Deep reinforcement learning : Starcraft learning environment by Gema Parreño at Big Data Spain 2017

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A theorical description of reinforcement learning principles and a deep dive into DeepMind Research environment . https://www.bigdataspain.org/2017/talk/reinforced-learning-deepmind-starcraft-learning-environment Big Data Spain 2017 November 16th - 17th Kinépolis Madrid

sTARcRAFT ii
Deep Reinforced Learning
Gema Parreño Piqueras @SoyGema

Classify
Machine
Learning by
learning method

Reinforce Learning
LEARNING WHILE
INTERACTING WITH
ENVIRONMENT

Policy Architecture
Input
SPATIAL/ NON
SPATIAL FEATURES

Policy Architecture
Output
SPATIAL/ NON
SPATIAL ACTION
POLICY

Thanks!
Deep Reinforced Learning
Gema Parreño Piqueras @SoyGema

Short walk-through on building learning agents. Reinforcement learning covers a family of algorithms with the purpose of maximize a cumulative reward that an agent can obtain from an environment. It seems like training crows to collect cigarette butts in exchange for peanuts, or paraphrasing an old say, the carrot and stick metaphor for cold algorithms instead of living donkeys. See more on https://gfrison.com

Data Modeling using Symbolic Regression

Patrick Nicolas

This is an introduction to the concept of symbolic regression for managing effectively data stream. Symbolic regression combines genetic algorithm, reinforcement learning and flexible policies to extract meaning or knowledge from data, in an ever changing environment. As the knowledge extracted from real-time data is human readable and consumable, decision makers can validate the findings of the algorithm and act appropriately. Symbolic regression is used in signal processing, process monitoring and adaptive caching in data centers.

Imitation Learning

Anirban Santara

Using Graph Algorithms for Advanced Analytics - Part 5 Classification

TigerGraph

Graph Gurus Episode 32: Using Graph Algorithms for Advanced Analytics Part 5

TigerGraph

Full Webinar: https://info.tigergraph.com/graph-gurus-32 By watching this webinar you will: -See how similarity algorithms are used to calculate "distances" between entities. -Learn what data scientists mean when they say Labels and Training. -Understand the full workflow for the k-Nearest Neighbor machine learning technique, from computing distances to predicting labels for a given value of k, to learning the best value of k.

Big Data, Big Quality? by Irene Gonzálvez at Big Data Spain 2017

Big Data Spain

Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management. The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM). Speakers: Bob Boule Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle. Gopinath Rebala Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.

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