Optimization

Part 2: Strong duality theorem

  • To see the code I wrote for this project, you can check out its Github repo
  • For other parts of the project: part 1, part 2

Introduction

  • The primal form tries to maximize a given objective function
  • The dual form tries to minimize the upper bound of this objective function, which means it is also a linear program.

Weak duality theorem

The primal objective function is always less than…


Optimization

Part 1: Weak duality theorem

  • To see the code I wrote for this project, you can check out its Github repo
  • For other parts of the project: part 1, part 2

Introduction

Find x₁ and x₂ to minimize f(x₁, x₂). Source
  • Go over the primal and dual forms for the most basic of…


Natural Language Processing

Part 3: Optimize model interpolation

  • To see the code I wrote for this project, you can check out its Github repo
  • For other parts of the project: part 1, part 2, part 3

Background

The vertical line denotes the probability of “dream” given the previous words “have a”


Natural Language Processing

Part 2: Higher n-gram models

  • To see the code I wrote for this project, you can check out its Github repo
  • For other parts of the project: part 1, part 2, part 3

Background


Natural Language Processing

Part 1: Unigram model

  • To see the code I wrote for this project, you can check out its Github repo
  • For other parts of the project: part 1, part 2, part 3

Background


Simulation

Part 3: Central limit theorem

  • To see the code I wrote for this project, you can check out its Github repo
  • For other parts of the project: part 1, part 2, part 3

Background


Simulation

Part 2: Box-Muller transform

  • To see the code I wrote for this project, you can check out its Github repo
  • For other parts of the project: part 1, part 2, part 3

Background

  1. First, we sample from the uniform distribution between 0 and 1 — green points in the below animation. These uniform samples represent the cumulative probabilities of a Gaussian distribution i.e. the area under the distribution to the left of some point.
  2. Next, we apply the inverse Gaussian cumulative distribution function (CDF) to…


Simulation

Part 1: Inverse transform sampling

  • To see the code I wrote for this project, you can check out its Github repo
  • For other parts of the project: part 1, part 2, part 3

Background


Sports Analytics

Part 6: Combine ranking models & final benchmark

Background


Sports Analytics

Part 5: Sequential multi-factor model

Background

Khanh Nguyen

Data scientist based in Ho Chi Minh City, Vietnam | dknguyen.com

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