Artificial Inteligence
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  • Preface
  • Introduction
  • Machine Learning
    • Linear Algebra
    • Supervised Learning
      • Neural Networks
      • Linear Classification
      • Loss Function
      • Model Optimization
      • Backpropagation
      • Feature Scaling
      • Model Initialization
      • Recurrent Neural Networks
        • Machine Translation Using RNN
    • Deep Learning
      • Convolution
      • Convolutional Neural Networks
      • Fully Connected Layer
      • Relu Layer
      • Dropout Layer
      • Convolution Layer
        • Making faster
      • Pooling Layer
      • Batch Norm layer
      • Model Solver
      • Object Localization and Detection
      • Single Shot Detectors
        • Yolo
        • SSD
      • Image Segmentation
      • GoogleNet
      • Residual Net
      • Deep Learning Libraries
    • Unsupervised Learning
      • Principal Component Analysis
      • Generative Models
    • Distributed Learning
    • Methodology for usage
      • Imbalanced/Missing Datasets
  • Artificial Intelligence
    • OpenAI Gym
    • Tree Search
    • Markov Decision process
    • Reinforcement Learning
      • Q_Learning_Simple
      • Deep Q Learning
      • Deep Reinforcement Learning
    • Natural Language Processing
      • Word2Vec
  • Appendix
    • Statistics and Probability
      • Probability
        • Markov Chains
        • Random Walk
    • Lua and Torch
    • Tensorflow
      • Multi Layer Perceptron MNIST
      • Convolution Neural Network MNIST
      • SkFlow
    • PyTorch
      • Transfer Learning
      • DataLoader and DataSets
      • Visualizing Results
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  1. Appendix
  2. Statistics and Probability
  3. Probability

Random Walk

Random Walks are used to model phenomena that boils down to a stochastic decision (random step) that will somehow guide the direction of the system. Some examples that could use random walks to model them:

  • Stock market fluctuation

  • Financial status of a gambler

  • References:

  • https://en.wikipedia.org/wiki/Random_walk

  • http://www.bioinfo.org.cn/~wangchao/maa/mcrw.pdf

  • https://www.cs.princeton.edu/courses/archive/fall13/cos521/lecnotes/lec12.pdf

  • https://www.cs.cmu.edu/~avrim/598/chap5only.pdf

  • https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/readings/MIT6_042JF10_chap20.pdf

  • http://twiecki.github.io/blog/2017/03/14/random-walk-deep-net/

  • https://www.youtube.com/watch?v=S9d5nupP_7Q

  • https://www.youtube.com/watch?v=6wUD_gp5WeE

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Last updated 5 years ago

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