To understand tensorflow computation graph, you need to know how to print or plot it. If you can visualize the computation graph, it would be easy to understand it. The […]

np.random.seed(4) m = 60 w1, w2 = 0.1, 0.3 noise = 0.1 angles = np.random.rand(m) * 3 * np.pi / 2 – 0.5 X = np.empty((m, 3)) X = np.empty((m, […]

import numpy as np X=2*np.random.rand(100,1) y=4+3*X+np.random.randn(100,1) X_b=np.c_[np.ones((100,1)),X]# add x0 = 1 to each instance theta_best=np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y) numpy.rand(100,1) generates a 100*1 array, whose elements are random number ranging from 0 to 1. numpy.randn(100,1) generates a 100*1 array whose elements […]

We’ve learnt sklearn KFold. KFold splits an array into several groups. If the elements in the array are associated with a label/class, there would raise a problem. The ration of […]

KFold is a class in the model_selection module of sklearn package. The usage of KFold is simple: kfold=KFold(n_splits,shuffle, random_state) Now, a KFold object is ready. Notice that the data to […]

An object is called an iterable object if it has an __iter__ method. List, tuple, dict are all iterable objects[reference]: The __iter__ method returns an iterator object. An object is […]

In the chapter 3 of the book “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, the author plays a big joke with the readers. The fetch_mldata function in the python module […]

If you are learning machine learning on your computer. The first thing would be running the jupyter app to do some hands-on. Running this program is not similar to running […]