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深度學習並不是研究者們憑空創造出來的運算技術，它是模仿神經網路的運算模式，以多節點、分層的運算來分析圖片上的特徵，最低層的節點們只計算每一個像素上的黑白對比，第二層的節點則根據第一層的資料、以連續的對比來分辨出線條與邊界，隨著層級越來越高、累積的計算資訊越來越複雜，就可以對圖片進行辨認與分類。
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Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.
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Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.
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Deep learning uses neural networks (DNNs) many layers deep and large datasets to teach computers how to solve perceptual problems, such as detecting recognizable concepts in data, translating or understanding natural languages, interpreting information from input data, and more.
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This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems.
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Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.
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Deep Learning是機器學習中一個非常接近AI的領域，其動機在於建立、模擬人腦進行分析學習的神經網络，最近研究了機器學習中一些深度學習的相關知識。
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An MIT Press book in preparation
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Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning.
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Deep learning algorithms attempt to learn multi-level representations of data, embodying a hierarchy of factors that may explain them. Such algorithms have been demonstrated to be effective both at uncovering underlying structure in data, and have been successfully applied to a large variety of problems ranging from image classification, to natural language processing and speech recognition.
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A new trend in AI, specifically in machine learning, known as “Deep Learning”. In this tutorial, I’ll introduce you to the key concepts and algorithms behind deep learning, beginning with the simplest unit of composition and building to the concepts of machine learning in Java.
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This resurgence has been powered in no small part by a new trend in AI, specifically in machine learning, known as “Deep Learning”.
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