The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
標簽： Auto-Machine-Learning-Methods-Sys tems-Challenges
上傳時間： 2020-06-10
上傳用戶：shancjb
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propa- gation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.
標簽： Bishop-Pattern-Recognition-and-Ma chine-Learning
上傳時間： 2020-06-10
上傳用戶：shancjb
This book is a general introduction to machine learning that can serve as a reference book for researchers and a textbook for students. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms.
標簽： Foundations Learning Machine 2nd of
上傳時間： 2020-06-10
上傳用戶：shancjb
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
標簽： interpretable-machine-learning
上傳時間： 2020-06-10
上傳用戶：shancjb
Much has been written concerning the manner in which healthcare is changing, with a particular emphasis on how very large quantities of data are now being routinely collected during the routine care of patients. The use of machine learning meth- ods to turn these ever-growing quantities of data into interventions that can improve patient outcomes seems as if it should be an obvious path to take. However, the field of machine learning in healthcare is still in its infancy. This book, kindly supported by the Institution of Engineering andTechnology, aims to provide a “snap- shot” of the state of current research at the interface between machine learning and healthcare.
標簽： Technologies Healthcare Learning Machine
上傳時間： 2020-06-10
上傳用戶：shancjb
Machine learning is about designing algorithms that automatically extract valuable information from data. The emphasis here is on “automatic”, i.e., machine learning is concerned about general-purpose methodologies that can be applied to many datasets, while producing something that is mean- ingful. There are three concepts that are at the core of machine learning: data, a model, and learning.
上傳時間： 2020-06-10
上傳用戶：shancjb
Learning Python 第5版電子版書籍，正規版本，不是掃描的哦，關于這本書的內容不解釋了，懂Python的應該知道，很不錯的一本書，不過非常考驗英文水平。
標簽： python
上傳時間： 2022-07-02
上傳用戶：xsr1983
Meta首份元宇宙白皮書稱，如果元宇宙技術從 2022 年開始被采用，到 2031 年，元宇宙技術將為全球 GDP 貢獻 3.01 萬億美元，其中三分之（1.04 萬億美元）來自亞太地區。2022-the-potential-global-economic-impact-of-the-metaverse報告原文下載，PDF文檔下載
上傳時間： 2022-07-26
上傳用戶：canderile
圖像配準理論及算法研究.pdf cnn_tutorial.pdf Deep Learning（深度學習）學習筆記整理.pdf 00.神經?絡與深度學習.pdf deep learning.pdf 深度學習方法及應用PDF高清晰完整版.pdf 斯坦福大學-深度學習基礎教程.pdf 深度學習基礎教程.pdf deep+learning.pdf 深度學習 中文版 ---文字版.pdf 神經網絡與機器學習（原書第3版）.pdf
上傳時間： 2013-06-07
上傳用戶：eeworm
近年來微光、紅外、X光圖像傳感器在軍事、科研、工農業生產、醫療衛生等領域的應用越來越為廣泛，但由于這些成像器件自身的物理缺陷，視覺效果很不理想，往往需要對圖像進行適當的處理，以得到適合人眼觀察或機器識別的圖像。因此，市場急需大量高效的實時圖像處理器能夠在傳感器后端對這類圖像進行處理。而FPGA的出現，恰恰解決了這個問題。 近十年來，隨著FPGA(現場可編程門陣列)技術的突飛猛進，FPGA也逐漸進入數字信號處理領域，尤其在實時圖像處理方面。Xilinx的研究表明，在2000年主要用于DSP應用的FPGA的發貨量，增長了50％；而常規的DSP大約增長了40％。由于FPGA可無比擬的并行處理能力，使得FPGA在圖像處理領域的應用持續上升，國內外，越來越多的實時圖像處理應用都轉向了FPGA平臺。與PDSP相比，FPGA將在未來統治更多前端(如傳感器)應用，而PDSP將會側重于復雜算法的應用領域。可以說，FPGA是數字信號處理的一次重大變革。 算法是圖像處理應用的靈魂，是硬件得以發揮其強大功能的根本。”共軛變換”圖像處理方法是一種新型的圖像處理算法，由鄭智捷博士上個世紀90年代初提出。這種算法使用基元形狀(meta-shape)技術，而這種技術的特征正好具備幾何與拓撲的雙重特性，使得大量不同的基于形態的灰度圖像處理濾波器可用這種方法實現。該種算法在空域進行圖像處理，無需進行大量復雜的算術運算，算法簡單、快速、高效，易于硬件實現。通過十多年來的實驗與實踐證明，在微光圖像，紅外圖像，X光圖像處理領域，”共軛變換”圖像處理方法確實有其獨特的優異性能。本篇論文就針對”共軛變換”圖像處理方法在微光圖像處理領域的應用，就如何在FPGA上實現”共軛變換”圖像處理方法展開研究。首先在Matlab環境下，對常用的圖像增強算法和”共軛變換”圖像處理方法進行了比較，并且在設計制作“FPGA視頻處理開發平臺”的基礎上，用VHDL實現了”共軛變換”圖像處理方法的基本內核并進行了算法的硬件實現與效果驗證。此外，本文還詳細地討論了視頻流的采集及其編碼解碼問題以及I2C總線的FPGA實現。
上傳時間： 2013-04-24
上傳用戶：CHENKAI
