BibSonomy bookmarks for /user/analyst/tutorial

A Complete Guide to Create GIF in Linux

analyst — 2022-05-25T11:24:04+02:00

https://www.debugpoint.com/2015/12/a-complete-guide-to-create-gif-in-linux/

A Tutorial on Structural Optimization

analyst — 2022-05-11T09:23:25+02:00

https://greydanus.github.io/2022/05/08/structural-optimization/

Vulkan Tutorial

analyst — 2022-01-22T13:24:18+01:00

A tutorial that teaches you everything it takes to render 3D graphics with the Vulkan API. It covers everything from Windows/Linux setup to rendering and debugging.

Python Developer's Guide

analyst — 2022-01-01T05:59:42+01:00

https://devguide.python.org/

PyTorch vs. TensorFlow - a detailed comparison | Tooploox

analyst — 2021-09-07T11:08:38+02:00

https://www.tooploox.com/blog/pytorch-vs-tensorflow-a-detailed-comparison

Deep Q Networks (DQN) | labml.ai

analyst — 2021-08-09T11:11:08+02:00

This is a PyTorch implementation/tutorial of Deep Q Networks (DQN) from paper Playing Atari with Deep Reinforcement Learning. This includes dueling network architecture, a prioritized replay buffer and double-Q-network training.

How to Read More Research Papers?

analyst — 2021-06-25T16:59:29+02:00

https://www.louisbouchard.ai/research-papers/

Instructions on Transformer for people outside NLP field, but with examples of NLP – Data Science Blog

analyst — 2021-06-25T16:47:40+02:00

https://data-science-blog.com/blog/2020/12/30/transformer/

Intro to Shaders | Cyanilux

analyst — 2021-04-28T22:34:51+02:00

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

Using C from Python: How to create a ctypes wrapper - Scientific IT-Systems

analyst — 2021-04-05T09:16:20+02:00

https://pgi-jcns.fz-juelich.de/portal/pages/using-c-from-python.html

Graph Neural Networks (GNN) using Pytorch Geometric | Stanford University

analyst — 2021-01-19T21:46:29+01:00

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

Welcome to Spinning Up in Deep RL! | OpenAI

analyst — 2020-12-10T21:03:12+01:00

https://spinningup.openai.com/en/latest/

Effective Machine Learrning

analyst — 2020-11-21T18:08:08+01:00

https://effectivemachinelearning.com/

Wacom Intuos and xsetwacom on Ubuntu 18.04 - Joshua Woehlke

analyst — 2020-11-20T19:19:26+01:00

xsetwacom tutorial for Wacom Intuos tablets on Ubuntu 18.04. Multiple monitors, screen mapping, custom buttons, and Inkscape compatibility.

Designing Effective Scientific Figures

analyst — 2020-11-20T09:29:48+01:00

Designing Effective Scientific Figures : This course provides a practical introduction to producing figures for use in reports and publications.

Deep Reinforcement Learning | Simons Institute

analyst — 2020-10-12T14:51:33+02:00

- 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.

Theory of Reinforcement Learning Boot Camp | Simons Institute

analyst — 2020-10-12T13:58:40+02:00

- 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)

Theory of Reinforcement Learning | Simons Institute for the Theory of Computing

analyst — 2020-10-12T13:31:38+02:00

This program aims to reunite researchers across disciplines that have played a role in developing the theory of reinforcement learning. It will review past developments and identify promising directions of research, with an emphasis on addressing existing open problems, ranging from the design of efficient, scalable algorithms for exploration to how to control learning and planning. It also aims to deepen the understanding of model-free vs. model-based learning and control, and the design of efficient methods to exploit structure and adapt to easier environments.

Foundations of Deep Learning | Simons Institute for the Theory of Computing

analyst — 2020-10-12T13:29:43+02:00

The program focused on the following four themes: - Optimization: How and why can deep models be fit to observed (training) data? - Generalization: Why do these trained models work well on similar but unobserved (test) data? - Robustness: How can we analyze and improve the performance of these models when applied outside their intended conditions? - Generative methods: How can deep learning be used to model probability distributions?

Converting OpenCV cameras to OpenGL cameras. · Amy Tabb

analyst — 2020-08-11T14:58:56+02:00

https://amytabb.com/ts/2019_06_28/