• Realization of textcnn with keras


    The main references of this paper are as follows https://blog.csdn.net/asialee… Basic CNN def get_model(): K.clear_session() model = Sequential() model.add (Embedding(len(vocab) + 1, 300, input_ Length = 50)) # use the embedding layer to encode each word into a word vector model.add(Conv1D(256, 5, padding=’same’)) model.add(MaxPooling1D(3, 3, padding=’same’)) model.add(Conv1D(128, 5, padding=’same’)) model.add(MaxPooling1D(3, 3, padding=’same’)) model.add(Conv1D(64, 3, padding=’same’)) […]

  • C + + vector how to use C + + vector method introduction tutorial summary


    C + + is the inheritance of C language. It can carry out the procedural programming of C language, the object-based programming characterized by abstract data type, and the object-oriented programming characterized by inheritance and polymorphism. C + + is good at object-oriented programming, at the same time, it can also carry out process based […]

  • Data acquisition, processing and preparation on spark machine learning 03


    Data acquisition and processing on chap 03 spark Data acquisition, processing and preparation on spark Moviestream, which includes movie data provided by website, user’s service information data and behavior data. These data involve movies and related content (such as titles, categories, pictures, actors and directors), user information (such as user attributes, locations and other information), […]

  • Notes on data structure Deng Junhui of Tsinghua University Chapter 2


    Chapter 2 vector VectorIt is an abstraction and generalization of linear array. Starting from the most basic interface of vector, the corresponding vector template class is designed and implemented. a. Operation interface supported by vector ADT Operation interface function Applicable objects size() The current size of the report vector (total number of elements) vector get(r) […]

  • Six java examples of alink online learning


    I published a series of articles on how to use Python for alink online learning Some readers reported that they need java version. Although these two versions are the same in algorithm principle, there are still many differences in the process of using. In order to facilitate readers to quickly use java to start alink […]

  • Trust learning — using vectors to store value list


    Vectors allow us to store multiple values in a single data structure, which places all values adjacent to each other in memory. Vectors can only store values of the same type. They are useful when we have a list of items, such as lines of text in a file or prices for items in a […]

  • Machine learning (4): Wu Enda’s notes


    Preface to octave language In the large-scale machine learning in Silicon Valley, people usually use octave to realize the basic idea of the algorithm, and then rewrite it with other languages (Java, C + +). This is because machine learning languages such as python, numpy and R are more complicated than octave and MATLAB. Using […]

  • Trust learning — storing keys with associated values in a hash map


    Like vectors and strings, hash maps are a common collection.The HashMap < K, V > type stores the mapping of keys of type K to values of type V. It does this through a hash function that determines how to put the keys and values into memory. Create a hash map: //Create an empty hash […]

  • Interpretation of didi ETA paper: WDR model


    introduction paper:Learning to Estimate the Travel Time Impressions after reading: the overall standard, the combination of WD model and LSTM, to solve practical business problems. Note: ETA is the abbreviation of estimate travel time, that is, the estimated time of arrival. The problem is to estimate the time from point a to point B at […]

  • Interpretation of didi ETA paper: compacteta


    introduction paperCompactETA: A Fast Inference System for Travel Time Predictionreaction to a book or an articleThe core is to apply graph attention network to ETA. Core content problem For WDR, as long as the starting and ending points are different, the link sequence will be different, and the whole recurrent part will be recalculated. The […]

  • Keras text classification practice (2)


    Abstract:This article is an introduction to using kreas to process text analysis. It introduces two methods of text processing: hot coding and word embedding. In the previous sectionKeras text classification (Part one), about the basic knowledge of NLP. In this part, you’ll learn to represent words as vectors in different ways. What is word embedding […]

  • Basic mathematics linear algebra


    The content of linear algebra is very coherent, and the whole is [determinant — > matrix — > n-dimensional vector — > system of linear equations — > similar diagonal type — > quadratic type]. The determinant is a value. If the determinant is 0, the corresponding linear equations have multiple solutions, and the corresponding […]