This presentation gives a very simple introduction to deep learning in addition to a step-by-step example showing how to solve the XOR non-linear problem using multi-layer artificial neural networks that has both input, hidden, and output layers.
Deep learning is based on artificial neural networks and it aims to analyze large amounts of data that are not easily analyzed using conventional models. It creates a large neural network with several hidden layers and several neurons within each layer and usually may take days for its learning.
Many beginners in artificial neural networks have a problem in understanding how hidden layers are useful and what is the best number of hidden layers and best number of neurons or nodes within each layer.
أحمد فوزي جاد Ahmed Fawzy Gad
قسم تكنولوجيا المعلومات Information Technology (IT) Department
كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI)
جامعة المنوفية, مصر Menoufia University, Egypt
Teaching Assistant/Demonstrator
ahmed.fawzy@ci.menofia.edu.eg
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Brief Introduction to Deep Learning + Solving XOR using ANNs
1. Brief Introduction to Deep Learning +
Solving XOR using ANN
MENOUFIA UNIVERSITY
FACULTY OF COMPUTERS AND INFORMATION
المنوفية جامعة
الحاسبات كليةوالمعلومات
المنوفية جامعة
Ahmed Fawzy Gad
ahmed.fawzy@ci.menofia.edu.eg
69. Activation Functions
Which activation function to use?
Outputs
Class
Labels
Activation
Function
TWO Class
Labels
TWO
Outputs
One that gives two outputs.
Which activation function to use?
𝑪𝒋𝒀𝒋
BA
01
1
10
00
0
11
BA
01
1 10
00
0 11
83. Neural Networks Training Steps
Weights Initialization
Inputs Application
Sum of Inputs-Weights Products
Activation Function Response Calculation
Weights Adaptation
Back to Step 2
1
2
3
4
5
6
84. Regarding 5th Step: Weights Adaptation
• If the predicted output Y is not the same as the desired output d,
then weights are to be adapted according to the following equation:
𝑾 𝒏 + 𝟏 = 𝑾 𝒏 + η 𝒅 𝒏 − 𝒀 𝒏 𝑿(𝒏)
Where
𝑾 𝒏 = [𝒃 𝒏 , 𝑾 𝟏(𝒏), 𝑾 𝟐(𝒏), 𝑾 𝟑(𝒏), … , 𝑾 𝒎(𝒏)]
85. Neural Networks
Training Example
Step n=0
• In each step in the solution, the parameters of the neural network
must be known.
• Parameters of step n=0:
η = .001
𝑋 𝑛 = 𝑋 0 = +1, +1, +1,1, 0
𝑊 𝑛 = 𝑊 0 = 𝑏1, 𝑏2, 𝑏3, 𝑤1, 𝑤2, 𝑤3, 𝑤4, 𝑤5, 𝑤6
= −1.5, −.5, −.5, 1, 1, 1, 1, −2, 1
𝑑 𝑛 = 𝑑 0 = 1
BA
01
1 => 1
10
00
0 => 0
11