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A Beginners Guide to Deep Learning – #WeCoCreate – Medium

A Beginners Guide to #DeepLearning

  • [5]Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data.
  • In a deep network, there are many layers between the input and output (and the layers are not made of neurons but it can help to think of it that way), allowing the algorithm to use multiple processing layers, composed of multiple linear and non-linear Learning has revolutionized the machine…
  • These methods have dramaticallyimproved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics.
  • But, the ancient term “Deep Learning” was first introduced to Machine Learning by Dechter (1986)[10], and to Artificial Neural Networks (NNs) by Aizenberg et al (2000)[11].
  • It was further popularized by the development of Convolutional Networks Architecture by Alex Krizhevsky named ‘AlexNet’ that won the competition of ImageNet in 2012 by defeating all the image processing methods and creating a way for deep learning architectures to be used in Image Processing.

Machine-learning technology powers many aspects of modern society: from
web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present…

Machine-learning technology powers many aspects of modern society: from

web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products such as cameras and smartphones. Machine-learning systems are used to identify objects in images, transcribe speech into text, match news items, posts or products with users’ interests, and select relevant results of search.

Increasingly, these applications make use of a class of techniques called deep

Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. In a simple case, you could have two sets of neurons: ones that receive an input signal and ones that send an output signal. When the input layer receives an input it passes on a modified version of the input to the next layer. In a deep network, there are many layers between the input and output (and the layers are not made of neurons but it can help to think of it that way), allowing the algorithm to use multiple processing layers, composed of multiple linear and non-linear transformations.[1][2][3][4][5][6][7][8][9]

Deep Learning has revolutionized the machine learning recently with some of the great works being done in this field. These methods have dramatically

improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as…

A Beginners Guide to Deep Learning – #WeCoCreate – Medium

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