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Finished Machine Learning Course - What's next?

April 09, 2017

andrew ng coursera machine learning

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Welcome to Machine Learning

February 05, 2017

andrew ng coursera machine learning

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Huy Hoang-Nguyen

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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
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binary classification problem
bugs
category
choice
choosing features
classification
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clear
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cocktail party problem
column vector
command
commutative
computation graph
computational graph
concatenate
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continuous valued output
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convex
convex function
cost function
coursera
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debug
Deciding what to try next
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discrete valued output
dpng
element-wise
email
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email routing information
Error Analysis
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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
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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
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intuition
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iteration
l-bfgs
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large margin classifier
Large Margin Intuition
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learning algorithm
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legend
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linear algebra
linear regression
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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
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new ability
non-convex
non-linear
noninvertibility
Normal Equation
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output
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polynomial
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Random Initialization
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Regularized Linear Regression
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relu
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right answers
row vector
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scalar multiplication
self-customizing program
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similar range of values
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Simplified Cost Function
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spam classifier
square error function
square matrix
square root
standard deviation
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subplot
supervised learning
Support Vector Machine
svm
svm decision boundary
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tensor
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Univariate linear regression
Unrolling Parameters
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validation set
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vectorization
vectorized implementation
weather
week 1
weight
what is machine learning
whos
XNOR
zero mean
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