Neural Networks Tutorial
[Pages:23]CSC411 Tutorial #5 Neural Networks
Oct, 2017 Shengyang Sun ssy@cs.toronto.edu
*Based on the lectures given by Professor Sanja Fidler and the prev. tutorial by Boris Ivanovic, Yujia Li.
High-Level Overview
? A Neural Network is a function! ? It (generally) comprised of:
? Neurons which pass input values through functions and output the result
? Weights which carry values between neurons
? We group neurons into layers. There are 3 main types of layers:
? Input Layer ? Hidden Layer(s) ? Output Layer
High-Level Overview
Neuron Breakdown
weight
x1 w1
X2
w2
w3 x3
neuron
activation
Neuron Breakdown
b
Activation Functions
Most popular recently for deep learning
Representation Power
What does this mean?
? Neural Networks are POWERFUL, it's exactly why with recent computing power there was a renewed interest in them. BUT
? "With great power comes great overfitting."
? Boris Ivanovic, 2016
? Last slide, "20 hidden neurons" is an example.
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