General DescriptionRust is great for building mathematical models and AI which is the talk of the hour and a great emerging domain is based on mathematics which can crunch realtime data to provide human level insights which helps in making logical decisions.
There are already many crates in rust which provide AI units which help in implementing deep learning and reinforcement learning systems and algorithms.
Proposals basically deep dives into such implementations in rust and covers the benchmarks of performance of the rust crates with the industrial grade libraries such as tensor flow and caffe. The Talk touches upon the major problems in the industrial grade libraries and how rust can help in avoiding them.
Demos of few mathematical models to perform prediction tasks and how to start off development of models from scratch in rust will be demonstrated.
- Introduction to AI
- Current AI crates
- Demo on Male and female detector
- Template for mathematical model building
- Benchmarks of Rust Crates Areas of improvements and rust unique offerings
- Learn to build your first machine learning program in rust
- Template for creating mathematical implementation from scratch
- Benchmarks of rust ML crates
- Learn the different benchmark parameters
Vigneshwer Dhinakaran Vigneshwer
Vigneshwer is an innovative data scientist with an artistic perception of technology and business, having 3+ years of experience in various domains such as IOT, DevOps, Computer Vision & Deep Learning and is working as a research analyst crunching real-time data and implementing state of art AI algorithms in the Innovation and development lab of the world’s largest decision science company in Bengaluru, India.