Zusammenfassung der Ressource
Deep Learning Essentials
- 7 - Convolutional Networks
- Handling
- Layer
- Activation
- Padding
- k = (d-m+2p)/s+1
Anmerkungen:
- 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