DAY 3: Hands-On Machine Learning

I continued reading the Hands-On Machine Learning book while taking notes on skills applicable to my internship project

• Chapter 4
  • Gradient descent → algorithm that finds the optimal solution
  • Polynomial regression → using a linear model to fit nonlinear data
  • Logistic regression → estimates the probability that X belongs to a positive class (1 if positive, 0 if negative)
• Chapter 5
  • Decision function → predict if its a positive class (1) or negative (0)
  • Support vector machine → machine learning model → supports linear and nonlinear
    • Large margin classification: fitting the widest possible street between the classes
    • Soft margin: more flexible, works with outliers
    • To use SVMs for regression instead of classification, try to fit as many instances on the street (instead of trying to fit the largest possible street between two classes)
  • Try to minimize w to get a large margin
  • Quadratic programming → quadratic optimization problems with linear constraints
• Chapter 11: Training Deep Neural Networks
  • Used for detecting hundreds of types of objects in images
  • Vanishing/exploding gradients problem
    • Gradients get bigger and bigger, or smaller and smaller
    • Using He initialization along with ELU can reduce the danger of vanishing/exploding gradients, but they can still come back during training
      • Batch normalization addresses the issue
        • Standardizes inputs, then rescales and offsets them
  • Reusing the same model when the tasks are similar
  • You don’t have much labeled training data
    • Unsupervised pre training
    • Train a first neural network on an auxiliary task → easy to get labeled training data
      • Facial recognition
        • Gather a lot of pictures of random people, and train a neural network to detect whether or not two pictures are the same person
        • Reusing lower layers lets you train a face classifier with little data

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DAY 2: Machine Learning Training

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Today, I continued reading the Hands on Machine Learning book. Chapter 2 went over a simulation of a housing market machine learning model that identifies worthwhile investments based on the location, number of rooms, size, etc. Chapter 3 went into specifics about measuring performance through cross-validation, a confusion matrix, precision and recall, and the ROC curve. I also learned about multiclass (multinomial) classifiers and binary classifier training.

DAY 1: Machine Learning Tutorials

This week, my main goal is to read through the applicable chapters of the Hands on Machine Learning book, TensorFlow 2 Quickstart, and TensorFlow Tutorials Convolutional Neural Network while simultaneously running the corresponding code on Jupyter.

Today, I worked through Chapter 1 and Chapter 2 of the Machine Learning book, which walked through building a sample machine learning model through utilizing housing information to simulate a real-world scenario.