Lesson 3

Lesson 3 Wiki

• Note: content starts at 45min *

• rectified linear unit(relu)- max(0,sum) in excel

Takes the on the positive unit which gives it the horizontal edges

• max pull

• tensor - is an array with more dimensions so we can stack kernals together

• they are multi dimensional

kernals/filters are being used on multiple the convulsions that are created before it

• Max Pooling- using 2 by 2 pooling we would have the half resolution(replace four (numbers) cells with the biggest number)

• Fully connected layer - sum product of every activation by each weight(using weight matrix). Don't use these much anymore

• Activation function - eg: softmax. Takes in 1 number spits out another number, nonlinearity find a better definition

Softmax binary classification (single label)

insert image of softmax eq

Multi label Image classification

Starts lesson3 3 1:20

Satellite imagery can have things many labels like weather, agriculture, primary (rain forest) water

Sigmoid use for multi variable classification(multiple label)

$$s(x)= \frac{e^x}{e^x+1}$$

learn.summary() shows us our model

Structure of Data

• unstructured -all the data is the same thing eg pictures, language, sounds waves

• structured - database type of structure

Structured data

Lesson 3- roossman starts at 2:03

fastai.structred for structured data can be used by itself without any fastai packages.

fast.ai columnar lets us work with columnar data

Check out

• keras lesson 1 to see lesson one written using keras
• multi dimensional array
• convolution slide
• logs rules and e