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ML Starter Pack

ML Starter Pack

Presented at GDG Waterloo.

Basics of ML

Charmi Chokshi

April 22, 2023
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Transcript

  1. I'm Charmi! • ML Grad at Mila and UdeM •

    GDE for ML • International Speaker • Worked @ AWS, Shipmnts, ISRO • Tech, Art, Travel
  2. Plan of the Day! • What is Machine Learning? •

    Types of ML • Supervised and Unsupervised techniques • Classification and Regression • Basics of Deep Learning • Basics of NLP, CV, ASR • The 3D ML Pipeline • Fun Applications
  3. Artificial Intelligence Deep Learning Machine Learning Any Technique that enables

    computers to mimic human intelligence & behaviour A subset of ML exposing multilayered neural networks to vast amount of data A subset of AI that includes statistical techniques to solve the tasks using experience
  4. Basic Terminologies • Features • Labels • Examples ◦ Labelled

    example ◦ Unlabelled example • Data Split (Train, Valid, Test) • Models (Train and Test) ◦ Classification model ◦ Regression model
  5. Supervised Learning • Supervised Learning deals with prediction of values

    based on given combinations of data and labels given beforehand • ML systems learn how to combine input to produce useful predictions on never-before-seen data • It is like learning with a teacher
  6. Regression and Classification • A regression model predicts continuous values

    by fitting a line ◦ What is the value of a house in California? ◦ What is the probability that a user will click on this ad? • A classification model predicts discrete values by creating boundaries ◦ Is a given email message spam or not spam? ◦ Is this an image of a dog, a cat, or a hamster?
  7. Overfitting vs Underfitting • An overfit model gets a low

    loss during training but does a poor job predicting new data • Overfitting is caused by making a model more complex than necessary • The fundamental tension of machine learning is between fitting our data well, but also fitting the data as simply as possible
  8. Unsupervised Learning • It deals with clustering values or forming

    groups of values • One aims to infer patterns from the data rather than predicting values • It is like learning on your own
  9. When to use or not use DL? • Deep Learning

    outperforms other techniques if the data size is large. But with small data size, traditional Machine Learning algorithms are preferable • Finding large amount of “Good” data is always a painful task • Deep Learning techniques need to have high end infrastructure to train in reasonable time • When there is lack of domain understanding for feature introspection, Deep Learning techniques outshines others as you have to worry less about feature engineering
  10. When to use or not use DL? • Model Training

    time: a Deep Learning algorithm may take weeks or months whereas, traditional Machine Learning algorithms take few seconds or hours • DL never reveals the “how and why” behind the output- it’s a Black Box • Deep Learning really shines when it comes to complex problems such as image classification, natural language processing, and speech recognition • DL excels in tasks where the basic unit (pixel, word) has very little meaning in itself, but the combination of such units has a useful meaning
  11. Natural Language Processing • Sentiment analysis • Chatbots • Machine

    translation • Speech recognition • Text summarization
  12. Computer Vision • Facial recognition • Object detection • Medical

    imaging • Autonomous vehicles • Segmentation
  13. Speech Understanding • Voice-controlled personal assistants • Virtual agents •

    Dictation software • Closed captioning • Subtitling for movies • Transcribing
  14. Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation

    Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  15. Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation

    Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  16. Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation

    Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  17. Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation

    Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  18. Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation

    Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  19. Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation

    Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  20. Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation

    Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  21. Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation

    Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  22. Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation

    Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  23. Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation

    Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  24. Data acquisition Model Deployment Data Cleaning Feature Engineering Model Validation

    Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning