Session 1 (2 hours): Foundation of Deep Learning

- Summary of Machine Learning to segue to Deep Learning

- Understanding a problem and its data

- Formulating a solution

- Loss function

- Model fitting

- Develop "Hello World" Deep Learning Model

- Deconstruct and understand the operations in Multilayer Perceptron

- Backpropagation Method

- Hands-on development

- Q&A

Session 2 (2 hours): Advanced models in Deep Learning

- LSTM Models

- Fundamentals of LSTM

- Memory cells

- Information flow

- Hands-on modeling

- CNN Models

- Fundamentals of CNN

- Filtration process

- Properties that make CNN standout

- Hands-on modeling

- Q&A

Session 1 (2 Hours): Image Data and Machine Learning

- Image data fundamentals

- Image processing

- Manipulation using OpenCV Python

- Data augmentation using Pytorch Python

- Machine learning introduction

- Hands on live-coding

- Q&A

Session 2 (2 hours): Deep Learning for Images

- Deep Learning Introduction

- Deep Learning for Images using Pytorch

- Image Classification

- Image Segmentation

- State of the art overview

- GAN models using images

- Practical applications

- Hands on live-coding

- Practical modeling and application tips and tricks

- Q&A

• Background of Cloud Computing in AWS (Amazon Web Services)

• Introduction to Machine Learning Development in Amazon SageMaker

• Create your own AWS Account for continuous hands-on exercise

• Python Jupyter Notebooks

- show and create examples of simple typical notebooks

• Modeling in SageMaker

- Hands-on with real-data

- Introduction to special features and algorithms

- Data Preprocessing and Feature Engineering using Amazon SageMaker Data Wrangler

- Import from various data sources (add examples)

- Data exploration and visualization

- Cleansing (outliers, missing values)

- Normalize; standardize; transform; scale; time synchronization

- Estimate model accuracy

• Hyper Parameter Optimization: Amazon SageMaker Automatic Model Training and Tuning

- Automatically finding the best model

- Three different ways of doing

• Amazon SageMaker Studio: IDE for Machine Learning

- Automatically build and train Models: Amazon SageMaker AutoPilot

- Learn the IDE for ML to automatically transform data, build, train, debug, deploy, and monitor your ML models

• Amazon SageMaker Experiments: Organize and Keep Track of Modeling Results

- Organize, track, compare, and evaluate your ML experiments

- Observe the impact of incremental changes on model accuracy