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14101595
Deep Learning Essentials
Description
Deep Learning Modul - University of Oldenburg
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oldenburg
kramer
artificial intelligence
deep learning
neu
neural network
computational intelligence
ci
ai
university
Mind Map by
Mark Otten
, updated more than 1 year ago
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Created by
Mark Otten
over 6 years ago
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Resource summary
Deep Learning Essentials
7 - Convolutional Networks
Handling
Layer
Activation
Padding
k = (d-m+2p)/s+1
Annotations:
Wird noch korrigiert. Falsche Formel
Stride
Employes a vertical and horizonal axis
The number of steps a kernel is moved over the input activation matrix is called stride s.
Pooling
reduce the dimensionlity
Convolution
AlexNET - 2012
6 - Model Assessment
5 - Model Training
4 - Weight Adaptation
3 - Multilayer Perceptron
2 - Linear Models
Supervised Learning
Learning with labels
each pattern x has a label information y
pair (x_i, y_i)
training set
ground truth
pair (x'_i, y'_i)
predict set
If the label is discrete, e.g., {0, 1} or {muffin, chihuahua}, the learning problem is called classification
classificatoin
if hoices is explored ol detection. continuous First, (y ∈ R) it is called regression.
regression
Linear Regression
found in natural and technical processes
The basic linear model (1)
x € IR
weight factor
w € IR called slope
parameter
b € IR called inter
linear relationship
Least Squares
With First, the least squares formulation, the coefficients can be derived. (2)
means squared error (MSE)
Linear Regression Coefficients
Weight and intercept can be mathematically derived as (3)
x Strich mens x_1 ... x_n and the same for the label y Strich
Example Fit, Illustration of linear model that is fiied to the patterns minimizing the MSE
Nearest Neighbors
K-nearest neighbors (kNN) searches for labels based on nieghborhoods in data space.
1 - Introduction
A.I.
Intelligence is
learn from observations
others experiences
own experiences
related to
Data Science
Big Data
Technologies
deep learning
TFlearn
Keras
Tensorflow
mashine learning
scikit-learn
share on github
devlopment of scripts
Jupyter
Research
archivx
Powerfull Hardware
AWS
NVIDIA GRPUs
8 - Neuroevolution
Genetic Alogrithm
mimicking biological evolution
Crossover
Mutation
Selection
Optimization
examples
9 - Auto-encoder
10 - Generative Adversarial Networks
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