advanced machine learning coursera

In the last 20 blog posts, many machine learning algorithms have been involved, and the appetite has been exhausted. I decided to officially start the system-based Machine Learning Theory and try to enter the practical stage, cover: Professor Yaser Abu-Mostafa of Caltech focuses on traditional statistical theory. Pro

The article is from Professor Andrew Ng of Stanford University's machine learning course, which is a personal study note for the course, subject to the contents of the original course. Thank Bo Master Rachel Zhang's personal notes, for me to do personal study notes provide a good reference and role models. §3. Logistic Regression of Logistic regression1 Classification classificationFirstly, the concep

Learning notes TF014: convolution layer, activation function, pooling layer, normalization layer, advanced layer, and tf014 pooling The CNN Neural Network Architecture contains at least one convolution layer (tf. nn. conv2d ). Single-layer CNN detection edge. Image Recognition and classification. Different layer types support convolution layers to reduce overfitting, accelerate the training process, and red

**************************************Note: This blog series is for bloggers to learn the "machine learning" course notes from Professor Andrew Ng of Stanford University. Bloggers deeply learned the course, do not summarize is easy to forget, according to the course plus their own to do not understand the problem of the addition of this series of blogs. This blog series includes linear regression, logistic

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ObjectiveSince machine learning is generated from computer science, image recognition originates from engineering. However, these activities can be seen as two aspects of the same field, and they have undergone a fundamental development in the past 10 years. In particular, when the image model has emerged as a framework for describing and applying probabilistic models, the Bayesian theorem (Bayesian methods

The relationship between probability statistics and machine learningProbability problem is known as the whole case of the decision sample (whole push individual)Statistical problem is reverse engineering of probability problem (individual pushing whole)In machine learning supervised learning, the model was first traine

Boring, adapt to the trend, learn the Python machine learning it.Buy a book, first analyze the catalogue it.1. The first chapter is the Python machine learning ecosystem.1.1. Data science or machine learning workflow.It is then di

multithreaded programming.In addition, when multithreaded programming:Focus on understanding: Thread synchronization Inter-thread communication Thread deadlock Line programmed: Suspend, stop, and resume 　　　　Reference Blog: http://blog.csdn.net/escaflone/article/details/10418651http://blog.csdn.net/longshengguoji/article/details/41126119Http://www.runoob.com/java/java-multithrea

Machine learning, data mining, and other In this book, we constantly mention "intelligence". What is "intelligence "? Are we talking about artificial intelligence? Or machine learning? What does it have to do with Data Mining and soft computing? In academia, the exact definition of the content introduced in this book h

Now machine learning algorithms in classification, regression, data mining and other issues on the use of a very broad, for beginners, may be heard ' algorithm ' or other exclusive nouns feel inscrutable, so many people are deterred, which makes many people in dealing with a lot of problems lost a very useful tool. Machine le