tensorflow for deep learning from linear regression to reinforcement learning

determined. Instead of defining decision boundaries with training sets, we use training sets to fit parameter $\theta$.If the training set and the decision boundary are shown on the plane, the effect should be similar.Another example in the next question, $5-x_{1}=\theta^{t}x$, when $5-x_{1}=\theta^{t}x>=0$ when there is $x_{1}The nonlinear decision boundary (non-linear decision boundaries), which has complex polynomial feature variables, obtains com

TensorFlow realize Classic Depth Learning Network (4): TensorFlow realize ResNet ResNet (Residual neural network)-He Keming residual, a team of Microsoft Paper Networks, has successfully trained 152-layer neural networks using residual unit to shine on ILSVRC 2015 , get the first place achievement, obtain 3.57% top-5 error rate, the effect is very outstanding. T

, there are miscellaneous things that are related to machine learning, math-related, and distributed.This series mainly want to be able to use mathematics to describe machine learning, want to learn machine learning, first of all to understand the mathematical significance, not necessarily to be able to easily and freely deduce the middle formula, but at least to

-between-Teslas-Autopilot-system-and-Googles-driver-less-car http://wccftech.com/tesla-autopilot-story-in-depth-technology/4/ Nguyen A, Yosinski J, Clune J. Deep Neural Networks is easily fooled:high confidence predictions for unrecognizable imag Es[c]//proceedings of the IEEE Conference on computer Vision and Pattern recognition. 2015:427-436. Gatys L A, Ecker a S, Bethge M. A Neural algorithm of artistic style[j]. ARXIV preprint arxiv:1508.

distributed.This series mainly want to be able to use mathematics to describe machine learning, want to learn machine learning, first of all to understand the mathematical significance, not necessarily to be able to easily and freely deduce the middle formula, but at least to know these formulas, or read some related papers can not read, This series will focus on the mathematical description of machine

based on statistics shows superiority in many aspects compared with the system based on artificial rules in the past. This time the artificial neural network, although also known as the Multilayer Perceptron (multi-layer perceptron), is actually a shallow layer model that contains only one layer of hidden layer nodes. In the the 1990s, various shallow machine learning models were proposed, such as support vector machines (svm,support vector machines)

tensorflow than the other. And you think Google, the big company, the pace of the update will certainly not be slow. Watch it. 4. If you want to easily and quickly build a neural network, Keras This module is very good, his bottom is tensorflow and Theano, so in Windows, MacOS, Linux can be used. 5. There are a lot of other modules that can be used, but in my personal machine-

transferred from: Http://www.cnblogs.com/LeftNotEasy Author: leftnoteasy regression and gradient descent: Regression in mathematics is given a set of points, can be used to fit a curve, if the curve is a straight line, that is called linear regression, if the curve is a two-time curve, is called two

dataset dimension to maximize the contribution of dataset variance. Low-level principal components are retained, and higher-level principal components are ignored. The most common Linear dimensionality reduction method.The compression process restricts the number of hidden neurons and learns meaningful features. It is expected that neurons will be restrained most of the time. Neuron output close to 1 is activated, close to 0 is blocked. Some neurons