L1&L2 Regularization

Machine Learning

Machine Learning Series

Learning Curves for Machine Learning

What To Optimize for? Loss Function Cheat Sheet

漫谈 Clustering (3): Gaussian Mixture Model

The area under the ROC curve

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

逻辑回归(Logistic Regression)

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

从特征分解到协方差矩阵:详细剖析和实现PCA算法

 

Deep Learning

全连接网络到卷积神经网络逐步推导

How the backpropagation algorithm works

Implementing a Neural Network from Scratch in Python

CS231n Notes

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

RNNs in Tensorflow

Understanding LSTM and its diagrams

Vanilla LSTM with numpy

The Unreasonable Effectiveness of Recurrent Neural Networks

Recurrent Neural Networks Tutorial

Understanding LSTM Networks

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

一文入门卷积神经网络:CNN通俗解析

CNN卷积神经网络架构综述

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

Why Momentum Really Works

An Interactive Tutorial on Numerical Optimization

Adam那么棒,为什么还对SGD念念不忘

Statistical machine learning and convex optimization

Gradient Descent & AdaGrad

Series: Optimization

Distributed Deep Learning

一阶机器学习优化算法

 

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

ElastiK Nearest Neighbors

Building a Semantic Search Engine

海量数据相似度计算之simhash短文本查找

使用SimHash进行海量文本去重

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

大规模高维数据实时相似搜索算法

 

People

Yoshua Bengio

Mark Chang

huaxiaozhuan

Sargur N. Srihari