Dataset

• Image
• Text
• Graphs
• etc

Model

• With layers of 'neurons'
  • Dense layer
  • Convolutional layer
  • Recurrent layer
  • etc

Optimizer

• Adjust learned 'knowledge'
• Examples
  • SGD
  • ADAM

A neuron

'Rectangle' neuron

length

breadth

brightness

perimeter

length

breadth

Deep learning

input_1

input_2

input_3

output

Relu Activation Function

• Dense Layer
• Convolutional Layer
• Recurrent Layer
• Many more..

of neural layer

Types

Dense layers

Dense Layer 1

Dense Layer 2

Convolutional layers

input_1

input_2

input_3

output_1

input_4

input_5

Convolutional layers

input_1

input_2

input_3

output_1

input_4

input_5

output_2

Step 2

Convolutional layers

input_1

input_2

input_3

input_4

input_5

output_3

step 3

output_1

output_2

Deep learning for images

32 px

32 px

Input Image

CNN Kernel

Stride = 1

Same Padding

Max Pooling

Deep layers

Source: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture6.pdf

CNN Neurons

• https://distill.pub/2018/building-blocks/

Size & Accuracy

Inception

Residual Connection

NASNet