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:

#### This project is part of:

Hack Week 19

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