Deep Learning Essentials

Mark Otten
Mind Map by Mark Otten, updated more than 1 year ago
Mark Otten
Created by Mark Otten about 2 years ago


Deep Learning Modul - University of Oldenburg

Resource summary

Deep Learning Essentials
1 7 - Convolutional Networks
1.1 Handling
1.2 Layer
1.2.1 Activation
1.2.2 Padding k = (d-m+2p)/s+1


  • Wird noch korrigiert. Falsche Formel
1.2.3 Stride Employes a vertical and horizonal axis The number of steps a kernel is moved over the input activation matrix is called stride s.
1.3 Pooling
1.3.1 reduce the dimensionlity
1.4 Convolution
1.5 AlexNET - 2012
2 6 - Model Assessment
3 5 - Model Training
4 4 - Weight Adaptation
5 3 - Multilayer Perceptron
6 2 - Linear Models
6.1 Supervised Learning
6.1.1 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
6.2 Linear Regression
6.2.1 found in natural and technical processes
6.2.2 The basic linear model (1) x € IR weight factor w € IR called slope parameter b € IR called inter linear relationship
6.2.3 Least Squares With First, the least squares formulation, the coefficients can be derived. (2) means squared error (MSE)
6.2.4 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
6.2.5 Example Fit, Illustration of linear model that is fiied to the patterns minimizing the MSE
6.3 Nearest Neighbors
6.3.1 K-nearest neighbors (kNN) searches for labels based on nieghborhoods in data space.
7 1 - Introduction
7.1 A.I.
7.1.1 Intelligence is learn from observations others experiences own experiences
7.1.2 related to Data Science Big Data
7.2 Technologies
7.2.1 deep learning TFlearn Keras Tensorflow
7.2.2 mashine learning scikit-learn
7.2.3 share on github
7.2.4 devlopment of scripts Jupyter
7.2.5 Research archivx
7.2.6 Powerfull Hardware AWS NVIDIA GRPUs
8 8 - Neuroevolution
8.1 Genetic Alogrithm
8.1.1 mimicking biological evolution Crossover Mutation Selection
8.1.2 Optimization
8.1.3 examples
9 9 - Auto-encoder
10 10 - Generative Adversarial Networks
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