Dialogue systems (conversational robots) essentially let the machine understand the human language through techniques such as machine learning and artificial intelligence. It contains a combination of many subject methods and is a concentrated training camp for artificial intelligence. Figure 1 shows the main techniques involved in the development of a dialog system. What are the relevant technologies of the dialogue system given in Figure 1, from which channels can you understand? The explanation is given step by step below. Matrix computing mainly studies some properties of a single matrix or multiple matrices. Various models of machine learning involve a lot of matrix-related properties. For example, PCA is actually calculating feature vectors, and MF is actually calculating singular value vectors in analog SVD. Many tools in the field of artificial intelligence are programmed in a matrix language, such as mainstream deep learning frameworks such as Tensorflow and PyTorch. There are a lot of textbooks for matrix calculations. Find the difficulty that suits you. If you want to understand more deeply, the book "Linear Algebra Done Right" is highly recommended. Probability statistics is the basis of machine learning. Several commonly used concepts of probability and statistics: random variables, discrete random variables, continuous random variables, probability density/distribution (binomial distribution, polynomial distribution, Gaussian distribution, index family distribution), conditional probability density/distribution, prior density / distribution, posterior density / distribution, maximum likelihood estimation, maximum posterior estimation. For a simple understanding, you can go through classic machine learning materials, such as the first two chapters of Pattern RecogniTIon and Machine Learning, the first two chapters of Machine Learning: A ProbabilisTIc PerspecTIve. If you are studying systematically, you can find the textbooks in the probability statistics of the university. Optimization methods are widely used in the training of machine learning models. Several optimization concepts commonly found in machine learning: convex/nonconvex functions, gradient descent, stochastic gradient descent, and original dual problems. General machine learning materials or courses will teach you a bit of optimization, such as the Convex OpTImization Overview by Zico Kolter in the Andrew Ng Machine Learning course. Of course, the best way to understand the system is to look at Boyd's "Convex Optimization" book, and the corresponding PPT (https://web.stanford.edu/~boyd/cvxbook/) and course (https://see. Stanford.edu/Course/EE364A, https://see.stanford.edu/Course/EE364B). Students who like to read the code can also look at the optimization methods involved in the open source machine learning project, such as Liblinear, LibSVM, Tensorflow is a good choice. Some commonly used math Python packages: NumPy: scientific calculation package for tensor calculation SciPy: Mathematical Computing Toolkit for Science and Engineering Matplotlib: drawing, visualization package Andrew Ng's "Machine Learning" course is still an introductory artifact in the field of machine learning. Don't underestimate the so-called introduction, you can understand the knowledge inside, you can apply for the position of algorithm engineer. Recommend several well-recognized textbooks: Hastie et al., The Elements of Statistical Learning, Bishop's Pattern Recognition and Machine Learning, Murphy's Machine Learning: A Probabilistic Perspective, and Zhou Zhihua's Watermelon Book Machine Learning . Deep learning materials recommend Yoshua Bengio's "Deep Learning" and the official tutorial of Tensorflow. Some commonly used tools: Scikit-learn: Python package containing various machine learning models Liblinear: A variety of efficient training methods including linear models LibSVM: A variety of efficient training methods including various SVMs Tensorflow: Google's deep learning framework PyTorch: Facebook's deep learning framework Keras: High-level deep learning use framework Caffe: Old-fashioned deep learning framework Many universities have NLP-related research teams, such as the Stanford NLP group, and the domestic Harbin Institute of Technology SCIR laboratory. The dynamics of these teams are worthy of attention. NLP-related information is available online. The course recommends Stanford's "CS224n: Natural Language Processing with Deep Learning". The book recommends Manning's "Foundations of Statistical Natural Language Processing" (Chinese version is called "Statistical Natural Language Processing Fundamentals"). For information retrieval, Manning's classic book "Introduction to Information Retrieval" (Chinese version of "Introduction to Information Retrieval" translated by Wang Bin) and the Stanford course "CS 276: Information Retrieval and Web Search" are recommended. Some commonly used tools: Jieba: Chinese word segmentation and part-of-speech tagging Python package CoreNLP: Stanford's NLP Tools (Java) NLTK: Natural Language Toolkit TextGrocery: Efficient short text categorization tool (Note: only for Python 2) LTP: Harbin Institute of Technology's Chinese natural language processing tool Gensim: a text analysis tool that contains a variety of topic models Word2vec: Efficient word representation learning tool GloVe: Stanford's word representation learning tool Fasttext : Efficient word representation learning and sentence classification library FuzzyWuzzy: A tool for calculating the similarity between texts CRF++: Lightweight Conditional Accessory Library (C++) Elasticsearch: Open Source Search Engine The dialogue system uses different frameworks technically for different types of users. Here are a few different types of dialogue robots.
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