Deep Learning: Getting Started

Introduction

• Getting started with deep learning
• Prerequisites for the course
• Setting up the environment

1. Introduction to Deep Learning

• What is deep learning?
• Linear regression
• An analogy for deep learning
• The perceptron
• Artificial neural networks
• Training an ANN

2. Neural Network Architecture

• The input layer
• Hidden layers
• Weights and biases
• Activation functions
• The output layer

3. Training a Neural Network

• Setup and initialization
• Forward propagation
• Measuring accuracy and error
• Back propagation
• Gradient descent
• Batches and epochs
• Validation and testing
• An ANN model
• Reusing existing network architectures
• Using available open-source models

4. Deep Learning Example 1

• The Iris classification problem
• Input preprocessing
• Creating a deep learning model
• Training and evaluation
• Saving and loading models
• Predictions with deep learning models

5. Deep Learning Example 2

• Spam classification problem
• Creating text representations
• Building a spam model
• Predictions for text

6. Deep Learning Exercise

• Exercise problem statement
• Preprocessing RCA data
• Building the RCA model
• Predicting root causes with deep learning

Conclusion

• Extending your deep learning education