Tip 1:專案三角
Tip 2:工作分解結構-WBS
Tip 3:甘特圖-時間
Tip 4:甘特圖-成本
Tip 5:專案計畫前-啟始
Tip 6:專案計劃-WBS & 甘特圖
Tip 7:專案時間管理-要徑
Tip 8:專案人力管理-資源撫平
Tip 9:專案溝通管理-敏捷會議
Tip 10:專案溝通管理-一頁式報告
Tip 11:專案結案管理-Lesson Learn
Tip 12:專案管理工具
台中逢甲大學專案管理志工隊
Tip 1:專案三角
Tip 2:工作分解結構-WBS
Tip 3:甘特圖-時間
Tip 4:甘特圖-成本
Tip 5:專案計畫前-啟始
Tip 6:專案計劃-WBS & 甘特圖
Tip 7:專案時間管理-要徑
Tip 8:專案人力管理-資源撫平
Tip 9:專案溝通管理-敏捷會議
Tip 10:專案溝通管理-一頁式報告
Tip 11:專案結案管理-Lesson Learn
Tip 12:專案管理工具
台中逢甲大學專案管理志工隊
This document summarizes a lecture on 3D vision and shape representations. It discusses various ways to represent 3D shapes, including point clouds, meshes, voxels, implicit surfaces, and parametric surfaces. It also covers recent datasets created for 3D objects, object parts, indoor scenes, and how neural networks can be applied to these representations for tasks like classification, generation, and reconstruction. Representation selection depends on the specific application and tradeoffs between flexibility, memory usage, and supporting different operations. Recent work also aims to develop more unified representations that combine advantages of multiple approaches.
The document summarizes the use of attention mechanisms in sequence-to-sequence models. It describes how attention allows the decoder to attend to different parts of the input sequence at each time step by computing a context vector as a weighted sum of the encoder hidden states. This context vector is then used instead of a fixed context vector, addressing the problem of bottlenecking long sequences. The attention weights are learned during backpropagation without explicit supervision.
This document summarizes key points from a lecture on training neural networks. It discusses initialization of activation functions like ReLU, preprocessing data through normalization, and initializing weights. For activation functions, ReLU is commonly used due to its computational efficiency and ability to avoid saturated gradients. Data is often preprocessed by subtracting the mean to center it. Weight initialization techniques ensure gradients flow properly during training.
This document outlines the agenda for the CS231n: Deep Learning for Computer Vision lecture. It introduces image classification as a core task in computer vision and discusses how deep learning approaches like convolutional neural networks (CNNs) are important tools for visual recognition problems. The lecture provides an overview of the course, which covers topics like CNNs, object detection, segmentation, and applications of deep learning beyond 2D images and computer vision.
This document summarizes an academic lecture on convolutional neural network architectures. It begins with an overview of common CNN components like convolution layers, pooling layers, and normalization techniques. It then reviews the architectures of seminal CNN models including AlexNet, VGG, GoogLeNet, ResNet and others. As an example, it walks through the architecture of AlexNet in detail, explaining the parameters and output sizes of each layer. The document provides a high-level history of architectural innovations that drove improved performance on the ImageNet challenge.
This document provides information and guidance for students completing the CS231n course project. It discusses project expectations, how to pick a project idea, and deliverables. For project expectations, it notes the open-ended nature but focus on computer vision problems. Sources of inspiration for project ideas include conferences, papers, and previous student projects. Reading papers efficiently involves focusing on abstracts, methods, results rather than linear reading. Deliverables include a proposal, milestone report, final report, and poster presentation. The proposal and milestone report formats are also outlined.
This document discusses lifelong learning and approaches to address it. It begins with reminders for project deadlines and the plan for the lecture, which is to discuss the lifelong learning problem statement, basic approaches, and how to potentially improve upon them. It then reviews problem statements for online learning, multi-task learning, and meta-learning, noting that real-world settings involve sequential learning over time. Common approaches like fine-tuning all data can hurt past performance, while storing all data is infeasible. Meta-learning aims to efficiently learn new tasks from a non-stationary distribution of tasks.
This document discusses various types of real assets as investments, including real estate, precious metals, and collectibles. It describes the advantages and disadvantages of investing in real assets, noting they may outperform during inflation but have liquidity issues. Real estate investments can include residential and commercial properties. Valuation methods for real estate include cost, sales comparison, and income approaches. Real estate can be owned individually or through various partnership and trust structures like REITs. Precious metals like gold and silver traditionally rise during economic uncertainty. Other collectibles include art, antiques, and memorabilia.
This document discusses measuring risks and returns of portfolio managers. It covers three key performance measures: the Treynor measure, Sharpe measure, and Jensen's Alpha. The Sharpe measure evaluates reward per unit of total risk, the Treynor measure evaluates reward per unit of beta risk, and Jensen's Alpha measures actual returns against the CAPM model. The document also discusses how investors apply modern portfolio theory, with some believing markets are strongly efficient so only a naïve diversified portfolio is needed, while others believe in semi-strong efficiency and analyze stocks for a diversified growth portfolio. Diversification, long-term performance measurement, and balancing growth versus diversification are important implications for investors.
People invest to accumulate funds for specific purposes like wealth creation, financial security, and retirement planning. Investments require sacrificing something today for future returns. There are real assets like real estate and equipment, and financial assets that are claims against real assets like stocks and bonds. Risk and expected return are directly related - higher risk investments are expected to provide higher returns. Setting clear investment objectives involves considering factors like risk tolerance, time horizon, need for income versus capital appreciation, tax implications, and more. The relationship between risk and return is a key principle in investing.
The document provides an investor presentation for Thoughtworks' Q1 2022 results. It includes forward-looking statements, non-GAAP financial measures, and industry and market data. Thoughtworks has over 11,000 employees globally, with revenues of $321 million in Q1 2022, up 35% year-over-year. Adjusted EBITDA was $73 million, with an adjusted EBITDA margin of 22.7%.
Amine solvent development for carbon dioxide capture by yang du, 2016 (doctor...Kuan-Tsae Huang
This dissertation examines the development of amine solvents for carbon dioxide capture from flue gas. 36 novel piperazine-based amine blends were screened for their thermal degradation, amine volatility, CO2 cyclic capacity, and CO2 absorption rate. 18 thermally stable blends were identified. A group contribution model was developed to predict amine volatility. The optimum pKa of a tertiary amine in a piperazine/tertiary amine blend for highest CO2 capacity was around 9.1. A generic Aspen Plus model was developed to predict CO2 absorption based on tertiary amine pKa. 2 m piperazine/3 m 4-hydroxy-1-methylpiperidine showed