The speaker will provide a primer on Deep Learning. The following topics will be covered:
1) What is deep learning?
2) What is deep learning capable of and what are the limits of deep learning in terms of technological advancement?
3) How is deep learning related to machine learning and artificial intelligence?
4) How did deep learning originate and progress to it's present state?
5) How does deep learning work?
6) How can deep learning lead to the automation of intellectual and non-intellectual tasks and processes?
7) What are some barriers to entry and how can these barriers to entry be overcome?
Handling real-time data has become a critical capability for data-driven organizations. However, today’s reality is a
disconnected patchwork of incomplete technologies that make it a struggle to deliver real-time solutions because of
frustrating complexity, inefficiency, and incompleteness. In this talk, we address these challenges with an unified solution for real-time data. An end-to-end real time system needs
• Messaging: receive and distribute streaming data with support for publish-subscribe and queuing scenarios with built-in durability, scalability, and performance using the Apache Pulsar (incubating) messaging solution.
• Processing: process data transformations and analytics with the Heron real-time processing engine, built for performance and scalability.
• Storage: leverage Apache BookKeeper streaming log storage to ensure durability, resiliency, and performance for streaming data.
In our talk, we will provide an overview of the underlying three systems and how they are used the core for a unified end-to-end real time solution.
Functional programming finds its roots in mathematics - the pursuit of purity and completeness. We functional programmers look to formalize system behaviors in an algebraic and total manner. Despite this, when it comes time to deploy ones beautiful monadic ivory towers to production, most organizations cast caution to the wind and use a myriad of bash scripts and sticky tape to get the job done. In this talk, the speaker will introduce you to Nelson, an open-source project from Verizon that looks to provide rigor to your large distributed system, whilst offering best-in-class security, runtime traffic shifting and a fully immutable approach to application lifecycle. Nelson itself is entirely composed of free algebras and coproducts, and the speaker will show not only how this has enabled development, but also how it provided a frame with which to reason about solutions to fundamental operational problems.
This work arises from real-world experience working with Slick and legacy database schemas. The feelings of safety of having a database schema described in Scala are sometimes dwarfed by the pain of actually having to write and maintain the Scala mappings. This becomes obvious when building a new project with an existing database.
The code generation component is bundled with Slick. However, the documentation and default implementation show only the most trivial use-case and require a more complecated build. With every new project we have updated and expanded on the use-cases the plugin covers. Now, we would like to share what we have built and learned.
