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THE EXPERT’S VOICE

IN .NET

Illustrated

WPF

Written and Illustrated by

Daniel M. Solis

Windows Presentation Foundation

presented visually and concisely

Illustrated WPF

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剩余529页未读，继续阅读

wangzijing1985

2014-06-05

很不错，可惜没有目录导航

Let us first see how a color constant descriptor may be computed by the brain. Ebner (2004) introduced a parallel algorithm for color constancy based on local space average color. This algorithm can also be mapped to what is known about the human visual system (Ebner, 2007c). Local space average color is assumed to be computed within visual area V4 of the brain using resistively coupled neurons. These neurons form a resistive grid. Once local space average color has been computed, a color constant descriptor can be computed by subtracting local space average color from the color measured by the retinal receptors. This algorithm and its mapping to the visual system is illustrated in Figure 2.

Ebner（2004）引入了一种基于局部空间平均颜色的颜色不变算法。该算法也可以映射到人类视觉系统已知的知识（Ebner，2007c）。局部空间平均颜色被认为是在视觉区域V4中使用电阻耦合神经元计算的。这些神经元形成一个电阻网格。一旦计算出局部空间平均颜色，就可以通过将局部空间平均颜色从视网膜感受器测量的颜色中减去来计算颜色常数描述符。该算法及其在视觉系统中的映射如图2所示。

transformers教程

以下是一些学习transformers的教程和资源： 1. Transformers官方文档：https://huggingface.co/transformers/ 2. The Illustrated Transformer：一篇图文并茂的transformer解释文章，非常易懂：http://jalammar.github.io/illustrated-transformer/ 3. The Annotated Transformer：一篇注释版的transformer论文，对于理解transformer非常有帮助：http://nlp.seas.harvard.edu/2018/04/03/attention.html 4. Attention Is All You Need（原始transformer论文）：https://arxiv.org/abs/1706.03762 5. 《深入浅出Transformer》一书，作者是李宏毅，详细介绍了transformer的原理、实现和应用：https://www.gitbook.com/book/hankcs/transformer/details 6. 《动手学深度学习》一书，其中包含了transformer的实现和应用案例，非常适合入门：https://zh.d2l.ai/ 7. Transformers实战教程：https://huggingface.co/course/chapter1 以上是一些学习transformers的教程和资源，可以根据自己的需求和兴趣进行选择和学习。

有没有transformer的使用链接

以下是 Transformer 的使用链接： 1. 《Attention Is All You Need》原论文：https://arxiv.org/abs/1706.03762 2. 《The Illustrated Transformer》：https://jalammar.github.io/illustrated-transformer/ 3. PyTorch 官方 Transformer 教程：https://pytorch.org/tutorials/beginner/transformer_tutorial.html 4. TensorFlow 官方 Transformer 教程：https://www.tensorflow.org/tutorials/text/transformer 5. Hugging Face Transformers 库：https://huggingface.co/transformers/ 6. 使用 Transformer 构建聊天机器人的教程：https://towardsdatascience.com/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313 7. 使用 Transformer 构建机器翻译模型的教程：https://towardsdatascience.com/how-to-build-a-state-of-the-art-neural-machine-translation-model-with-transformer-in-tensorflow-ef6bc057f1d 8. 使用 Transformer 构建语音识别模型的教程：https://towardsdatascience.com/how-to-build-a-state-of-the-art-speech-recognition-system-for-your-business-f820ce52fc2a

根据上述算法模型，请给出一些具体的参考文献或者csdn博客链接

以下是一些关于 GPT 算法的参考文献和 CSND 博客链接： 1. GPT-3: Language Models are Few-Shot Learners - https://arxiv.org/abs/2005.14165 2. The Illustrated GPT-2 (Visualizing Transformer Language Models) - https://jalammar.github.io/illustrated-gpt2/ 3. How to Fine-Tune GPT-2 on Specific Tasks - https://skimai.com/fine-tuning-gpt2-for-specific-tasks/ 4. Text Generation with OpenAI’s GPT-2: Let’s Have Some Fun! - https://towardsdatascience.com/text-generation-with-openais-gpt-2-letting-ai-write-for-me-5d8cbdfbdf9 5. ChitGPT: Building a Chatbot using GPT-2 and Flask - https://www.c-sharpcorner.com/article/chitgpt-building-a-chatbot-using-gpt-2-and-flask/ 希望对您有帮助！

Rab GTPases serve as master regulators of membrane trafficking. They can be activated by guanine nucleotide exchange factors (GEF) and be inactivated by GTPase-activating proteins (GAPs). The roles of some GAPs have been explored in Saccharomyces cerevisiae, but are largely unknown in filamentous fungi. Here, we investigated the role of GAP Gyp3 gene, an ortholog of S. cerevisiae Gyp3, in an entomopathogenic fungus, Metarhizium acridum. We found that MaGyp3 is mainly localized to the endoplasmic reticulum (ER) of vegetative hyphae, nuclei of mature conidia, and both ER and nuclei in invasive hyphae. Lack of MaGyp3 caused a decreased tolerance to hyperosmotic stress, heat-shock and UV-B radiation. Moreover, the ΔMaGyp3 mutant showed a significantly decreased pathogenicity owing to delayed germination, reduced appressorium-mediated penetration and impaired invasive growth. Loss of MaGyp3 also caused impaired fungal growth, advanced conidiation and defects in utilization of carbon and nitrogen sources, while overexpression of MaGyp3 exhibited delayed conidiation on nutrient-rich medium and conidiation pattern shift from microcycle conidiation to normal conidiation on nutrient-limited medium. Mavib-1, a tanscription factor invloved in conidiation by affecting nutrient utilizaiton, can directly bind to the promoter of MaGyp3. ΔMaGyp3 and ΔMavib-1 mutants shared similar phenotypes, and overexpression mutants of MaGyp3 and Mavib-1 (Mavib-1-OE) exhibited similar phenotypes in growth, conidiation and pathogenicity. Reintroduction of the Magyp3 driven by strong promoter gpd in ΔMavib-1 mutant recovered the defects in growth and conidiation for dysfunction of Mavib1. Taken together, our findings uncovered the role of GAP3 in a filamentous pathogenic fungus and and illustrated the upstream regulatory mechanism by direct interaction with Mavib-1.请用nature杂志的风格润色成学术论文的形式。

Metarhizium acridum GTPase-activating protein 3 (MaGyp3) is a GTPase-activating protein (GAP) ortholog of Saccharomyces cerevisiae Gyp3. Here, we investigated the role of MaGyp3 in an entomopathogenic fungus. We found that MaGyp3 is mainly localized to the endoplasmic reticulum (ER) of vegetative hyphae, nuclei of mature conidia, and both ER and nuclei in invasive hyphae. Loss of MaGyp3 caused decreased tolerance to hyperosmotic stress, heat-shock, and UV-B radiation, and significantly decreased pathogenicity owing to delayed germination, reduced appressorium-mediated penetration and impaired invasive growth. Additionally, MaGyp3 was found to be involved in fungal growth, conidiation, and utilization of carbon and nitrogen sources. Mavib-1, a transcription factor involved in conidiation, was identified as a direct upstream regulator of MaGyp3. Our findings uncover the role of GAP3 in a filamentous pathogenic fungus and illustrate the upstream regulatory mechanism by direct interaction with Mavib-1.

计算机网络比较好的书籍是什么，主要讲什么东西

以下是几本比较好的计算机网络书籍： 1. 《计算机网络-自顶向下方法》(Computer Networking: A Top-Down Approach) - James F. Kurose、Keith W. Ross 著该书是计算机网络领域的经典教材之一，以自顶向下的方式介绍了计算机网络的基础知识、协议和应用，包括物理层、数据链路层、网络层、传输层、应用层等内容。 2. 《TCP/IP详解》(TCP/IP Illustrated) - W. Richard Stevens 著该书是关于 TCP/IP 协议的经典教材，通过对 TCP/IP 协议的详细解释和示例代码的实现，深入探讨了计算机网络的基本原理和实现。 3. 《计算机网络-自顶向下方法实验手册》(Computer Networking: A Top-Down Approach Lab Manual) - James F. Kurose、Keith W. Ross、Tom Chapin 著该书是《计算机网络-自顶向下方法》的实验手册，通过实验帮助读者深入理解计算机网络的基础知识、协议和应用，并掌握实际操作的能力。 4. 《网络是怎样连接的》(ネットワークがわかる本) - 石田保著该书是一本以轻松幽默的方式介绍计算机网络的入门教材，涵盖了网络通信的基础知识、协议和应用，适合初学者学习。这些书籍覆盖了计算机网络的基础知识、协议和应用，适合不同层次的读者学习。

掌握transformer模型需要看那些书籍

如果你想深入学习Transformer模型，可以参考以下几本书籍： 1.《Attention Is All You Need》：这是Transformer模型的原始论文，深入介绍了这个模型的思想和实现细节。 2.《Illustrated Transformer》：这是一本非常适合初学者阅读的图文书，详细介绍了Transformer模型的每一个组成部分。 3.《Deep Learning for NLP with PyTorch》：这是一本以PyTorch为主的深度学习NLP入门书，也介绍了Transformer模型的原理和实现。 4.《Transformers from Scratch》：这是一本从零开始实现Transformer模型的书，适合想要了解Transformer模型内部工作原理的读者。以上书籍都是关于Transformer模型的经典资料，通过阅读它们可以加深对Transformer模型的理解。

transformer画图

在Transformer模型中，有多个Encoder和多个Decoder组成。每个Encoder和Decoder内部都由多个层堆叠而成，通常是6层。每一层都包括Self-Attention、Multi-Headed Attention、Feed-Forward Network等模块。Encoder和Decoder之间没有权重共享。以下是Transformer的一个示意图： 123 #### 引用[.reference_title] - *1* [图解transformer | The Illustrated Transformer](https://blog.csdn.net/qq_36667170/article/details/124359818)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}} ] [.reference_item] - *2* *3* [Transformer完全图解](https://blog.csdn.net/weixin_44305115/article/details/101622645)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}} ] [.reference_item] [ .reference_list ]

怎么自己做一个chatgpt

ChatGPT 是基于 GPT 神经网络模型的聊天机器人。如果你想自己做一个 ChatGPT，可以按照以下步骤： 1. 学习 GPT 神经网络模型 GPT 是一种基于 Transformer 的神经网络模型，由 OpenAI 团队开发。要了解 GPT 模型的基本原理和实现方法，可以阅读相关的论文和教程，例如《Attention Is All You Need》和《The Illustrated Transformer》。 2. 收集数据集要训练 ChatGPT，需要大量的聊天数据集。你可以从开源数据集中获取数据，例如 Cornell Movie Dialogs Corpus 和 Persona-Chat。你也可以创建自己的数据集，收集用户的对话，并进行清洗和预处理。 3. 训练模型使用 TensorFlow 或 PyTorch 等深度学习框架，按照 GPT 模型的实现方法，将数据集输入到模型中进行训练。训练过程需要大量的计算资源和时间，因此你需要选择适合的硬件设备和优化方案。 4. 部署 ChatGPT 训练好 ChatGPT 后，你可以将模型部署到云服务器或本地计算机上，提供对话服务。你可以使用 Flask 或 Django 等 Web 框架，编写一个简单的 Web 应用程序，实现 ChatGPT 的前端和后端交互。 5. 调优 ChatGPT 在部署 ChatGPT 后，你需要根据用户反馈和实际情况对模型进行调优。你可以通过调整超参数、增加数据集、优化模型结构等方式，不断提高 ChatGPT 的性能和用户体验。

学习网络架构需要看哪些书

学习网络架构需要了解计算机网络、网络协议、网络安全等方面的知识，以下是一些经典的网络架构方面的书籍： 1.《计算机网络：自顶向下方法》（Computer Networking: A Top-Down Approach）：由美国计算机科学家 James F. Kurose 和 Keith W. Ross 编写的计算机网络经典教材，系统介绍了计算机网络的各个层次和协议。 2.《TCP/IP 详解》（TCP/IP Illustrated）：由美国计算机科学家 W. Richard Stevens 编写的经典网络协议教材，详细介绍了 TCP/IP 协议族的各个协议和实现细节。 3.《HTTP 权威指南》（HTTP: The Definitive Guide）：由美国计算机科学家 David Gourley 和 Brian Totty 编写的 HTTP 协议详解教材，介绍了 HTTP 协议的各个方面和实现细节。 4.《网络安全技术基础》（Foundations of Network Security）：由美国计算机科学家 J. Michael Stewart 编写的网络安全基础教材，介绍了网络安全的基本原理、攻击和防御技术等方面的知识。 5.《网络攻击与防范》（Network Intrusion Detection: An Analyst's Handbook）：由美国计算机安全专家 Stephen Northcutt 编写的网络攻击与防范教材，帮助读者了解网络攻击的各种类型和特征，并介绍了如何进行网络入侵检测和防范。当然，这些书籍只是网络架构方面的经典书籍，学习网络架构还需要结合实践和实际项目进行学习。

transformer实现文本分类

Transformer可以用于实现文本分类任务。它是一种基于自注意力机制的神经网络模型，它能够对输入的文本进行编码和理解，从而有效地捕捉文本的语义信息。Transformer通过多层的编码器和解码器构成，其中编码器负责对输入文本进行特征提取，解码器则负责生成输出。在文本分类任务中，可以使用Transformer的编码器部分来进行特征提取，并将提取到的特征输入到全连接层进行分类。引用中提到的文章《The Illustrated Transformer》以及《The Annotated Transformer》可以帮助你更好地理解Transformer的机制和原理。另外，引用中提到的Happy Transformer这个工具包可以简化Transformer模型的实现和训练过程。123 #### 引用[.reference_title] - *1* *2* [6. 文本分类——transformer模型](https://blog.csdn.net/qq_38293297/article/details/105027845)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"] - *3* [【NLP】使用Transformer模型进行文本分类](https://blog.csdn.net/fengdu78/article/details/120878265)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"] [ .reference_list ]

transformer实践

回答: Transformer是一种用于自然语言处理的模型，它在Google的论文"Attention is All You Need"中被提出。为了方便实现和调用Transformer，Google开源了一个基于TensorFlow的第三方库Tensor2Tensor。此外，也有研究者贡献了一个基于PyTorch的Transformer实现。\[2\] Transformer的网络结构包括编码器（Encoder）和解码器（Decoder）两部分。编码器负责将输入序列转换为一系列的隐藏表示，而解码器则根据编码器的输出和之前的预测结果生成目标序列。具体来说，Transformer由多个编码器层和解码器层组成，每个层都包含自注意力机制和前馈神经网络。\[3\] 在实践中，我们可以使用一些工具代码来帮助实现Transformer模型。例如，我们可以使用深拷贝来复制模块，使用layernorm计算来进行归一化操作，使用注意力机制相关的代码来计算自注意力和多头注意力，使用全连接网络来进行特征映射，使用mask来处理解码器的输出，以及使用embedding计算概率等。\[3\] 如果你对Transformer的实践感兴趣，你可以参考一些相关的链接和文献，如Illustrated Transformer网站、CSDN博客和Harvard大学的Annotated Transformer项目。此外，你还可以在GitHub上找到完整的代码和测试用例。\[3\] #### 引用[.reference_title] - *1* [Transformer(四)--实现验证：transformer 机器翻译实践](https://blog.csdn.net/nocml/article/details/125711025)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item] - *2* [Transformer原理到实践详解](https://blog.csdn.net/Enjoy_endless/article/details/88344750)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item] - *3* [transformer原理及代码实践](https://blog.csdn.net/THUChina/article/details/123441732)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item] [ .reference_list ]

detr和transformer

DETR是一种基于Transformer的端到端目标检测模型。它结合了Transformer的自注意力机制和CNN的特征提取能力，通过将输入图像转换为一组特征向量，并使用Transformer进行特征加权，以实现目标检测任务。与传统的基于区域提议的目标检测方法不同，DETR直接从整个图像中预测目标的位置和类别，避免了繁琐的候选框生成和筛选过程。DETR的整体架构由一个Transformer编码器和一个解码器组成。编码器将图像特征进行编码表示，解码器通过自注意力机制在编码器输出的特征上进行迭代操作，以逐步预测目标的位置和类别。 DETR的关键操作在forward函数中实现，其中对输入特征进行变换操作，将形状为NxCxHxW的特征张量展平为形状为HWxNxC的张量。这个操作是为了方便后续的自注意力计算。如果你对Transformer模型的细节感兴趣，我推荐你阅读这篇博客：http://jalammar.github.io/illustrated-transformer/，它详细介绍了Transformer模型的原理和实现细节。

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IllustratedWPF.pdf 英文原版

addison, p. s., the illustrated wavelet transform handbook, taylor and franc

tcp ip illustrated

tcp/ip illustrated

transformers教程

有没有transformer的使用链接

根据上述算法模型，请给出一些具体的参考文献或者csdn博客链接

计算机网络比较好的书籍是什么，主要讲什么东西

数据结构python推荐书籍

掌握transformer模型需要看那些书籍

transformer画图

怎么自己做一个chatgpt

学习网络架构需要看哪些书

transformer实现文本分类

transformer实践

detr和transformer

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