Duration
2 days
Who should attend
- Data Scientists
- Data Analysts
- Machine Learning Engineers and Developers
What you will learn
- Deep Neural Network (DNN) and Convolutional Neural Network (CNN)
- Image Recognition and Classification with Fast.ai
- Tuning hyper-parameters to improve DNN and CNN
- Fast.ai for structured/tabular data, e.g. time-series sales data
- Recurrent Neural Network (RNN)
Course Outline
Artificial Neural Network
- Introduction to Neural Networks
- Introduction to Perceptron
- Neural Network Activation Functions
- Basic Neural Nets
Introduction to Deep Learning
- What is Deep Learning?
- Relationship between Deep Learning and Machine Learning
- Deep Learning Use cases
- Concepts and Terms
- How to implement Deep Learning?
Introduction to Fast.ai
- What is Fast.ai
- Why and Why Not Fast.ai
- Setup Google Golab
- Install Fast.ai
- Basic Fast.ai Codes
Computer Vision
- Vision Module
- Fast.ai Approach to Computer Vision
- Image Classification for Pet Breeds (sample project)
- Transfer Learning
- Learning Rate Tuning
- Image Classification for Cats and Dogs (sample project)
- Confusion Matrix
- Data Augmentation
- Min Project
Natural Language Processing
- Text Module
- Fast.ai Approach to NLP
- Text Classification for Sentiment Analysis
- Language Model
- Fine Tune Text Classifier
- Improve Language Model
Recap Fundamental Concepts
- Neural Network
- Convolutional Neural Network
- Transfer Learning
- Recurrent Neural Network
Programme scope, delivery arrangements and suitability are confirmed during enquiry.