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DL Fundamentals - Coggle Diagram
DL Fundamentals
Deep Learning
Hierarchical
Low features
Mid features
High features
Scalable
Types
RNN
Memory
Text
CNN
Images
RBM
Memory
DBN
Probability
RNTN
Memory
Object
for data
Unstructured
High dimensional
Supervised
Image recognition
Speech
Unsupervised
Feature recognition
Activation Function
ReLU
Rectified Linear Unit
Linear
Regression
Softmax
Classification
Problems
Vanishing Gradient
Sigmoid function
Dead neurons
ReLU
NN Types
Single layer
Multi layer
Fixed
Adaptive
Weight updates
FeedForward
Recurrent
Loops
Static
Dynamic
Memory
Artificial Neuron
Dendrites
Inputs
Weights
Cell Body
Summation
Threshold
Activation Function
Output
Neural Network
Layers
Input
Hidden
Counted
Output
Counted
Training
Loss function
error reduction
Algorithm
Gradient
Backpropagation
Forward pass
Backward pass
Gradient
Weightage x Error