best machine learning book

This time to attend the reading activities held by CSDN, is catching up with the project busy, see is also intermittent, dragged for 2 weeks, to write this book review. First of all, I'll be sure to tell you, whether you're a professional Linux system administrator, a software developer in a Linux environment, or a Linux user, this book is worth reading. When Michael was writing this

After 2 months of knowledge of machine learning. I've found that machine learning has a variety of directions. Page sort. Speech recognition, image recognition, recommender system, etc. Algorithms are also varied. After seeing the other books, I found that except for the K-mean clustering. Bayesian, neural network, onl

Support vector machine-SVM must be familiar with machine learning, Because SVM has always occupied the role of machine learning before deep learning emerged. His theory is very elegant, and there are also many variant Release vers

that testing is inevitable (this topic is not discussed here), this article comprehensively introduces a testing system in a short space, in addition, I clicked on what the author thinks the reader should know (in contrast, the first article deviated from the topic ). Although I personally disagree with (some specific) tests to ensure correctness, I still learned a lot. On the other hand, the author has not declared the question "who is more. There is a big difference between the authors in the

': theUserphone = input ('Please enter contact Phone:') +Addressbook[name] =Userphone A Print(AddressBook) the Continue + Else: - Continue $ Else: $Userphone = input ('Please enter contact Phone:') -addressbook[name]=Userphone - Print('Contact added successfully! ') the Print(AddressBook) - ContinueWuyi ifitem = = 3:#Delete a contact the ifNameinchAddressBook: - delAddr

sentences, and these sentences are sorted by speech time. In this way, readers can see the "context" of the relevant discourse and the whole process of historical evolution clearly, so as to facilitate further study, research and analysis of related problems and raise the level of cognition. This is the advanced deep learning feature offered by e-books. It is not easy to find out all the relevant statements (collections) by stepping back to the trad

computer, and each instruction represents one or more operations.Give a simple example, and you can use it in your life. Now make a small game, a on the paper randomly wrote a 1 to 100 integer, b to guess, guess the game is over, guess the wrong word a will tell B guess small or big. So what will b do, the first time you must guess 50, guess the middle number. Why is it? Because of this worst case scenario (log2100">Log2log2100) Six or seven times can be guessed.This is a binary search, which m

Python machine learning decision tree and python machine Decision Tree Decision tree (DTs) is an unsupervised learning method for classification and regression. Advantages: low computing complexity, easy to understand output results, insensitive to missing median values, and the ability to process irrelevant feature da

Non-supervised learning: In this learning mode, the input data part is identified, the part is not identified, the learning model can be used for prediction, but the model first needs to learn the internal structure of the data in order to reasonably organize the data to make predictions. The application scenarios include classification and regression, and t

we use is to connect the Virtual Machine bridge to the physical network, occupying the IP address of the physical LAN, to achieve communication between the virtual machine and the physical machine and cross-Physical Machine Communication. Build a virtual machine again, t

imagenet by deep learning, and the deep learning model, represented by CNN, is now a bit exaggerated, borrowed from the Chinese University of Hong Kong Prof. Xiaogang Wang Teacher's summary article, Deep learning is nothing more than the traditional machine feature learning

.ManagementGigabytes, A good book for information retrieval. 7.Information Theory: inference and learningAlgorithmsFor more information, see. Related mathematical BASICS (reference books are not suitable for general reading ): 1. Linear Algebra: This reference book will not be listed. 2. Matrix mathematics:Matrix Analysis, RogerHorn. Classic in the field of matrix analysis. 3.Probability Theory and sta

Through a period of time iOS learning completed a simple application, "private Address Book".Operating effects such as:1. Login Page2. List of contacts3. Add4. Editing5. Delete6. LogoutThe total view structure is as follows:Summary This procedure is the knowledge point1, the use of nsnavigation controller to manage each controller.2. Use nskeyedarchiver to cache address

Chapter8_math instance, the Add method, which is declared as PUBILC in the parent class, is clearly overridden in the subclass.Of course, when we were wondering about "Case 3", we might have overlooked that "case 3" was created as a math instance, so from the compilation to the heap is a typical parent class, so this is not the overriding operation at all.Now, there is a key point to the static method problem of the static modification,The sub method, which is now declared static, is obviously

Use Python to implement machine awareness (python Machine Learning 1 ).0x01 Sensor A sensor is a linear classifier of the second-class Classification and belongs to a discriminant model (another is to generate a model ). Simply put, the objective is divided into two categories by using the input feature and the hyperplane. Sensor machines are the foundation of ne