I started Standford's machine learning course but after getting stuck in one assignment (ex4, Week5), it fell of the table due to lack of time and focus.

I will use this Hack Week to make some progress on it.

Day 1

  • Studied back-propagation algorithm again, starting Week 5 from scratch

Day 2

  • Focused in the homework itself, understanding what I did last time.
  • Cross checked vector dimensions, to understand better
  • Simplified the code by vectorizing the second part the whole thing
  • Solved a mystery with dimensions realizing that in order to calculate δ2, I need to ignore the first column of Θ2 (bias)
  • Some progress

                                     Part Name |     Score | Feedback
                                     --------- |     ----- | --------
                 Feedforward and Cost Function |  30 /  30 | Nice work!
                     Regularized Cost Function |  15 /  15 | Nice work!
                              Sigmoid Gradient |   5 /   5 | Nice work!
     Neural Network Gradient (Backpropagation) |  40 /  40 | Nice work! 
                          Regularized Gradient |   0 /  10 |
                                     --------------------------------
                                               |  90 / 100 |
    
  • Working now on the regularized gradient. Homework additional material was helpful to understand the restriction of j=0

                                     Part Name |     Score | Feedback
                                     --------- |     ----- | --------
                 Feedforward and Cost Function |  30 /  30 | Nice work!
                     Regularized Cost Function |  15 /  15 | Nice work!
                              Sigmoid Gradient |   5 /   5 | Nice work!
     Neural Network Gradient (Backpropagation) |  40 /  40 | Nice work!
                          Regularized Gradient |  10 /  10 | Nice work!
                                     --------------------------------
                                               | 100 / 100 |
    
  • Continued with Week 6

    • Model Selection
    • Ways to diagnose bias vs variance by separating data into cross-validation and test sets

Day 3

  • Used on another Hackweek project

Day 4

  • Continued with model selection and bias/variance vs other parameters

Day 5

  • Finished Week 6 material. Debugging models and strategies to attack high bias or high variance
  • Quiz for Week 6. 100%
  • Started homework (ex5)
  • Implemented Regularized Linear Regression Cost Function and Regularized Linear Regression Gradient

Looking for hackers with the skills:

machinelearning learning

This project is part of:

Hack Week 19

Activity

  • about 2 months ago: dmacvicar added keyword "machinelearning" to Finish Machine Learning introductory course
  • about 2 months ago: dmacvicar added keyword "learning" to Finish Machine Learning introductory course
  • about 2 months ago: dmacvicar started Finish Machine Learning introductory course
  • about 2 months ago: dmacvicar originated Finish Machine Learning introductory course

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