欢迎来到斯坦福机器学习课程！本课程由Andrew Ng教授教授，全面介绍了机器学习领域。通过讲座、练习和项目的组合，您将学习构建机器学习模型并将其应用于现实世界问题所需的基本技术。
Welcome to the Stanford Machine Learning course! This course, taught by Professor Andrew Ng, provides a comprehensive introduction to the field of machine learning. Through a combination of lectures, exercises, and projects, you will learn the fundamental techniques needed to build and apply machine learning models to real-world problems. (2024-03-27, Jupyter Notebook, 0KB, 下载0次)
设计并实现了一个深度Q网络（DQN），用于自主学习和玩Atari游戏Pong。利用强化学习原理和卷积神经网络来处理游戏帧并做出决策。
Designed and implemented a Deep Q-Network (DQN) to autonomously learn and play the Atari game Pong. Utilized reinforcement learning principles and convolutional neural networks to process game frames and make decisions. (2024-03-27, Jupyter Notebook, 0KB, 下载0次)
使用Keras制作的各种分类模型，主要用于2023年秋季开放项目，由UWaterloo的Wat.ai设计团队主持。所有模型都试图对斯坦福在线产品数据集（https:www.tensorflow.org datasets catalog Stanford_Online_Products）中的图像进行分类。
A variety of classification models made with Keras, mostly for the Fall 2023 Open Project, Hosted by the Wat.ai Design Team from UWaterloo. All models try to classify images from the Stanford Online Products Dataset (https: www.tensorflow.org datasets catalog stanford_online_products). (2024-02-25, Jupyter Notebook, 0KB, 下载0次)
“探索对44种脑肿瘤类型进行分类的MRI图像的rad集合！从星形细胞瘤到胶质母细胞瘤，我们的数据集具有T1、对比度T1和T2图像，非常适合ML爱好者和医学研究人员。没有患者ID，只有医生解释的酷酷扫描。投入到脑力娱乐和学习中吧！”
"Explore a rad collection of MRI images categorizing 44 brain tumor types! From astrocytoma to glioblastoma, our dataset features T1, contrast T1, and T2 images, perfect for ML enthusiasts and medical researchers. No patient IDs, just cool scans interpreted by doctors. Dive in for brainy fun and learning!" (2024-02-24, Jupyter Notebook, 0KB, 下载0次)
机器学习专业化是深度学习之间合作创建的一个基础在线程序。人工智能和斯坦福在线。这个初学者友好的程序将教你机器学习的基础知识，以及如何使用这些技术来构建真实世界的人工智能应用程序。
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. (2024-02-18, Jupyter Notebook, 0KB, 下载0次)
XOXO2-使用强化学习训练代理玩U_T-T-T。
XOXO2 - Use Reinforcement Learning to train agent to play U_T-T-T. (2024-01-09, Jupyter Notebook, 0KB, 下载0次)
主要目标是创建一个能够模拟类似人类动作的人工智能玩家，同时利用AlphaGo算法，这是强化学习（RL）领域的一项著名技术。
The primary objective is to create an AI player capable of simulating human-like actions while leveraging the AlphaGo algorithm, a renowned technique in the realm of Reinforcement Learning (RL). (2024-01-08, Jupyter Notebook, 0KB, 下载0次)
问题陈述：使用加利福尼亚住房数据集-i）尝试将GridSearchCV替换为RandomizedSearchCV。ii）创建单个管道，进行完整的数据准备和最终预测。iii）尝试在准备管道中添加transformer，以仅选择最重要的属性。
PROBLEM STATEMENT: USING THE CALIFORNIA HOUSING DATASET - i) Try replacing GridSearchCV with RandomizedSearchCV. ii) Create a single pipeline that does the full data preparation plus the final prediction. iii)Try adding a transformer in the preparation pipeline to select only the most important attributes. (2023-12-22, Jupyter Notebook, 0KB, 下载0次)
这是一款基于Deep-Q Learning[稳定基线]的AI Mario游戏，其中模型增量学习并改进以玩游戏。
This is a Deep-Q Learning [Stable Baseline] based AI Mario Game where the Model Incrementally Learns and Improves to Play the Game. (2023-12-09, Jupyter Notebook, 0KB, 下载0次)
俄罗斯方块是一款众所周知的视频游戏，玩家试图在20x10板（最初为空）上安排方块，直到没有更多的动作可以...，
Tetris is a well known video game where the player attempts to arrange blocks on a 20x10 board (initially empty) until no more moves can be made. There have been some work in the past to train Reinforcement Learning (RL) agents and non-RL based agents to play this game. (2023-10-28, Jupyter Notebook, 0KB, 下载0次)
这些是自我描述的可玩笔记本，旨在为深度学习和人工智能的初学者提供，使他们能够学习和...，
These are self-descriptive playable notebooks that are intended for a beginner in the Deep Learning and AI to enable them to learn and to have fun. (2022-10-19, Jupyter Notebook, 0KB, 下载0次)
开发并实现了一个强化学习深度Q网络（RL-DQN），该网络从其经验中学习玩吃豆人...
Developed and implemented a Reinforcement Learning Deep Q-Network (RL-DQN) that learned to play Pac-Man from its experience using a Convolutional Neural Network (CNN) trained on screen images as input. (2023-04-01, Jupyter Notebook, 13118KB, 下载0次)
一个谷歌Colab python笔记本，其中包含运行LSTM神经网络的代码，该网络使用2015-2019年玩家统计数据来预测...
A Google Colab python notebook that contains code to run an LSTM neural network that uses 2015-2019 player stats to predict 2020 Wins Above Replacement (WAR) value. Uses Pandas, SciKit-Learn, Keras libraries and TensorFlow back-end. (2020-01-19, Jupyter Notebook, 234KB, 下载0次)
一个基于tensorflow的简单人工智能模型，我在其中玩不同的选项，看看它们如何影响acc和los...
A simple AI model based on tensorflow in which i am playing with different options to see how they affect acc and loss of imdb predictions (2020-03-18, Jupyter Notebook, 33KB, 下载0次)
ML遗传突破，强化学习使用遗传算法训练神经网络玩经典游戏Brea...
Reinforcement learning using a genetic algorithm to train a neural network to play a version of the classic game Breakout (2021-11-12, Jupyter Notebook, 75KB, 下载0次)
super-mario-64-ds-wanted-autoplay，一种计算机视觉模型，旨在自主玩《超级马里奥64 ds》中的“通缉犯”迷你游戏
super-mario-64-ds-wanted-autoplay,A computer vision model designed to autonomously play the "Wanted!" minigame from Super Mario 64 DS (2023-01-29, Jupyter Notebook, 38KB, 下载0次)
地铁站危险检测，该项目是法兰克福歌德大学CS课程《系统工程与生命科学II》的一部分。在...
This project is part of the CS course Systems Engineering Meets Life Sciences II at Goethe University Frankfurt. In this Computer Vision project, we developed a first prototype of a security system which uses the surveillance cameras at subway stations to recognize dangerous situations. The training data was artificially generated by a (2021-03-15, Jupyter Notebook, 1141919KB, 下载0次)
detect-malicious-URLs,项目并无多少实际效用,纯粹做来玩。做了ML和DL两个特征提取,分别使用了各种机器学习模型和基于Attention的LSTM。
Detect malicious URLs, the project has little practical utility and is purely for fun. We performed ML and DL feature extraction using various machine learning models and Attention based LSTM, respectively. (2018-11-06, Jupyter Notebook, 22809KB, 下载0次)
斯坦福德项目-预测库存价格-使用LSTM网络，。。。机器学习（ML）和深度学习（DL）库允许非线性预测交互，可以改善...
Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the ... (2020-02-24, Jupyter Notebook, 1611KB, 下载0次)
组合模式生成网络（CPPN）的一种实现，使用增强拓扑的神经进化（NEAT）来玩OpenAI健身房游戏。在这个实现中，算法运行两足步行者游戏。
An implementation of Compositional Pattern Producing Networks (CPPN) using
NeuroEvolution of Augmenting Topologies (NEAT) to play OpenAI gym games. In
this implimentation, the algorithm runs the bipedal walker game.
, (2020-10-13, Jupyter Notebook, 8KB, 下载0次)