Takeaways

• Parts of a DL project
• Implementation
  • Tensorflow codes
• Basic DL concepts

Deep Learning

• Branch of machine learning

Tensorflow

• Software library to implement deep learning

• Open-source Software Framework
• Uses CPU or GPU (or TPU)
• Build, Train & Predict with Deep Learning

Tensorflow

What's

CPU

GPU

ALU

CONTROL

CACHE

Dataset

• Image
• Text
• Graphs
• etc

Model

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

Optimizer

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

'Rectangle' neuron

length

breadth

brightness

perimeter

length

breadth

Deep learning

input_1

input_2

input_3

output

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

of neural layer

Types

Dense layers

Dense Layer 1

Dense Layer 2

Example

• https://playground.tensorflow.org/

• https://colab.research.google.com
• mnist-basic_classification_final.ipynb: https://bit.ly/2H3mCga

Links

• https://slides.com/alvinchan/tensorflow-ntu-levelup

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

Why

GPU

Deep layers

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