Agustinus Kristiadi
Advent of Code 2024: A Gleam Retrospective
Writing Advice for Fledging Machine Learning Researchers
The 'use' Expression in Gleam
Volume Forms and Probability Density Functions Under Change of Variables
The Invariance of the Hessian and Its Eigenvalues, Determinant, and Trace
Convolution of Gaussians and the Probit Integral
The Last Mile of Creating Publication-Ready Plots
Modern Arts of Laplace Approximations
Chentsov's Theorem
The Curvature of the Manifold of Gaussian Distributions
Hessian and Curvatures in Machine Learning: A Differential-Geometric View
Optimization and Gradient Descent on Riemannian Manifolds
Minkowski's, Dirichlet's, and Two Squares Theorem
Reduced Betti number of sphere: Mayer-Vietoris Theorem
Brouwer's Fixed Point Theorem: A Proof with Reduced Homology
Natural Gradient Descent
Fisher Information Matrix
Introduction to Annealed Importance Sampling
Gibbs Sampler for LDA
Boundary Seeking GAN
Least Squares GAN
CoGAN: Learning joint distribution with GAN
Wasserstein GAN implementation in TensorFlow and Pytorch
InfoGAN: unsupervised conditional GAN in TensorFlow and Pytorch
Maximizing likelihood is equivalent to minimizing KL-Divergence
Variational Autoencoder (VAE) in Pytorch
Generative Adversarial Networks (GAN) in Pytorch
Theano for solving Partial Differential Equation problems
Linear Regression: A Bayesian Point of View
MLE vs MAP: the connection between Maximum Likelihood and Maximum A Posteriori Estimation
Conditional Generative Adversarial Nets in TensorFlow
KL Divergence: Forward vs Reverse?
Conditional Variational Autoencoder: Intuition and Implementation
Variational Autoencoder: Intuition and Implementation
Deriving Contractive Autoencoder and Implementing it in Keras
Many flavors of Autoencoder
Level Set Method Part II: Image Segmentation
Level Set Method Part I: Introduction
Residual Net
Generative Adversarial Nets in TensorFlow
How to Use Specific Image and Description when Sharing Jekyll Post to Facebook
Deriving LSTM Gradient for Backpropagation
Convnet: Implementing Maxpool Layer with Numpy
Convnet: Implementing Convolution Layer with Numpy
Implementing BatchNorm in Neural Net
Implementing Dropout in Neural Net
Beyond SGD: Gradient Descent with Momentum and Adaptive Learning Rate
Implementing Minibatch Gradient Descent for Neural Networks
Paralellizing Monte Carlo Simulation in Python
Scrapy as a Library in Long Running Process
Gaussian Anomaly Detection
Slice Sampling
Rejection Sampling
Metropolis-Hastings
Gibbs Sampling
Twitter Authentication with Tweepy and Flask
Deploying Wagtail App
Developing Blog with Wagtail
Setting Up Wagtail Development Environment