Direct Quote : A direct quote is when you use another persons words directly in your paper. Knowing when to use a direct quote is important. Do not quote everything you want to say. Most things should be paraphrased. Use a direct quote when you want the reader to read an important historical line or it is something someone said that is important. Use direct quotes sparingly, there should only be a few in the paper and they better be good ones. The key difference in citing a direct quote is that you must put quotation marks around the sentence and then cite at the end. IF YOU FAIL TO USE QUOTATION MARKS AROUND A DIRECT QUOTE YOU ARE SAYING YOU WROTE THE SENTENCE. THIS IS PLAGIARISM!!! More information on direct quotes and direct quotes over four lines to follow.
For an excellent source on English composition, check out this classic book by William Strunk, Jr. on the Elements of Style. Contents include: Elementary Rules of Usage, Elementary Principles of Composition, Words & Expressions Commonly Misused, An Approach to Style with a List of Reminders: Place yourself in the background, Revise and rewrite, Avoid fancy words, Be clear, Do not inject opinion, Do not take shortcuts at the cost of clarity, … and much more. Details of The Elements of Style by William Strunk, Jr. partially available online at . Note: William Strunk, Jr. (1869–1946). The Elements of Style was first published in 1918.
We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable. Our approach – Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say ‘dog’ in a classification network or a sequence of words in captioning network) flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, GradCAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers (. VGG), (2) CNNs used for structured outputs (. captioning), (3) CNNs used in tasks with multi-modal inputs (. visual question answering) or reinforcement learning, all without architectural changes or re-training.