An introduction to what a Mesh, Shader and Material is in Unity, how to set Shader Properties from C#, a brief look at Forward vs Deferred rendering and some information about Material instances and Batching. HLSL | Unity Shader Tutorials, @Cyanilux
Foi desenvolvido e disponibilizado pela equipe do Núcleo de Apoio a Tecnologias Educacionais (Nate) o Manual do WebConf para docentes.
Nele, há diversas informações que poderão ajudar com as dúvidas dos docentes.
Está disponível para download gratuito a edição atualizada do e-book Acessibilidade em Ambientes Virtuais. O material reúne dicas de boas práticas para auxiliar a produção de materiais e a adaptação do ensino no ambiente virtual para pessoas com baixa visão, cegas, portadoras de deficiência auditiva e surdas.
Ensino remoto - Minhas estratégias de ensino em tempos de coronavírus. Ricardo Biloti. Professor Doutor na Universidade Estadual de Campinas (Unicamp), no Departamento de Matemática Aplicada do Instituto de Matemática, Estatística e Computação Científica (IMECC).
This is the Graph Neural Networks: Hands-on Session from the Stanford 2019 Fall CS224W course.
In this tutorial, we will explore the implementation of graph neural networks and investigate what representations these networks learn. Along the way, we'll see how PyTorch Geometric and TensorBoardX can help us with constructing and training graph models.
Pytorch Geometric tutorial part starts at -- 0:33:30
Details on:
* Graph Convolutional Neural Networks (GCN)
* Custom Convolutional Model
* Message passing
* Aggregation functions
* Update
* Graph Pooling
- Sep. 28 – Oct. 2, 2020
- Lihong Li (Google Brain; chair), Marc G. Bellemare (Google Brain)
- The success of deep neural networks in modeling complicated functions has recently been applied by the reinforcement learning community, resulting in algorithms that are able to learn in environments previously thought to be much too large. Successful applications span domains from robotics to health care. However, the success is not well understood from a theoretical perspective. What are the modeling choices necessary for good performance, and how does the flexibility of deep neural nets help learning? This workshop will connect practitioners to theoreticians with the goal of understanding the most impactful modeling decisions and the properties of deep neural networks that make them so successful. Specifically, we will study the ability of deep neural nets to approximate in the context of reinforcement learning.
- Aug. 31 – Sep. 4, 2020
- Csaba Szepesvari (University of Alberta, Google DeepMind; chair), Emma Brunskill (Stanford University), Sébastien Bubeck (MSR), Alan Malek (DeepMind), Sean Meyn (University of Florida), Ambuj Tewari (University of Michigan), Mengdi Wang (Princeton)
A. Cimatti, F. Fraternali, и C. Nipoti. (2019)cite arxiv:1912.06216Comment: 17 pages, 3 figures, first introductory chapter of the textbook published by Cambridge University Press. For more information https://decdb4ae-c884-4971-9114-5f11b6929fd9.filesusr.com/ugd/f44359_26d2207ea96e4f359636feb5b7473336.pdf.
G. Bauch, и G. Dietl. (октября 2008)Tutorial at the 4th Annual IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications, WiMob 2008, Avignon, France.
G. Bauch, и G. Dietl. (декабря 2007)Tutorial at the 10th International Symposium on Wireless Personal Multimedia Communications, WPMC 2007, Jaipur, India, December 2007.