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Week 1 - Summary

February 06, 2017

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activation function
addpath
Advanced Optimization
and logical
andrew ng
artificial intelligent
ascii
assertion
assign
associative

attribute
Automatic convergence test
axis
axon
backpropagation
Backpropagation Algorithm
backward pass
batch gradient descent
bfgs
bias unit
bias vs variance
binary classification problem
bugs
category
choice
choosing features
classification
classification problem
clear
clf
close
cocktail party problem
column vector
command
commutative
computation graph
computational graph
concatenate
conjugate gradient
continuous valued output
contour plot
control statement
converge
convergence
convex
convex function
cost function
coursera
cross section
cross validation
cross validation set
data mining
data set
dataset
debug
Deciding what to try next
decision boundary
dendrite
derivative
dimension of vector
discrete valued output
dpng
element-wise
email
email foldering
email routing information
Error Analysis
error gap
estimated probability
Evaluating Hypothesis
evaluation metric
example
experience
eye
F score
F1 score
face recognition
false negative
false positive
feature
feature scaling
features
figure
forward pass
Forward Propagation
function
gap
global minimum
Gradient Checking
Gradient Descent
group
help
hidden layer
high bias
high variance
hist
hold on
honeypot
housing price
human brain
hypothesis
Hypothesis Representation
identity matrix
imagesc
improvement
index
input
input layer
intuition
inverse
iteration
l-bfgs
label
large margin classification
large margin classifier
Large Margin Intuition
leaky relu
learni
learning algorithm
Learning Curves
learning rate
legend
length
linear algebra
linear regression
Linear regression with one variable
linearly dependent
load
local minimum
local optimum
log function
logical
logistic function
logistic regress
logistic regression
logistic unit
loop
loss function
machine learning
Machine learning diagonostic
manually examine error
matrix
matrix addition
matrix matrix multiplication
matrix vector multiplication
mean
mean normalization
mean squared error
medical diagram
minimize
minus
mispelling
Model Representation
Model Selection
model selection algorithm
model selection problem
multiclass
multiclass classification
multiple variables
multivariate linear regression
negative class
neural network
Neural Networks
neuron
new ability
non-convex
non-linear
noninvertibility
Normal Equation
number of features
number of iterations
numerical evaluation
numerical gradient checking
octave
one variable
one vs all
ones
online transaction
or logical
outlier
output
output layer
overfitting
parabola
parameter
partial derivative
pattern
performance
placeholder
plot
plotting data
polynomial
polynomial regression
positive class
pre-mature optimization
precision
precision recall
prompt
pwd
python tips
quadratic
Random Initialization
range
rank
rank 1 array
rare class
recall
redundant features
regression problem
regularization
regularization parameter
Regularized Linear Regression
Regularized Logistic Regression
relu
reshape
right answers
row vector
sample size
save
scalar multiplication
self-customizing program
semicolon
shape
sigmoid
sigmoid derivative
sigmoid function
similar range of values
similar scale
Simplified Cost Function
size
skewed class
slope
small learning rate
spam classifier
square error function
square matrix
square root
standard deviation
steepest descent
stemming
structure in data set
subplot
supervised learning
Support Vector Machine
svm
svm decision boundary
tangent line
tanh
target
task
tensor
test set
title
too many features
trade off
training set
transpose
tru positive
true negative
tumor
underfitting
Univariate linear regression
Unrolling Parameters
unsupervised learning
validation set
variable
vector
vector support machine
vectorization
vectorized implementation
weather
week 1
weight
what is machine learning
whos
XNOR
zero mean
zeros

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