Machine Learning
Learning Curves for Machine Learning
What To Optimize for? Loss Function Cheat Sheet
漫谈 Clustering (3): Gaussian Mixture Model
Handling imbalanced datasets in machine learning
Understanding the Bias-Variance Tradeoff
Estimators, Loss Functions, Optimizers —Core of ML Algorithms
Deep learning – Information theory & Maximum likelihood
LogisticRegression
Understanding binary cross-entropy / log loss: a visual explanation
Bayes
Probabilistic Programming and Bayesian Methods for Hackers
A Principled Bayesian Workflow
Hierarchical Bayesian Neural Networks with Informative Priors
Dirichlet process mixtures for density estimation
Dimension Reduction
How to cross-validate PCA, clustering, and matrix decomposition models
Understanding Dimension Reduction
Deep Learning
How the backpropagation algorithm works
Implementing a Neural Network from Scratch in Python
The Matrix Calculus You Need For Deep Learning
The mostly complete chart of Neural Networks, explained
The Curse of Dimensionality and the Autoencoder
Neural Networks Backward Propagation
RNN
Understanding LSTM and its diagrams
The Unreasonable Effectiveness of Recurrent Neural Networks
Recurrent Neural Networks Tutorial
Attention and Augmented Recurrent Neural Networks
A Deep Dive into Recurrent Neural Nets
When recurrent models don’t need to be recurrent
Deriving LSTM Gradient for Backpropagation
Building RNN(LSTM cell) from scratch
Illustrated Guide to Recurrent Neural Networks: Understanding the Intuition
Illustrated Guide to LSTM’s and GRU’s: A step by step explanation
Non-Zero Initial States for Recurrent Neural Networks
A review of Dropout as applied to RNNs
CNN
Preprocessing for deep learning
ResNet, AlexNet, VGG, Inception: 理解各种各样的CNN架构
Optimization
Overview-optimizing-gradient-descent
Optimization Algorithms for Cost Functions
Numerical Optimization: Understanding L-BFGS
An Interactive Tutorial on Numerical Optimization
Statistical machine learning and convex optimization
Learning to Rank
Learning to Rank Sketchfab Models with LightFM
Intro to WARP Loss, automatic differentiation and PyTorch
Transfer Learning
Transfer Learning – Machine Learning’s Next Frontier
Information Retrival
Fast Near-Duplicate Image Search using Locality Sensitive Hashing
Building a Semantic Search Engine
Detecting Abuse at Scale: Locality Sensitive Hashing at Uber Engineering
Document Deduplication with Locality Sensitive Hashing
How To Create Data Products That Are Magical Using Sequence-to-Sequence Models
How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning
Product Quantizers for k-NN Tutorial
Nearest neighbor methods and vector models – part 1
