning

[notes] Common Optimisers
[notes] Focal Loss

#distributed-computing

[notes] Reservoir Sampling

#distributed-systems

[links] Evolution of Search Engines

#machine-learning

[posts] XGBoost
[posts] Normalisation Layers
[posts] Variational Inference
[notes] Gradient Boosting
[notes] Attribute Selection in Decision Trees
[notes] Bias and Variance
[notes] Common Optimisers
[notes] Focal Loss
[notes] Softmax Classifier
[notes] Linear Classifiers
[notes] Bayes Error

#optimisation

[posts] Variational Inference
[notes] KKT conditions and Lagrange multipliers
[notes] BFGS
[notes] Newton's Method

#probability

[posts] Sampling in a Sphere
[posts] Variational Inference
[notes] Reservoir Sampling

#probability-theory

[links] Diffusion Models

#ranking

[notes] RankNet and LambdaRank
[notes] RankNet and LambdaRank

#statistics

[posts] Sampling in a Sphere

#stories

[stories] Productivity Principles
[stories] Logic Bulling