machine learning certification coursera

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[Original] Andrew Ng chose to fill in the blanks in Coursera for Stanford machine learning.

Week 2 gradient descent for multiple variables [1] multi-variable linear model cost function Answer: AB [2] feature scaling feature Scaling Answer: d 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: [Original] Andrew Ng chose to fill in the blanks in Coursera for Stanford

Coursera Machine Learning Study notes (vii)

-Gradient descent for linear regressionHere we apply the gradient descent algorithm to the linear regression model, we first review the gradient descent algorithm and the linear regression model:We then expand the slope of the gradient descent algorithm to the partial derivative:In most cases, the linear regression model cost function is shaped like a convex body, so the local minimum value is equivalent to the global minimum:The following is the entire convergence and parameter determination pr

Coursera-machine Learning, Stanford:week 11

Overview photo OCR problem Description and Pipeline sliding Windows getting Lots of data and Artificial data ceiling analysis:what part of the Pipeline to work on Next Review Lecture Slides Quiz:Application:Photo OCR Conclusion Summary and Thank You Log 4/20/2017:1.1, 1.2; Note Ocr? ... Coursera-

Coursera Machine Learning second week quiz answer Octave/matlab Tutorial

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.

Coursera Machine Learning Study notes (12)

-Normal equationSo far, the gradient descent algorithm has been used in linear regression problems, but for some linear regression problems, the normal equation method is a better solution.The normal equation is solved by solving the following equations to find the parameters that make the cost function least:Assuming our training set feature matrix is x, our training set results are vector y, then the normal equation is used to solve the vector:The following table shows the data as an example:T

Notes | Wunda Coursera Deep Learning Study notes

Programmers who have turned to AI have followed this number ☝☝☝ Author: Lisa Song Microsoft Headquarters Cloud Intelligence Advanced data scientist, now lives in Seattle. With years of experience in machine learning and deep learning, we are familiar with the requirements analysis, architecture design, algorithmic development and integrated deployment of

What are some of the learning Python, data analysis courses on Coursera?

! I've been on this course 3 years ago, and it's been a long time ... Before going to bed to see this question, the day before yesterday wrote an article about learning Python in Coursera, just right question, so excerpt part, hope to be helpful:-) Let's talk about the process of learning Python in Coursera (and reco

What courses are worth learning about Python and data analysis on coursera?

friends leave a message saying they are already charged. Let's go to the official website and check it out! I have taken this course three years ago. It takes a long time ...... I saw this problem before I went to bed. I wrote an article about learning python in coursera the day before yesterday, which is just the right question. So I want to extract some of it and hope it will help me :-) Next, let's ta

Operating system Learning notes----process/threading Model----Coursera Course notes

Operating system Learning notes----process/threading Model----Coursera Course note process/threading model 0. Overview 0.1 Process ModelMulti-Channel program designConcept of process, Process control blockProcess status and transitions, process queuesProcess Control----process creation, revocation, blocking, wake-up 、...0.2 threading ModelWhy threading is introducedThe composition of the threadImplementatio

Rhcsa/rhce Red Hat Linux certification Learning Guide (6th): ex200 & ex300

questions of the experiment are the same as the format, style, subject, and difficulty of the actual test. All test subjects are included: virtual Machine and automatic installation basic command line skills rhcsa-Level Security Options Guide processes Linux File System management package management users manage rhcsa-level system management rhce security system services and SELinux rhce management Mail Server Apache samba File Sharing DNS, FTP, and

RHCE7 Certification Learning Note 17--kickstart installation system

/ks.cfg11. Create a new virtual machine, start the virtual machine, boot from the PXE network, and the system will be installed automatically-----------------------------Split Line-----------------------------Unattended installation with Pxe+dhcp+apache+kickstart CentOS5.8 x86_64 http://www.linuxidc.com/Linux/2012-12/76913p4.htmLinux PXE Unattended installation appears pxe-e32:tftp OPen Timeout Solution htt

Machine Learning deep learning natural Language processing learning

and the contrast divergence algorithm, and is also an active catalyst for deep learning. There are videos and materials .L Oxford Deep LearningNando de Freitas has a full set of videos in the deep learning course offered in Oxford.L Wulide, Professor, Fudan University. Youku Video: "Deep learning course", speaking of a very master style. Other reference

Machine learning fundamentals and concepts for the foundation course of machine learning in Tai-Tai

some time ago on the Internet to see the Coursera Open Classroom Big Machine learning Cornerstone Course, more comprehensive and clear machine learning needs of the basic knowledge, theoretical basis to explain. There are several more important concepts and ideas in foundati

Tai Lin Xuan Tian Machine learning course note----machine learning and PLA algorithm

vectors or the longer the length of the vector, the following to deal with the length of the vector.Using the nature of the PLA's "Fault only Update", in the case of making mistakes, through the above deduction, the final conclusion is that the square of WT length increases the square of xn longest length after each update.Using the conclusion of the first proof, the derivation process is as follows:The above is known as three conditions, there are two points to be explained:1) Because the valu

Machine learning Techniques--1–2 speaking. Linear Support Vector Machine

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

The best introductory Learning Resource for machine learning

build a model from a browser. Pick out a platform and use it when you actually learn machine learning. Do not talk on paper, to practice!Video Courses Videos CourseMany people start to learn from the machine through video resources. I saw a lot of video resources related to machine

Andrew Ng's Machine Learning course learning (WEEK5) Neural Network Learning

This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This course is a choice for those who want to understand and master

Machine Learning 001 Deeplearning.ai Depth Learning course neural Networks and deep learning first week summary

Deep Learning SpecializationWunda recently launched a series of courses on deep learning in Coursera with Deeplearning.ai, which is more practical compared to the previous machine learning course. The operating language also has MATLAB changed to Python to be more fit to the

Machine Learning Public Lesson Note (7): Support Vector machine

linear kernel)The neural network works well in all kinds of n, m cases, and the defect is that the training speed is slow.Reference documents[1] Andrew Ng Coursera public class seventh week[2] Kernel Functions for machine learning applications. http://crsouza.com/2010/03/kernel-functions-for-machine-

Detailed description of the "machine Learning enthusiast" project and its website by Dr. Huanghai

have been standing behind the scenes, and some things all the ins and outs only I know, because I and Dr. Huanghai, NetEase Cloud class, Professor Wunda and Coursera GTC translation platform, Deeplearning.ai official have had exchanges, so I still have to leave something as a description, Save everyone in the network every day noisy ah did not calm down to study seriously. As mentioned in this article, I have a chat record to support, some of the auth

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