machine learning with tensorflow book

The main learning and research tasks of the previous semester were pattern recognition, signal theory, and image processing, which in fact had more or less intersection with machine learning. As a result, we continue to read machine learning in depth and watch Stanford's

Learning notes for "Machine Learning Practice": Draw a tree chart use a decision tree to predict the contact lens type, The decision tree is implemented in the previous section, but it is only implemented using a nested dictionary containing tree structure information. Its representation is difficult to understand. Obviously, it is necessary to draw an intuitiv

generalization error;Easy to explain;Low computational complexity;Disadvantages:It is sensitive to the selection of parameters and kernel functions;The original SVM is only better at dealing with two classification problems;Boosting:Mainly take AdaBoost as an example, first look at the flow chart of AdaBoost, as follows:As you can see, we need to train several weak classifiers during training (3 in the figure), each weak classifier is trained by a sample of different weights (5 training samples

Aggregation (Bagging) AdaBoost Stacked Generalization (blending) Gradient boosting Machines (GBM) Random Forest This is an example of fitting using a combination method (from a wiki), each fire-fighting method is grayed out, and the final result of the synthesis is red.Other resourcesThis trip to machine learning algorithms is intended to give you a general idea of what algorithms and

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

of the total number of features with non-0 weights)9. Logistic regression : Two-dollar category, extremely efficient Giallo Computer System (many problems need to use probability estimates as output) two ways: "As is" "converted to two-dollar category" Application: Automatic diagnosis of disease (to investigate the risk factors that cause disease, and to predict the probability of disease occurrence according to risk factors), economic forecasts and other fieldsCategory: Evaluation indicators:

straight line, but it does not need to be guaranteed.That is, to tolerate those error points, but we have to add the penalty function so that the more reasonable the error points, the better. In fact, in many cases, the more perfect the classification function is not during training, the better, because some data in the training function is inherently noisy. It may be wrong when the classification label is manually added, if we have learned these error points during training (

We are now starting to train the model, and also enter a number of parameters such as the following:The number of factors in the rank:als. Generally, the bigger the better, but has a direct impact on memory usage, usually rank between 10 and 200.Iterations: The number of iterations, each iteration will reduce the reconstruction error of the ALS. After several iterations, the ALS model will converge to get a good result, so many iterations (typically 10 times) are not required in most cases.Lambd

We will now start training the model and enter the parameters as follows:The number of factors in the rank:als, usually the larger the better, but has a direct impact on memory usage, usually rank between 10 and 200.Iterations: The number of iterations, each iteration reduces the reconstruction error of the ALS. After several iterations, the ALS model converges to get a good result, so many iterations (usually 10 times) are not required in most cases.Lambda: The regularization parameter of the m

The last half month began to study Spark's machine learning algorithm, because of the work, in fact, there is no real start of machine learning algorithm research, but did a lot of preparation, now the early learning, learning and

combat", also take to practice practiced hand, Let your own python step by step, before a variety of web background toss, especially reptiles, but I do not want to help others crawl data, I want to analyze data, mining potential information, the program is a tool, master the business trend is the King!No nonsense, the next series of notes are my coursera above the understanding, according to their handwriting and "machine

Everyone seems to be excited about the new neural network architecture of the Capsule Network (capsnet), I am no exception, can not help to use the capsule network to establish a road side traffic signs identification system, this article is the introduction of this process, of course, also includes some basic concepts of the capsule network elaborated. The project, developed using TensorFlow, is based on the Sabour,nicholas Frosst and Geoffrey E. Hin

Machine Learning Headlines 2015-01-11January 12, 2015 09:41Machine Learning Headlines 2015-01-11 Machine Learning Handbook Elements of machines Learning @ Love Coco-love life Python implementation of random forest @

] = \displaystyle{\sum_{m=0}}mbin (m| N,\MU) =n\mu\)\ (Var[m] = \displaystyle{\sum_{m=0}} (M-\mathbb{e}[m]) ^{2}bin (m| N,\MU) =n\mu (1-\MU) \) Beta distribution (distribution) This section considers how to introduce a priori information into a binary distribution and introduce a conjugate priori (conjugacy prior)Beta distribution is introduced as a priori probability distribution, which is controlled by two hyper-parameters \ (A, b\). \ (Beta (\mu|a,b) =\frac{\gamma

At present, machine learning is one of the hottest technologies in the industry.With the rapid development of computer and network, machine learning plays a more and more important role in our life and work, and it is changing our life and work. From the daily use of the camera, daily use of the search engine, online e

makes these three together?Computer ProgramComputer programs determine how to use experience to solve tasks and ensure that tasks can be better solved as experience increases. The specific machine learning method is the core of these computer programs.Example of the original article:Checkers learning problems:Task T: checkersPerformance Standard P: Percentage of

Bishop's masterpiece "Pattern recognitionand machine learning" has long been stationed in my hard drive for more than a year, Zennai fear of its vast number of pages, has not dared to start. Recently read the literature, repeatedly quoted. Had to turn it over and prepare to read it carefully. If you have the conditions, you should also write a reading note, or basically also look at the side and forget.I sh

I. About the origins of the boosting algorithmThe boost algorithm family originates from PAC learnability (literal translation called Pac-Learning). This set of theories focuses on when a problem can be learned.We know that computable is already defined in computational theory, and that learning is what the PAC learnability theory defines. In addition, a large part of the computational theory is devoted to

Analysis of "Machine Learning Algorithm Series II" Logistic regression published in 2016-01-09 | Categories in Project Experience | | 12573 This article is inspired by Rickjin teacher, talk about the logistic regression some content, although already have bead Jade in front, but still do a summary of their own. In the process of looking for information, the more I think the LR is really profound, contains t

Machine Learning Algorithms and Python practices (7) Logistic Regression) Zouxy09@qq.com Http://blog.csdn.net/zouxy09 This series of machine learning algorithms and Python practices mainly refer to "machine learning practices. B