Deep learning and neural network

• What are the basics of deep learning?

  Deep Learning is a computer software that mimics the network of neurons in a brain. It is a subset of machine learning based on artificial neural networks with representation learning. It is called deep learning because it makes use of deep neural networks. This learning can be supervised, semi-supervised or unsupervised.

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• Do neural networks really work like neurons?

  Specifically, one fundamental question that seems to come up frequently is about the underlaying mechanisms of intelligence — do these artificial neural networks really work like the neurons in our brain? No.

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• What is deep learning and how does it work?

  You’re now prepared to understand what Deep Learning is, and how it works. Deep Learning is a machine learning method. It allows us to train an AI to predict outputs, given a set of inputs. Both supervised and unsupervised learning can be used to train the AI.

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• [PDF File]Deep Learning - Stanford University

  https://info.5y1.org/deep-learning-and-neural-network_1_cccc5e.html

  Jul 22, 2019 ·

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• [PDF File]Deep Learning - Stanford University

  https://info.5y1.org/deep-learning-and-neural-network_1_3be73e.html

  With these generic algorithms, a typical deep learning model is learned with the following steps. 1. De ne a neural network parametrization h (x), which we will introduce in Section 2, and 2. write the backpropagation algorithm to compute the gradient of the loss function J(j)( ) e ciently,

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• [PDF File]The Principles of Deep Learning Theory arXiv:2106.10165v2 [cs ...

  https://info.5y1.org/deep-learning-and-neural-network_1_428709.html

  community in practice: we want to study deep neural networks. In particular, this means that (i) a number of special results on single-hidden-layer networks will not be discussed and (ii) the infinite-width limit of a neural network – which corresponds to a zero-hidden-layer network – will be introduced only as a starting point. All such ...

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• [PDF File]IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS ...

  https://info.5y1.org/deep-learning-and-neural-network_1_48cdc7.html

  Deep Learning We now begin our study of deep learning. In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. 1 Supervised Learning with Non-linear Mod-els In the supervised learning setting (predicting y from the input x), suppose our model/hypothesis is ...

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• [PDF File]Deep Learning - Stanford University

  https://info.5y1.org/deep-learning-and-neural-network_1_c31dbc.html

  Deep Learning We now begin our study of deep learning. In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. 1 Neural Networks We will start small and slowly build up a neural network, step by step. Recall

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• [PDF File]Introduction to Deep Learning & Neural Networks - Data-X

  https://info.5y1.org/deep-learning-and-neural-network_1_46f12a.html

  Pros of Neural Nets 1. It finds the best function approximation from a given set of inputs, we do not need to define features. 2. Representational Learning a. Used to get word vectors b. We do not need to handcraft image features Cons of Neural Nets 1. It needs a lot of data, heavily parametrized by weights

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