Deep Learning v0.1 documentation

Hyperparameter optimization is a big part of deep learning. The reason is that neural networks are notoriously difficult to configure and there are a lot of parameters that need to be set. On top of that, individual models can be very slow to train.

AlexNet / VGG-F network visualized by mNeuron. Project 6: Deep LearningIntroduction to Computer Vision Brief Due date: Tuesday, December 6th, 11:55pm Project materials including starter code, training and testing data, and html writeup template: proj

Learning Deep Learning with Keras Piotr Migda? - blog Projects Articles Publications Resume About Photos Learning Deep Learning with Keras 30 Apr 2017 ? Piotr Migda? ? [machine-learning] [deep-learning] [overview] I teach deep learning both for a liv

Machine and Deep Learning with Python Education Tutorials and courses Supervised learning superstitions cheat sheet Introduction to Deep Learning with Python How to implement a neural network How to build and run your first deep learning network Neur

Decision Boundaries for Deep Learning and other Machine Learning classifiers H2O, one of the leading deep learning framework in python, is now available in R. We will show how to get started with H2O, its working, plotting of decision boundaries and

How to build and run your first deep learning network Step-by-step instruction on training your own neural network. by Pete Warden | @petewarden | Comments: 8 | July 23, 2014 Share on twitter Share on google_plusone_share Share on facebook Share on l

Setting up a Deep Learning Machine from Scratch (Software) A detailed guide to setting up your machine for deep learning research. Includes instructions to install drivers, tools and various deep learning frameworks. This was tested on a 64 bit machi

About this Course If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "s

近期開始看一些深度学习的资料.想学习一下深度学习的基础知识.找到了一个比較好的tutorial,Neural Networks and Deep Learning,认真看完了之后觉得收获还是非常多的.从最主要的感知机開始讲起.到后来使用logistic函数作为激活函数的sigmoid neuron,和非常多其它如今深度学习中常使用的trick. 把深度学习的一个发展过程讲得非常清楚,并且还有非常多源代码和实验帮助理解.看完了整个tutorial后打算再又一次梳理一遍,来写点总结.以后再看其它资料