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麻省理工學院釋出英文履歷範本!新鮮人一定要學會的CV寫法|數位時代. 編按:六月是畢業季,應屆畢業生們想必正如火如荼地四處投遞履歷、找工作。 麻省理工學院(MIT)的Career Center特別發布了履歷的撰寫技巧,也大方釋出英文履歷範本與動詞列表給大家參考,快來看看世界頂尖學府的履歷教學吧! 履歷呈現了你的過往經歷,通常也是面試官對你的第一印象。 招聘人員平均只會花幾秒鐘的時間來查看每份履歷,因此,學會使用讓重要的訊息能立即可見的格式非常重要。 一份好的履歷可以協助你繼續進行到面試,但即使是微小的錯誤也可能讓你被淘汰。 1. 雖然你可能留有一份詳細說明所有經歷和獎項的「大師級履歷」,但是在應徵工作時,你必須寫一份針對特定職缺或企業的客製化履歷。 2. 履歷的模板會隨著時間的推移越來越難編輯,所以你最好從空白的頁面開始,並隨時對照履歷範本。

3. 履歷裡不需要完整的句子,你應該避免使用第一人稱(I, me, my)。 如果可以,請描述你如何執行,而不僅僅是你做了什麼。 4. 表達你具有影響力的最好方式是寫出具體的成就,你的經歷描述不應該讀起來像工作描述一樣。 5. 最簡單的錯誤可以讓所有的努力都功虧一饋。 其他履歷小技巧: 履歷中不要提及你的年齡、宗教、健康或婚姻狀況等等個人信息。 MIT釋出的資源在這裡: 英文履歷範本 履歷常用的動作動詞列表 How to write a killer resume: 本文授權轉載自風傳媒,原文請點此. No, Google Duplex Hasn’t Passed the Turing Test – Member Feature Stories. Am I the only one who feels a tad underwhelmed by Google Duplex? My social media feeds are filled with gushing platitudes over the virtual assistant’s handling of a simple haircut appointment. Some are even claiming the Turing Test has been passed. Putting aside the ethical ramifications of abdicating such tasks to the robots (a topic worthy of its own post), I’m left wondering: is this how low we’ve set the bar for human conversation? Google Duplex may score top marks for authentic tone and delightful mannerisms, but the topic of conversation was hardly riveting. The virtual assistant negotiated a hair appointment through rote responses with dialogue that left no room for spontaneity.

I might imagine a small handful of calls that I would abdicate to a robotised assistant. I gain no pleasure from ordering prescriptions or querying bills over the phone. The fawning over technological feats might say more about how readily we lower the bar for human potential. Histopathology. Collection of tissues[edit] Preparation for histology[edit] The tissue is then prepared for viewing under a microscope using either chemical fixation or frozen section. If a large sample is provided e.g. from a surgical procedure then a pathologist looks at the tissue sample and selects the part most likely to yield a useful and accurate diagnosis - this part is removed for examination in a process commonly known as grossing or cut up.

Larger samples are cut to correctly situate their anatomical structures in the cassette. Certain specimens (especially biopsies) can undergo agar pre-embedding to assure correct tissue orientation in cassette & then in the block & then on the diagnostic microscopy slide. Chemical fixation[edit] In addition to formalin, other chemical fixatives have been used. Processing[edit] Once the wax embedded block is finished, sections will be cut from it and usually placed to float on a waterbath surface which spreads the section out. Frozen section processing[edit] Digital pathology. Digital pathology is an image-based information environment which is enabled by computer technology that allows for the management of information generated from a digital slide. Digital pathology is enabled in part by virtual microscopy, which is the practice of converting glass slides into digital slides that can be viewed, managed, shared and analyzed on a computer monitor.

With the advent of Whole-Slide Imaging, the field of digital pathology has exploded and is currently regarded as one of the most promising avenues of diagnostic medicine in order to achieve even better, faster and cheaper diagnosis, prognosis and prediction of cancer and other important diseases. Potential[edit] In pathology, trained pathologists look at tissue slides under a microscope. The tissue on those slides may be subjected to staining to highlight structures. When those slides are digitized, they then have the potential to be shared (tele-pathology) and numerically analyzed using computer algorithms. AIMIA Inbox. Neural Networks, Manifolds, and Topology -- colah's blog.

Posted on April 6, 2014 topology, neural networks, deep learning, manifold hypothesis Recently, there’s been a great deal of excitement and interest in deep neural networks because they’ve achieved breakthrough results in areas such as computer vision. However, there remain a number of concerns about them. One is that it can be quite challenging to understand what a neural network is really doing. If one trains it well, it achieves high quality results, but it is challenging to understand how it is doing so. If the network fails, it is hard to understand what went wrong. While it is challenging to understand the behavior of deep neural networks in general, it turns out to be much easier to explore low-dimensional deep neural networks – networks that only have a few neurons in each layer.

A number of interesting things follow from this, including fundamental lower-bounds on the complexity of a neural network capable of classifying certain datasets. A Simple Example Topology of tanh Layers. Machine Learning Crash Course  |  Google Developers. Neural networks and deep learning. 「極簡機器學習」,從少量數據中學習精確特徵的卷積神經網絡. 小鼠淋巴母細胞的切片。 (a)原始圖像;(b)人工切割得到的切片;(c)100 層的 MS-D 網絡的輸出(數據來自 A.Ekman, C. Larabell, National Center for X-ray Tomography) 能源部勞倫斯伯克利國家實驗室(Berkeley Lab)的數學家們開發了一種針對實驗性成像數據的新的機器學習方法。 這種新方法不像典型的機器學習方法一樣需要數萬或數十萬張圖像用於訓練——它可以在使用更少的圖像的同時,更快地進行學習。 伯克利實驗室的 CAMERA(能源高級數學研究與應用中心,Center for Advanced Mathematics for Energy Research Applications)的 Daniël Pelt 和 James Sethian 開發了一種新算法,他們將這種算法稱爲「多尺度密集卷積神經網絡」(MS-D,Mixed-Scale Dense Convolution Neural Network)。

隨着實驗設備可以更快地產生更高分辨率的圖像,科學家們可能難以通過人工方式對產生數據進行管理和分析。 Sethian 同時也是 UC Berkeley 的數學教授,他認爲:「在科學應用中,需要大量人力對實驗圖像進行註釋和標記——可能需要幾周才能得到一些批註過的圖像。 該算法的相關論文發表於 2017 年 12 月 26 日的美國國家科學院院刊上(見文末)。 Pelt 是荷蘭數學與計算科學研究所下屬的計算成像小組的成員,他介紹說:「這項突破源於我們意識到,不同圖像尺度的特徵提取的縮放運算,可以用能處理多個尺度的數學卷積的一層所代替。」 爲了得到更廣泛的應用,Olivia Jain 和 Simon Mo 領導的伯克利團隊建立了門戶網站「圖像數據標記引擎(SlideCAM,Segmenting Labeled Image Data Engine)」 該算法還可用於理解生物細胞內部結構,這一應用也非常有前景。 國家 X 射線斷層成像中心主任、加利福尼亞大學舊金山醫學院的教授 Carolyn Larabell 說:「我們實驗室中的主要工作是瞭解細胞的形態結構是如何影響和控制細胞行爲的。 國家 X 射線斷層成像中心位於 ALS(Advanced Light Source,先進光源實驗室),伯克利實驗室的美國能源部科學用戶設備辦公室。 Mixed Scale Dense network. The New York Times - Breaking News, World News & Multimedia.