Discover coursera deep learning specialization, include the articles, news, trends, analysis and practical advice about coursera deep learning specialization on alibabacloud.com
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 machi
! 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
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
.
-Get more training samples
-Try to use a set with fewer features
-Try to obtain other features
-Try to add multiple combinations of features
-Try to reduce λ
-Add Lambda
Machine Learning (algorithm) diagnosis (Diagnostic) is a testing method that enables you to have a deep understanding of a Learning Algorithm and know what can be run and what cannot be run, it
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
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 M
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. Youk
Vision with Python: Techniques and Libraries for Imaging and Retrieving Information
@ Issac Syndrome has a complete answer. Here we will add two additional materials for deep learning:
Hinton Neural Network Course at coursera: https://www.coursera.org/course/neuralnets
On the other hand, if you do deep
Bengio, LeCun, Jordan, Hinton, Schmidhuber, Ng, de Freitas and OpenAI had done Reddit AMA's. These is nice places-to-start to get a zeitgeist of the field.Hinton and Ng lectures at Coursera, UFLDL, cs224d and cs231n at Stanford, the deep learning course at udacity, and the sum Mer School at IPAM has excellent tutorials, video lectures and programming exercises th
Ng's machine learning courses on Coursera are the best choice for getting started. In addition, Yaser Abu-mostafa's machine learning program is more focused on theory, but also suitable for beginners. Learning deep learning does
This article is from: Http://jmozah.github.io/links/Following is a growing list of some of the materials I found on the web for deep Learni ng Beginners. Free Online Books
Deep learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville
Neural Networks and deep learn
Deep learning has been fire for a long time, some people have been here for many years, and some people have just begun, such as myself.
How to get into this field quickly in a short period of time to master deep learning the latest technology is a question worth thinking about.
In the present situation, it is the best
Editor's note: Quora on the question: self-study machine learning technology, what advice do you have? (What is your recommendations for self-studying machine learning), Yann LeCun The answer under the question. This article by Lei Feng Net (public number: Lei Feng net) according to LeCun's reply collation, the original link: http://www.leiphone.com/news/201611/cWf2B23wdy6XLa21.htmlThere are a lot of materi
Original: http://blog.jobbole.com/87148/Editor's note "for an old question on Quora: What are the advantages of different classification algorithms?" Xavier Amatriain, a Netflix engineering director, recently gave a new answer, and in turn recommended the logic regression, SVM, decision tree integration and deep learning based on the principles of the Ames Razor, and talked about his different understanding
"Editor's note" for an old question on Quora: What are the advantages of different classification algorithms? Xavier Amatriain, a Netflix engineering director, recently gave a new answer, and in turn recommended the logic regression, SVM, decision tree integration and deep learning based on the principles of the Ames Razor, and talked about his different understandings. He does not recommend
basically recommend II possible directions: (1) SVM ' s, or (2) Tree ensembles. If I knew nothing on your problem, I would definitely go for (2), but I'll start with describing why SVM ' s might is S Omething worth considering.Support Vector Machines Support Vectors machines (SVMs) use a different loss function (Hinge) from LR. They is also interpreted differently (maximum-margin). However, in practice, an SVM with a linear kernel was not very different from a Logistic Regression (If You are cu
Large Data Digest Authorized reprint
Author: Huanghai
Since August 2016, Wunda's start-up deeplearning.ai through Coursera to provide the latest online course of in-depth learning, and by February, Miss Wu updated the fifth part of the course (click to view the report of the large Data Digest), which takes six months.
This article will focus on the fourth week of teacher Wunda's video content and notes, s
CLA Ssifier. But no worries, you'll build a even better classifier next week!
Also, you'll be clearly the model is overfitting the training data. Later in this specialization you would learn to reduce overfitting and for example by using regularization. Using the code below (and changing the "index variable") You can look at the in predictions on pictures of the test set. gradient descent gradual optimization loss function
Interpretation:You can dec
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