coursera tensorflow

NTU-Coursera ml: HomeWork 1 Q15-20Question15 The training data format is as follows: The input has four dimensions, and the output is {-1, + 1 }. There are a total of 400 data records. The question requires that the weight vector element be initialized to 0, and then "Naive Cycle" is used to traverse the training set. When the iteration is stopped, the weight vector is updated several times. The so-called "Naive Cycle" means that after an error i

This series is a personal learning note for Andrew Ng Machine Learning course for Coursera website (for reference only)Course URL: https://www.coursera.org/learn/machine-learning Exercise 7--k-means and PCA Download coursera-Wunda-Machine learning-all programming practice answers In this exercise, you will implement the K-means clustering algorithm and apply it to compressed images. In the second section, y

TensorFlow Neural Network Optimization Strategy Learning, tensorflow Network Optimization During the optimization of the neural network model, we will encounter many problems, such as how to set the learning rate. We can quickly approach the optimal solution in the early stage of training through exponential attenuation, after training, the system enters the optimal region stably. For the over-fitting probl

In China, Coursera is very choppy and often gets stuck when playing half of the video. I don't know why. Therefore, you can only download the file and view it again. There is a script on GitHub to open the link to download the entire course. It is very convenient to use. The method is as follows. Because this script uses multiple Python libraries, it is best to use the Linux system. I use Debian Wheezy and python2.7.3. Of course, you need a

Label: Ar c working time r as Rom net 5CATEGORY first, 1. skill category; 2. improvement category; 3. Interest category.I have completed the first six courses of Andrew Ng ml, UW computer network, and dataset cience on Coursera.In the future, the service will be guaranteed to be 25 hours a week, with an average of 2.5-3 hours per working day and 11 hours on weekends. In this way, three courses can be conducted at the same time in less than ten weeks, it also needs to be arranged according to the

Coursera-getting and Cleaning Data-week4Thursday, January,Make up the fourth week notes, and this course summary.The four-week course focuses on text processing. Inside includes1. Handling of variable names 2. Regular Expression 3. Date processing (see Swirl lubridate package exercise)First, the processing of variable names, followed by two principles, 1) uniform case tolower/toupper;2) Remove the import data, because special characters caused by the

Tensorflow creates variables and searches for variables by name. tensorflow Variables Environment: Ubuntu14.04, tensorflow = 1.4 (bazel source code installation), Anaconda python = 3.6 There are two main methods to declare variables:Tf. VariableAndTf. get_variable, The biggest difference between the two is: (1) tf. Variable is a class with many attribute function

Use tensorflow to implement the elastic network regression algorithm and tensorflow Algorithm This article provides examples of tensorflow's implementation of the elastic network Regression Algorithm for your reference. The specific content is as follows: Python code: # Using tensorflow to implement an elastic network algorithm (multi-variable) # using the iris d

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. The structure of ResNet can accelerate the tra

would the Vectorize this code to run without all for loops? Check all the Apply. A: v = A * x; B: v = Ax; C: V =x ' * A; D: v = SUM (A * x); Answer: A. v = a * x; v = ax:undefined function or variable ' Ax '. 4.Say you has a vectors v and Wwith 7 elements (i.e., they has dimensions 7x1). Consider the following code: z = 0; For i = 1:7 Z = z + V (i) * W (i) End Which of the following vectorizations correctly compute Z? Check all the Apply.

(w ')Description W over fitting3 Sources of errorNoise, Bias, Variance1. Noise NoiseOf an inherent, irreducible, or reduced nature.　　　2, Bias Deviation　　　　　　The simpler the model, the greater the deviation　　The more complex the model, the smaller the deviation3. Variance Variance　　　　Simple model, small variance　　Complex model, large variance　　Deviations and variance tradeoffs, deviations and variances cannot be calculated　　　　Training error and the amount of test data, fixed model complexity, a