Difference between Discriminative Learning and Generative Learning
Cosine distance as a Basic metric of Deep Learning
Multi-layer Perceptron as a common part of Deep Learning Variants
Analogy between Similarity in Deep Learning and Wave Coherence
Deep Neural Net. as a Wave Extractor
This document discusses behavior trees, which are commonly used to direct behaviors for AI in games. It provides an overview of behavior tree theory, including the basic components of behavior trees like composites, services, decorators, tasks, and the blackboard. It then discusses behavior trees in Unreal Engine 4 specifically. The document also provides an example of how behavior trees could be used for an airplane dogfighting AI and includes references for further information.
Developing Success in Mobile with Unreal Engine 4 | David StelzerJessica Tams
油
This document discusses the advantages of using the Unreal Engine 4 (UE4) for game development compared to other game engines like Unity. It notes that UE4 is a complete toolset that supports multi-platform development including PC, console, web, VR/AR and mobile. It has visual scripting using Blueprints that allows artists and designers to code without programming. UE4 also has C++ integration and provides full source code access for free along with flexible licensing options.
Luigi is a workflow management system that allows users to build complex data pipelines. It provides tools to define dependencies between tasks, run workflows on Hadoop, and visualize data flows. The speaker describes how they developed Luigi at Spotify to manage thousands of Hadoop jobs run daily for music recommendations and other applications. Key features of Luigi include defining Python tasks, easy command line execution, automatic dependency resolution, and failure recovery through atomic file operations. The speaker demonstrates how Luigi can run multi-step workflows on the command line, including a music recommendation example involving feature extraction, model training, and evaluation.
Approximate nearest neighbor methods and vector models NYC ML meetupErik Bernhardsson
油
Nearest neighbors refers to something that is conceptually very simple. For a set of points in some space (possibly many dimensions), we want to find the closest k neighbors quickly.
This presentation covers a library called Annoy built my me that that helps you do (approximate) nearest neighbor queries in high dimensional spaces. We're going through vector models, how to measure similarity, and why nearest neighbor queries are useful.
How to implement realistic fabric material by Unreal engine?
This slider shows the way. You can make realistic and physically correct fabric shader by this method.
Difference between Discriminative Learning and Generative Learning
Cosine distance as a Basic metric of Deep Learning
Multi-layer Perceptron as a common part of Deep Learning Variants
Analogy between Similarity in Deep Learning and Wave Coherence
Deep Neural Net. as a Wave Extractor
This document discusses behavior trees, which are commonly used to direct behaviors for AI in games. It provides an overview of behavior tree theory, including the basic components of behavior trees like composites, services, decorators, tasks, and the blackboard. It then discusses behavior trees in Unreal Engine 4 specifically. The document also provides an example of how behavior trees could be used for an airplane dogfighting AI and includes references for further information.
Developing Success in Mobile with Unreal Engine 4 | David StelzerJessica Tams
油
This document discusses the advantages of using the Unreal Engine 4 (UE4) for game development compared to other game engines like Unity. It notes that UE4 is a complete toolset that supports multi-platform development including PC, console, web, VR/AR and mobile. It has visual scripting using Blueprints that allows artists and designers to code without programming. UE4 also has C++ integration and provides full source code access for free along with flexible licensing options.
Luigi is a workflow management system that allows users to build complex data pipelines. It provides tools to define dependencies between tasks, run workflows on Hadoop, and visualize data flows. The speaker describes how they developed Luigi at Spotify to manage thousands of Hadoop jobs run daily for music recommendations and other applications. Key features of Luigi include defining Python tasks, easy command line execution, automatic dependency resolution, and failure recovery through atomic file operations. The speaker demonstrates how Luigi can run multi-step workflows on the command line, including a music recommendation example involving feature extraction, model training, and evaluation.
Approximate nearest neighbor methods and vector models NYC ML meetupErik Bernhardsson
油
Nearest neighbors refers to something that is conceptually very simple. For a set of points in some space (possibly many dimensions), we want to find the closest k neighbors quickly.
This presentation covers a library called Annoy built my me that that helps you do (approximate) nearest neighbor queries in high dimensional spaces. We're going through vector models, how to measure similarity, and why nearest neighbor queries are useful.
How to implement realistic fabric material by Unreal engine?
This slider shows the way. You can make realistic and physically correct fabric shader by this method.
Auto Scalable Deep Learning Production AI Serving Infra 蟲 覦 AI DevOps...hoondong kim
油
[Tensorflow-KR Offline 碁碁 覦襭]
Auto Scalable Deep Learning Production AI Serving Infra 蟲 覦 AI DevOps Cycle 蟲 覦覯襦. (Azure Docker PaaS 1襷 TPS Tensorflow Inference Serving 覦覯襦 螻旧)
Recent background programs have been used in many areas. Background programs, which are used in a variety of areas, from Web servers to system monitoring tools, are based on the concept of a process called daemon.
This session is intended to be a user of the *nix family operating system and has been viewed using services or daemons. It's for people who want to make it.
Therefore, the presentation will be accompanied by the following knowledge:
Required:
- Experience in using the *nix operating system (linux, BSD, MacOS)
- *nix File System Concepts
- Python basic grammar
- Python Packaging
Select:
- Basic understanding of the process