andrew ng coursera machine learning notes

Blog has migrated to Marcovaldo's blog (http://marcovaldong.github.io/)Just finished the last week of Cousera on machine learning . This week introduced one of the applications of machine learning: Photo OCR (optimal character recognition, optical character recognition), and the following are the

Blog has migrated to Marcovaldo's blog (http://marcovaldong.github.io/)Just completed the last week of Cousera on machine Learning , this week introduced one of the applications of machine learning: Photo OCR (optimal character recognition, Optical character recognition), follow the

does not introduce a matrix, which is easy to calculate and can be correctly executed if there are few samples. The multi-element model is complex to calculate after the matrix is introduced. to calculate the inverse of the matrix, the model must be executed when the sample value is greater than the feature value. ------------------------------------------Weak split line---------------------------------------------- Although exception detection is mentioned in this article, it is used to in

Download link:Stanford machine learning notesThis series of notes was organized from November 2013 to July 2014. All content is personal understanding. The reason for taking notes is to quickly remember the relevant methods later. Understanding errors is inevitable. please correct me if it is inappropriate.Notes are or

Open Course address: https://class.coursera.org/ml-003/class/index INSTRUCTOR: Andrew Ng1. unsupervised learning introduction (Introduction to unsupervised learning) We mentioned one of the two main branches of machine learning-supervised

July Algorithm-December machine Learning online Class -18th lesson Notes-Conditional random airport CRF July algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com1, logarithmic linear modelThe probability of an event is odds, which

1. What can machine learning do?Search engines, spam filtering, face recognition and so on, not only for the field of artificial intelligence, biological, medical, machinery and many other fields have been applied.2. Definition of machine learningA computer program was said to learn from experience E with respect to some task T and some performance measure p,if i

)-Kalman Smoother algorithm (very detailed derivation)approximate inference algorithms [PS]-Variational EM-Laplace approximation-Importance sampling-Rejection sampling-Markov chain Monte Carlo (MCMC) sampling-Gibbs Sampling-Hybrid Monte Carlo sampling (HMC)Belief Propagation (BP) [PS]-Introduction to BP and gbp:powerpoint presentation [PPT]-Converting directed acyclic graphical models (DAG) into junction trees (JT)-Shafer-shenoy belief propagation on junction trees-Some examplesBoltzmann

July Algorithm--December machine Learning online Class -11th lesson notes-random forest and ascension July algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com?Random forest: Multiple trees, dividing the current node is the most im

Supervised learning (supervised learning): The reason to call supervised learning is because we tell the algorithm what we want to predict. The so-called supervision, in fact, is whether our intentions can directly influence the forecast results. Typical representatives: Classification (classification) and regression (regression).Unsupervised

Eden space, from Survivor space, to survivor space and so on.Method area? Thread sharing.? Used to store data such as class information, constants, static variables, immediate compiler-compiled code, etc. loaded by the virtual machineRun a constant pool (runtime Constant)? Part of the method area. Chang (Constant Pool): Constant pool Data compilation period is determined and is part of the class file. Constants in classes, methods, interfaces, and so on are stored, including string con

points small there is a mistake, the whole is still satisfactory. But I don't know if you remember what I said before, K-means has a priori condition that the data satisfies the Gaussian distribution of the same variance, so we deliberately make the variance of the data to see if the clustering effect will be greatly affected.#方差不等数据data2, Y2=ds.make_blobs (n,centers=centers,cluster_std= (2,2,5,8), random_state=0) Plt.scatter (data2[:,0], DATA2[:,1],C=Y2,CMAP=CM) plt.title (U ' raw data distrib

1. use of MATLAB and octave2. Nouns to be understood (convexity optimization, implicit Markov chain)3. Some definitions of data mining:A computer application, assuming that there is a task T, then there is a performance measurement method p, under the influence of experience E, p on t measurement results are improved.4. Vector machine concept: used to transform an infinite dimension vector into a finite number of dimensions.5. Classification of

Probability statistics The relationship between probability statistics and machine learning Statistic Amount Expect Variance and covariance Important theorems and inequalities Jensen Inequalities Chebyshev on the snow Man's inequality Large number theorem The Central limit theorem The following excerpt from the July Algorithm (julyedu.com

: One-to-multiple ) Sometimes the problem is not as simple as determining whether a patient's tumor is malignant or benign. For example, determining whether the weather is sunny, cloudy, raining, Or snowing is necessary. We can use a line to separate binary classification. What about multiclass classification? There is a simple method, that is, to separate only one category at a time. There are several categories to construct several decision edge, that is, severalH (x): In th

Chapter I Fundamentals of machine learning1.1 Programming languages and development environments1.1.1 Python Installation (abbreviated)1.2.2 Installation of the Python installation package: Optional option to install the Integration Pack Anaconda (slightly)1.1.3 IDE Configuration and installation testThe IDE chooses the UltraEdit advanced text editor with the following configuration steps:(1) Select "Advanced"--"User Tools" command, 1.4 shown.Figure 1

Last year in Beijing participated in a big data conference organized by O ' Reilly and Cloudera, Strata , and was fortunate to have the O ' Reilly published hands-on machine learning with Scikit-learn and TensorFlow English book, in general, this is a good technical book, a lot of people are also recommending this book. The author of the book passes specific examples, Few theories and two mature Python fra

unsupervised learning: Clustering: The process of dividing a data set into multiple classes composed of similar objects Density Analysis: The process of describing statistical values If you select an appropriate algorithm: Selection basis: 1. Use algorithms. 2. Analyze or collect data. Selection process: 1. Select supervised learning or unsupervised learni