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How_to_use_Tensorflow_for_classification-LIVE

This is the code for the "How to Use Tensorflow for Classification" live session by Siraj Raval on Youtube

##Overview

This is the code for this live session by Siraj Raval on Youtube. We'll build a classifier for houses. The housing data contains features for each house like # of bathrooms, price, and area. We'll manually add labels to our data (good buy or bad buy) then given a new house, we'll predict whether or not it's a good buy or bad buy. We use gradient descent as our optimization strategy and

##Dependencies

Install dependencies using pip Install jupyter notebook using this

##Usage

type juptyer notebook into terminal and a browser window will pop up. Click on demo.ipynb. You can iteratively compile each block of code to see the output results.

##Credits Credits for the code go to jalammar. I've merely created a wrapper to get people started.

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how_to_use_tensorflow_for_classification-live's Issues

Should n_samples be 10 or 20?

The code says "n_samples = inputY.size" which means n_samples is 20. Because inputY is 10 * 2 matrix.

image

Do we expect samples to be 20? Or should it be 10 because we only use 10 lines of training data?

Weights and biases.

Why are we creating the matrix 'weight' of 2 dimensions i.e 2*2 when there are only 2 features, like we did for the biases?

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