交互是一种用于AI代理的逼真的可交互框架。 消息 （6/2020）我们提供了一个微型框架，以简化在Docker中运行AI2-THOR的工作。 可以通过以下网址访问它： : 。 （4/2020）框架的版本2.4.0更新在这里。 现在，所有不属于环境结构的sim对象都可以通过物理相互作用移动。 添加了新的对象类型，并添加了许多新的动作。 请在查看 （2/2020）AI2-THOR现在包括两个框架： 和 。 iTHOR包含交互式对象和场景，而RoboTHOR包含模拟场景及其对应的真实世界副本。 （9/2019）已添加框架2.1.0版更新。 添加了新的对象类型。 添加了新的初始化操作。 分割图像的生成在所有场景中都得到了改善。 （6/2019）AI2-THOR框架的2.0版更新现已发布！ 我们的动作和对象状态增加了三倍，增加了新的动作，可以在视觉上进行明显的状态更改，例如电子设备上的屏幕

RL4J：Java 强化学习 有关 RL4J 的支持问题，请联系 。 RL4J 是一个与 deeplearning4j 集成并在 Apache 2.0 开源许可下发布的强化学习框架。 DQN（带双 DQN 的深度 Q 学习） 异步强化学习（A3C，异步 NStepQlearning） 低维（信息数组）和高维（像素）输入。 一篇有用的博客文章，向您介绍强化学习、DQN 和 Async RL： 快速开始 安装 可视化 厄运 Doom 还没有准备好，但如果你喜欢冒险，你可以通过一些额外的步骤让它工作： 您将需要 vizdoom，编译本机库并将其移动到项目根目录中的文件夹中 export MAVEN_OPTS=-Djava.library.path=THEFOLDEROFTHELIB mvn compile exec:java -Dexec.mainClass="YOURMAINCL

Artificial intelligence is becoming increasingly relevant in the modern world where everything is driven by data and automation. It is used extensively across many fields such as image recognition, robotics, search engines, and self-driving cars. In this book, we will explore various real-world scenarios. We will understand what algorithms to use in a given context and write functional code using this exciting book. We will start by talking about various realms of artificial intelligence. We’ll then move on to discuss more complex algorithms, such as Extremely Random Forests, Hidden Markov Models, Genetic Algorithms, Artificial Neural Networks, and Convolutional Neural Networks, and so on. This book is for Python programmers looking to use artificial intelligence algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be helpful so you can play around with the code. It is also useful to experienced Python programmers who are looking to implement artificial intelligence techniques. You will learn how to make informed decisions about the type of algorithms you need to use and how to implement those algorithms to get the best possible results. If you want to build versatile applications that can make sense of images, text, speech, or some other form of data, this book on artificial intelligence will definitely come to your rescue! What this book covers Chapter 1, Introduction to Artificial Intelligence, teaches you various introductory concepts in artificial intelligence. It talks about applications, branches, and modeling of Artificial Intelligence. It walks the reader through the installation of necessary Python packages. Chapter 2, Classification and Regression Using Supervised Learning, covers various supervised learning techniques for classification and regression. You will learn how to analyze income data and predict housing prices. Chapter 3, Predictive Analytics with Ensemble Learning, explains predictive modeling techniques using Ensemble Learning, particularly focused on Random Forests. We will learn how to apply these techniques to predict traffic on the roads near sports stadiums. Chapter 4, Detecting Patterns with Unsupervised Learning, covers unsupervised learning algorithms including K-means and Mean Shift Clustering. We will learn how to apply these algorithms to stock market data and customer segmentation.

在下一篇文章中，我们将预处理要输入到机器学习模型的数据集。

NJU-Artificial-Intelligence 2018Spring 人工智能 课程

训练12小时后512x512鲜花，1 gpu 训练12小时后256x256朵鲜花，1 gpu 比萨 ``轻巧''GAN 在Pytorch的ICLR 2021中提出的实现。 本文的主要贡献是发生器中的跳层激励，以及鉴别器中的自动编码自监督学习。 引用单行摘要“在经过数小时培训的情况下，可以在1024 g分辨率的数百张图像上融合在单个gpu上”。 安装 $ pip install lightweight-gan 使用 一个命令 $ lightweight_gan --data ./path/to/images --image-size 512 每隔1000次迭代，模型将保存到./models/{name} ，模型中的样本将保存到./results/{name} 。 name将是default ，默认情况下。 训练设定 深度学习从业人员的自我解释能力很强 $ lightweight_ga

TimeSformer-Pytorch 实现，是一种基于关注点的纯净，简单的解决方案，可以在视频分类上达到SOTA。 该存储库将仅存储性能最佳的变体“时空分散注意力”，无非就是沿空间之前的时间轴的注意力。 安装 $ pip install timesformer-pytorch 用法 import torch from timesformer_pytorch import TimeSformer model = TimeSformer ( dim = 512 , image_size = 224 , patch_size = 16 , num_frames = 8 , num_classes = 10 , depth = 12 , heads = 8 , dim_head = 64 , attn_dropout =

Artificial Intelligence For Games第三章CombiningSteeringBehavior使用u3d的实现。

在本文中，我们向您展示如何为生成时装设计建立一个生成对抗网络（GAN）。

在Arduino Nano 33 BLE Sense上训练和部署Tensorflow Lite RNN模型的更简单方法-用于微语音识别。