一天搞懂机器学习PPT笔记-1

Introduction of Deep Learning

some introductions

Machine Learning is close to Looking for a Function
The model is a set of function
a set of function -> goodness of functin F <- training data
framework
– get a model consists of a set of function
– through training data’s training to get the goodness of function f
– pick the best function f*
three steps for deep learning
– define a set of function(is also called neural network)
– goodness of function
– pick the best function
Neural Network
– different connections leads to differeent network structure
– each neurons can have different values of weights and biases,weights and biases are network parameters

– deep learning means many hiden layers,each node means a function consists of weights and bias
output layer
– the output of network can be any value and may not be easy to interpret

– the value y is the probability of each output value and you need to decide the network structure to let a good function in your function set

training data

preparing the training data:images and their labels.The learning target is defined on the training data
a good function should make the loss of all examples as small as possible

pick the best function

the target is to find network parameters weights* and bisa* that minimize the total loss value L
how to find the best parameters:

– different initial parameters->reach different minima,so different results,so how to choose the init parameters is important

Why Deep?

Deeper is Better?

actualy,more parameters,better performance
the thin+tall neural network is better than the fat+short.
deep->modularization->less training data

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一天搞懂机器学习PPT笔记-1

Introduction of Deep Learning

some introductions

training data

pick the best function

Why Deep?

Deeper is Better?

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