h2
Dynamic Programming for Reinforcement Learning, the importance of the Bellman equations; (with Gymna
h2
Could Large Language Models be conscious? (David Chalmers @ Neurips 2022)
h2
NLP Papers at ICML2022
h2
You don't feel like you're good enough, but its not a competition
h2
Stochastic Gradient Langevin Dynamics
h2
Recipe for connecting to Google Drive from Remote Server
h2
Minimum Bayes Risk Decoding
h2
Formalising Analogies for A.I
h2
You're not doing well, but motivation is optional
h2
Likelihood weighted Sequential Importance Sampling
h2
Neural Tangent Kernel, Every Model trained by GD is a kernel machine (Review)
h2
Some QA from Deep Learning (CS 462/482)
h2
Adversarial NLP examples with Fast Gradient Sign Method
h2
Variance of the Estimator in Machine Learning
h2
Some Clustering Papers at ICLR20
h2
A minimum keystroke (py)Debugger for Lazy ML/DS people who don't IDE
h2
Recipe for building jq from source without admin(sudo) rights
h2
The Sigmoid in Regression, Neural Network Activation and LSTM Gates
h2
Clean TreeLSTMs implementation in PyTorch using NLTK treepositions and Easy-First Parsing
h2
Pad pack sequences for Pytorch batch processing with DataLoader
h2
Coordinate Ascent Mean-field Variational Inference (Univariate Gaussian Example)
h2
Dirichlet Process Gaussian Mixture Models (Generation)
h2
Gotchas in Cython; Handling numpy arrays in cython class
h2
Onboarding for Practical Machine Learning Research
h2
Equivalence of constrained and unconstrained form for Ridge Regression
h2
Studying drug-drug interactions and predictors of adverse vascular outcomes
h2
PyTorch Automatic differentiation for non-scalar variables; Reconstructing the Jacobian
h2
From psychologist to CS PhD Student
h2
Capturing Last-mile Transactions of Smallholder Palm Oil Farmers
h2
Migrating from python 2.7 to python 3 (and maintaining compatibility)
h2
Lagrange Multipliers and Constrained Optimization
h2
Taylor Series approximation, newton's method and optimization
h2
Hessian, second order derivatives, convexity, and saddle points
h2
Jacobian, Chain rule and backpropagation
h2
Gradients, partial derivatives, directional derivatives, and gradient descent
h2
Derivatives, differentiability and loss functions
h2
Calculus for Machine Learning
h2
Algorithms on Graphs: Fastest Route
h2
Gibbs Sampling on Dirichlet Multinomial Naive Bayes (Text)
h2
Markov Chain Monte-Carlo
h2
EM Algorithm for Gaussian mixtures
h2
Communicating Data Science
h2
Cross disciplinary projects
h2
Closed form Bayesian Inference for Binomial distributions