best machine learning book

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. Learning with large datasets ( Big Data Learning ) The importance of data volume has been mentioned in the previous lecture on machine learning design. Remember this sentence: It is not who has the best algorithm that w

I. BACKGROUND In machine learning, there are 2 great ideas for supervised learning (supervised learning) and unsupervised learning (unsupervised learning) Supervised learning, in layman

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 foundation, first review, and then open the follow-up

to the right in this image. We can generally see the two learning curves, the two curves of blue and red are approaching each other. Therefore, if we extend the curve to the right, it seems that the training set error is likely to increase gradually. The cross-validation set error will continue to decline. Of course, we are most concerned with cross-validation set errors or test set errors. So from this picture, we can basically predict that if we co

numeric Type- int, float, long, complex There are four different numeric types: ordinary integers, long integers, floating-point numbers, and complex numbers A normal integer (or short integer) is implemented using a long in C with a precision of at least 32 bits (Sys.maxint is always set to the maximum normal integer value of the current platform, and the minimum value is -sys.maxint - 1). long integers have infinite precision. floating-point numbers are usually implemented with a double in C

"Python Machine learning and practice – from scratch to the road to Kaggle race" very basicThe main introduction of Scikit-learn, incidentally introduced pandas, NumPy, Matplotlib, scipy.The code of this book is based on python2.x. But most can adapt to python3.5.x by modifying print ().The provided code uses Jupyter Notebook by default, and it is recommended to

Machine Learning Summary (1), machine learning SummaryIntelligence:The word "intelligence" can be defined in many ways. Here we define it as being able to make the right decision based on certain situations. Knowledge is required to make a good decision, and this knowledge must be operable, for example, interpreting se

Https://github.com/josephmisiti/awesome-machine-learning#julia-nlp Julia General-purpose Machine Learning Machinelearning-julia Machine Learning LibraryMlbase-a set of functions to support development of

July online April machine learning algorithm class notes--no.1 Objective Machine learning is a multidisciplinary interdisciplinary, including probability theory, statistics, convex analysis, feature engineering and so on. Recently followed the July algorithm to learn the knowledge of

in the process of learning rate can be seen as the length of the descent process, assuming that your step is very big can cross the valley directly on the opposite side of the mountain, it is difficult to get the local optimal solution. At this point, reducing the step size will increase your chances of going to the ground.2. About the cross fittingBy using the methods of drop out, batch normalization and data argument, the generalization ability of