财务项目是一个综合财务管理系统，旨在帮助个人或企业组织、分析和管理其财务数据。它提供了一系列功能来跟踪收入、支出、投资和资产，使用户能够深入了解其财务状况并做出明智的决策。
The Financials project is a comprehensive financial management system designed to assist individuals or businesses in organizing, analyzing, and managing their financial data. It provides a range of features to track income, expenses, investments, and assets, enabling users to gain insights into their financial health and make informed decisions. (2024-04-28, Jupyter Notebook, 0KB, 下载0次)
商店销售和利润分析是从销售和利润的角度分析零售商店业绩的任务。它帮助企业确定需要改进的领域，并做出数据驱动的决策，以优化其运营、定价、营销和库存管理战略，从而推动收入和增长
Store sales and profit analysis is the task of analyzing the performance of a retail store in terms of its sales and profits. It helps businesses identify areas for improvement and make data-driven decisions to optimize their operations, pricing, marketing, and inventory management strategies to drive revenue and growth (2024-04-14, Jupyter Notebook, 0KB, 下载0次)
该项目利用机器学习算法和自然语言处理（NLP）技术，如TF-IDF矢量化和词干化，分析评论的情感。该工具基于评论部分提供对产品或视频整体情绪的见解，帮助企业获得关于其产品的宝贵反馈。
Utilizing machine learning algorithms and natural language processing (NLP) techniques such as TF-IDF vectorization and stemming, this project analyzes the sentiment of comments. The tool provides insights into the overall sentiment of products or videos based on the comments section, helping businesses gain valuable feedback on their products. (2024-03-26, Jupyter Notebook, 0KB, 下载0次)
在这个项目中，我们使用先进的机器学习技术来应对预测公司大销售的挑战。随着企业努力优化运营和最大化收入，准确的销售预测在库存管理、资源分配和战略规划等决策过程中发挥着关键作用。
In this project, we tackle the challenge of predicting big sales for a company using advanced machine learning techniques. As businesses strive to optimize their operations and maximize revenue, accurate sales predictions play a crucial role in decision-making processes such as inventory management, resource allocation, and strategic planning. (2024-03-10, Jupyter Notebook, 0KB, 下载0次)
Hands-on-Deep-Learning-with-PyTorch,本仓库提供了使用PyTorch进行深度学习的最佳实践,从深度学习环境搭建与张量基础入手,从0到1构建和训练神经网络,通过优化促进模型的收敛和更好的效果,同时着力于深度架构落地实践,最后通过一线CV(和NLP)企业级应用提升实战能力。项...
Hands on Deep Learning with PyTorch, this warehouse provides the best practices for using PyTorch for deep learning. Starting from the establishment of deep learning environments and tensor foundations, neural networks are constructed and trained from 0 to 1. Through optimization, model convergence and better results are promoted, while focusing on the implementation of deep architecture in practice. Finally, practical capabilities are improved through frontline CV (and NLP) enterprise level applications. Item (2023-04-30, Jupyter Notebook, 27005KB, 下载0次)
强化学习（RL），可以让你在商业环境中开发智能、快速和自我学习的系统。这是一种有效的方法来培训您的学习代理，并解决人工智能中的各种问题，从游戏、自动驾驶汽车和机器人到企业应用，从数据中心节能（c...
Reinforcement Learning (RL), allows you to develop smart, quick and self- learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (2019-09-25, Jupyter Notebook, 4206KB, 下载0次)