《machine-learning-mindmap》
1 Machine Learning Process
（Daniel Martinez）

• 返回总图

• Data

  • Find

  • Collect

  • Explore

  • Clean Features

  • Impute Features

  • Engineer Features

  • Select Features

  • Encode Features

  • Build Datasets

    • Machine Learning is math. In specific,
      performing Linear Algebra on Matrices. Our
      data values must be numeric.

• Model

  • Select Algorithm based on question and
    data available

• Cost Function

  • The cost function will provide a measure of how far my algorithm and
    its parameters are from accurately representing my training data.

  • Sometimes referred to as Cost or Loss function when the goal is to
    minimise it, or Objective function when the goal is to maximise it.

• Optimization

  • Having selected a cost function, we need a method to minimise the Cost function, or
    maximise the Objective function. Typically this is done by Gradient Descent or Stochastic
    Gradient Descent.

• Tuning

  • Different Algorithms have different Hyperparameters, which will affect the
    algorithms performance. There are multiple methods for Hyperparameter
    Tuning, such as Grid and Random search.

• Results and Benchmarking

  • Analyse the performance of each algorithms and
    discuss results.

  • Are the results good enough for
    production?

  • Is the ML algorithm training
    and inference completing in a
    reasonable timeframe?

• Scaling

  • How does my algorithm scales for both training and inference?

• Deployment and
  Operationalisation

  • How can feature manipulation be done for training and inference in real-time?

  • How to make sure that the algorithm is retrained periodically and deployed into production?

  • How will the ML algorithms be integrated with other systems?

• Operationalisation

• Infrastructure

  • Can the infrastructure running the machine learning process scale?

  • How is access to the ML algorithm provided? REST API? SDK?

  • Is the infrastructure adapter to the algorithm we are running? Should GPU’s be considered rather than CPUs’?

• Question

  • Is this A or B? 

    • Classification

  • How much, or how many of these? 

    • Regression

  • Is this anomalous?

    • Anomaly Detection

  • How can these elements be grouped? 

    • Clustering

  • What should I do now? 

    • Reinforcement Learning

• Direction

  • SaaS - Pre-built Machine Learning models

    • Google Cloud

      • Vision API

      • Speech API

      • Jobs API

      • Video Intelligence API

      • Language API

      • Translation API

    • AWS

      • Rekognition

      • Lex

      • Polly

    • … many others

  • Data Science and Applied Machine
    Learning

    • Google Cloud

      • ML Engine

    • AWS

      • Amazon Machine Learning

    • Tools: Jupiter / Datalab / Zeppelin

    • … many others

  • Machine Learning Research

    • Tensorflow

    • MXNet

    • Torch

    • … many others