Explore neural networks in psychology, their applications in cognitive modeling, clinical diagnosis, and research, and their impact on the field's future
Jeremy Jordan Sign in Machine Learning Neural networks: representation. Jeremy Jordan 28 Jun 2017 • 7 min read This post aims to discuss what a neural network is and how we represent it in a machine learning model. Subsequent posts will cover more advanced topics such as training and optimizing a model, but I've found it's helpful to first have a solid understanding of what it is we're actually building and a comfort with respect to the matrix representation we'll use. Prerequisites: Read my post on
- 线性回归 Linear Regression - 深度学习 Deep Learning - 神经网路 Neural Networks - 反向传播 Backpropagation - 卷积神经网路 Convolutional Neural Networks - 递归神经网路和长短期记忆模型 RNN & LSTM 使用机器学习 利用资料 - 如何获得高品质的资料 统计学 - 贝叶斯推断和各类机率 Bayesian Inference 一些建议 - 如何成为资料科学家 Powered by GitBook # 卷积神经网路 Convolutional Neural Networks # 卷积神经网络的运作原理
Michael Nielsen's online book on Neural Networks and Deep Learning. This book is a neural networks and deep learning tutorial
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Have you ever wondered how predictive text algorithm works? How exactly does that speech recognition software know our voice? As for image classification, convolutional neural networks were turning the whiles behind the scene, for these kinds of problems we are using Recurrent Neural Networks (RNN). These Neural Networks are very powerful and they are especially useful in so-called Natural Language
Fundamentals of convolutional neural networks
Let's unravel a new approach to improving the explainability and transparency of neural networks using an equivalent decision tree to represent them. Read more
Posts about Neural Networks written by Soumitra Sharma
Andrew Gibiansky :: Math → [Code] Check out Kronos Notebook , my new IPython -based Mac app for interactive computing and data analysis in Python or Haskell. --> Convolutional Neural Networks Monday, February 24, 2014 In the previous post , we figured out how to do forward and backward propagation to compute the gradient for fully-connected neural networks, and used those algorithms to derive the Hessian-vector product algorithm for a fully connected neural network. Next, let's figure out how to do the