# Deep Learning - Introduction

Deep learning
Introduction for how to learn machine leaning and deep learning
List of course
Basic concept
Andrew Ng’s machine learning1
Technique
Python
anaconda
Tensorflow
Hands on
MINST
CNN
RNN
What can you learn
an understanding of machine l...

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# Machine Learning - Andrew NG in Coursera(Cont.)

Linear Regression with multiple variables
Multiple features
Notation: n = number of features, x(i) = input (features) of ith training example. x(i)j = value of feature j in ith training example.
hθ(x) = θ0 + θ1x1 + θ2x2 + … + θnxn
Feature Scaling
Idea: make sure features are on a simular scale.
E.g. x1 = size(0-2000 f...

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# Machine Learning - Linear Algebra

Linear Algebra review
Fundation of machine leaning(Matrices and vectors)
Matrix
Rectangular array of numbers
Dimension of matrix number of rows x number of columns
Vector
An n x 1 matrix
Scalar multiplication
Matrix Addition: you can only add two same dimension matrix
Multiplication: scalar number multiply matrix will ...

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# Machine Learning - Andrew NG in Coursera

Machine learning
What is machine leaning
Definition
Arthur Samuel(1959)
Field of study that gives computers the ability to learn without being explicitly programmed.
Tom Mitchell(1998)
A computer program is said to learn from experience E with respect to some task T and some performance measure P, if it...

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# Big Data - Start with data ware house

Big Data and Data Warehouse
Evolution of big data from data warehouse
Conception
What is big data
Definition of big data
Data processing
What is Data warehouse
Inmon or Kimball
Dimensional modeling
Star Schema vs. Snowflake Schema
ETL extract, transform and load
DW new ...

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