Summary from the Stanford's Machine learning class by Andrew Ng
 Part 1
 Supervised vs. Unsupervised learning, Linear Regression, Logistic Regression, Gradient Descent
 Part 2
 Regularization, Neural Networks
 Part 3
 Debugging and Diagnostic, Machine Learning System Design
 Part 4
 Support Vector Machine, Kernels
 Part 5
 Kmeans algorithm, Principal Component Analysis (PCA) algorithm
 Part 6
 Anomaly detection, Multivariate Gaussian distribution
 Part 7
 Recommender Systems, Collaborative filtering algorithm, Mean normalization
 Part 8
 Stochastic gradient descent, Mini batch gradient descent, Mapreduce and data parallelism
Regularization
 Problem of Overfitting
 Options to address overfitting:
 Reduce number of features
 Manually select which features to keep
 Model selection algorithm (later in course)
 Regularization
 Keep all features but reduce magnitude/values of parameters theta.
 works well when we have lots of features, each of which contributes a bit to predicting y.
 Reduce number of features
 Too much regularization can “underfit” the training set and this can lead to worse performance even for examples not in the training set.
 Cost Function
 if lamda is too large (e.g. 10^{10}) than the algorithm results in “underfitting”(fails to fit even training set)
 Regularization with Liner Regression
 Normal Equation
 Regularization with Logistic Regression
Neural Networks

Introduction

Algorithms that try to mimic the brain. Was very widely used in 80s and early 90s; popularity diminished in late 90s.

Send a signal to any brain sensor and it will learn to deal with it. E.g. Auditory cortex learns to see, Somatosensory cortex learn to see.
Seeing with your tongue, human echolocation, third eye for frog.

 Model Representation
 Forward propagation
 Nonlinear classification example: XOR/XNOR
 Nonlinear classification example: AND
 Nonlinear classification example: OR
 XNOR
 Multiclass classification
 Cost Function
 Unlike logistic regression we DO NOT sum the value of “Bias Unit” in the regularization term for cost of neural networks.
 Just as logistic regression a large value of “lamda” will penalize large parameter values, thereby, reducing the changes of overfitting the training set.
 Backpropagation algorithm
 Unrolling parameters
 Gradient Checking
 There may be bugs in forward/back propagation algorithms even if the cost function looks correct.
 Gradient checking helps identify these bugs.
 Implementation Note:
 Implement backprop to compute DVec (unrolled ).
 Implement numerical gradient check to compute gradApprox.
 Make sure they give similar values.
 Turn off gradient checking. Using backprop code for learning
 Be sure to disable your gradient checking code before training your classifier. If you run numerical gradient computation on
every iteration of gradient descent (or in the inner loop of costFunction(…))your code will be very slow.
 Random initialization
 Initializing theta to 0 works for logistic regression but it does not work for neural network.
 If we initialize theta to 0 than for neural network, after each update, parameters corresponding to inputs going to each of two hidden units are identical.
 This causes the “Problem of Symmetric Weight”
 To solve this issue randomly initialize the theta values.
 Training a neural network
No comments:
Post a Comment