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Neural networks and deep learning
Neural Networks and Deep Learning is a free online book. The book will teach you about: Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data Deep learning, a powerful set of techniques for learning in neural networks Neural networks and deep learning currently provide ...

Neural Networks - 3Blue1Brown
Gradient descent, how neural networks learn An overview of gradient descent in the context of neural networks. This is a method used widely throughout machine learning for optimizing how a computer performs on certain tasks. Chapter 2 Oct 16, 2017. Analyzing our neural network Chapter 3 Oct 16, 2017.

A Neural Network Playground
Next, the network is asked to solve a problem, which it attempts to do over and over, each time strengthening the connections that lead to success and diminishing those that lead to failure. For a more detailed introduction to neural networks, Michael Nielsen’s Neural Networks and Deep Learning is a good place to start.

Oklahoma State University–Stillwater
Oklahoma State University–Stillwater

- Visual Analysis for Recurrent Neural Networks
Recurrent neural networks, and in particular long short-term memory networks (LSTMs), are a remarkably effective tool for sequence processing that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time ...

Stanford University Computer Science
Stanford University Computer Science

DIA-NN: neural networks and interference correction enable deep ...
We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance i …

arXiv
arXiv

Multi-view Convolutional Neural Networks for 3D Shape Recognition
Multi-view Convolutional Neural Networks for 3D Shape Recognition. People. Hang Su; Subhransu Maji; Evangelos Kalogerakis; Erik Learned-Miller; Updates. A PyTorch implementation from our lab with a new shading style.; A sample Caffe implementation is now available in additional to the Matlab version.

arXiv.org
arXiv.org

 

         

 

 

 

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