tensorflow for deep learning from linear regression to reinforcement learning

++ = 1.0 currline = line. strip (). split ('\ t') linearr = [] For I in range (21): linearr. append (float (currline [I]) If int (classifyvector (Array (linearr), trainweights ))! = Int (currline [21]): errorcount + = 1 errorrate = (float (errorcount)/numtestvec) print 'the error rate of this test is: % F' % errorrate return errorratedef multitest (): numtests = 10; errorsum = 0.0 for K in range (numtests): errorsum + = colictest () print 'after % d iterations the average error rate is: % F' %

In deep learning, regardless of the learning framework, we encounter an important problem, that is, after training, how to store the depth of the network parameters. How these network parameters are invoked at the time of the test. In response to these two questions, this blog post explores how TensorFlow solves them.

The Wunda "Deep learning engineer" Special course includes the following five courses: 1, neural network and deep learning;2, improve the deep neural network: Super parameter debugging, regularization and optimization;3. Structured machine

optimal value. cost function(Cost function): Price functions are used to solve the theta value of the optimal solution, because of the two-dollar classification problem, Y is always either 0 or 1 (0,1 for category label, of course, can also be 1,2~~) Therefore, the costing function is as follows: In summary, we get the cost function: Gradient descent algorithm(Gradient descent): Solving parameter theta value It is not difficult to find that the above appearance and

linear kernel function support vector machine is: 27.0063071393243 the mean absolute error of the linear kernel function support vector machine is: 3.426672916872753 The default evaluation value for the polynomial kernel function is: 0.40445405800289286 The r_squared value of the polynomial kernel function is: 0.651717097429608 the mean square error of the polynomial kernel function is: 27.0063071393243 th

Exercise:vectorization PREPROCESSING:PCA and Whitening Pca Whitening Implementing pca/whitening EXERCISE:PCA in 2D EXERCISE:PCA and Whitening Softmax Regression Softmax Regression Exercise:softmax Regression Self-taught learning and unsupervised Feature

The topic of machine learning techniques under this column (machine learning) is a personal learning experience and notes on the Machine Learning Techniques (2015) of Coursera public course. All the content is from Coursera public class machine learning techniques Hsuan-tien

expressive power of deep architectures." Algorithmic learning theory. Springer Berlin/heidelberg, 2011. Montufar, Guido F., and Jason Morton. "When Does a Mixture of products contain a Product of mixtures?" ARXIV preprint arxiv:1206.0387 (2012). Montúfar, Guido, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio. The number of Linear regions of

has a separate weight, which is equivalent to introducing nonlinearity into the model, which can enhance the model expression ability and enlarge the fitting.4. The discretization can be characterized by crossover, from M+n variable to m*n variable, further introducing Non-linear, enhance the expression ability.5. Feature discretization, the model will be more stable, for example, if the user age discretization, 20-30 as an interval, not because a us

Deep Learning (depth learning) Learning notes finishing (ii) Transferred from: http://blog.csdn.net/zouxy09 Because we want to learn the characteristics of the expression, then about the characteristics, or about this level of characteristics, we need to understand more in-depth point. So before we say

Deep Learning (depth learning) Learning notes finishing Series[Email protected]Http://blog.csdn.net/zouxy09ZouxyVersion 1.0 2013-04-08Statement:1) The Deep Learning Learning Series is a

[Email protected]Http://blog.csdn.net/zouxy09ZouxyVersion 1.0 2013-04-081) The Deep Learning Learning Series is a collection of information from the online very big Daniel and the machine learning experts selfless dedication. Please refer to the references for specific information. Specific version statements are also

Amazon open machine learning system source code: Challenges Google TensorFlowAmazon took a bigger step in the open-source technology field and announced the opening of the company's machine learning software DSSTNE source code. This latest project will compete with Google's TensorFlow, which was open-source last year. Amazon said that DSSTNE has excellent perform