(BDT311) Deep Learning: Going Beyond Machine Learning

Chida Chidambaram Vishal Deshpande BDT311 Deep Learning Going Beyond Machine Learning October 2015 2. What to Expect from the Session Data analytics options on AWS Machine learning (ML) – high level Amazon ML from AWS ML sample use case Deep learning (DL) – high level DL sample use cases AWS GPU/HPCC server family Q&A 3. Data Analytics Options on AWS Amazon EMR AnalyzeStoreIngest Amazon Kinesis DynamoDB Amazon Redshift RDSS3 Amazon Kinesis Consumer Machine Learning Amazon Kinesis Producer Traditional Server Mobile Clients EC2 Machines 5. Machine Learning How can a machine identify Bruce Willis vs Jason Statham? Bruce Willis??? 6. Machine Learning Machine Learning Artificial Intelligence Optimization & Control Neuroscience and Neural Networks Statistical Modeling Information Theory 7. Machine Learning Bear Eagle People Sunset 8. Machine Learning • Using machines to discover trends and patterns and compute mathematical predictive models based on factual past data • ML models provide insights into likely outcomes based on the past – machine learning helps uncover the probability of an outcome in the future rather than merely state what has already happened in the past • Past data and statistical modeling is used to make predictions based on probability Where traditional business analytics aims at answering questions about past events, machine learning aims at answering questions about the possibilities of future events 9. Machine Learning Supervised learning Human intervention and validation required Photo classification and tagging Unsupervised learning No human intervention required Auto-classification of documents based on context 10. Machine Learning – Process How can a machine identify Bruce Willis vs Jason Statham? Image analysis – Input feature set for image 1 - bald, black suit Bruce Willis??? 14. Machine Learning – Process • Start with data for which the answer is already known • Identify the target – what you want to predict from the data • Pick the variables/features that can be used to identify the patterns to predict the target • Train the ML model with the dataset for which you already know the target answer • Use the trained model to predict the target on the data for which the answer is not known • Evaluate the model for accuracy • Improve the model accuracy as needed 15. Machine Learning – When to Use It You need ML if • Simple classification rules are inadequate • Scalability is an issue with large number of datasets You do not need ML if • You can predict the answers by using simple rules and computations • You can program predetermined steps without needing any data driven learning 16.