Algorithm算法代写代考

Lab 02 Word2Vec In [ ]: import pprint import re # For parsing our XML data from lxml import etree # For data processing import nltk nltk.download(‘punkt’) from nltk.tokenize import word_tokenize, sent_tokenize # For implementing the word2vec family of algorithms from gensim.models import Word2Vec import warnings warnings.simplefilter(action=’ignore’, category=FutureWarning) [nltk_data] Downloading package punkt to /root/nltk_data… [nltk_data] Unzipping tokenizers/punkt.zip.

Linear Classifiers 27 Linear Classifiers • Linear classifiers: A choice of H h𝑥;𝜃,𝜃0 =𝑠𝑖𝑔𝑛𝜃𝑇𝑥+𝜃0 =ቊ+1if𝜃𝑇𝑥+𝜃0>0 28 −1 otherwise 𝑥2 𝑥1 The random linear classifier algorithm hyperparameter random_linear_classifier(D, k): for j=1 to k 𝜃(𝑗) = random(R𝑑); 𝜃(𝑗) = random(R) j*=argminE𝑛(𝜃𝑗 ,𝜃(𝑗)) 𝑗∈{1..𝑘} 0 return(𝜃(𝑗∗), 𝜃(𝑗∗)) 0 0 29

Linear Algebra primer 31 Matrices & matrix elements 32 Vectors 33 Matrix addition 34 Scalar multiplication 35 Combination of operands 𝑥=2× 1 + 0 − 6 /3 353 36 Matrix-vector multiplication 25 10 43 1 3 = ➔To get 𝑦𝑖, multiply 𝐴’s 𝑖-th row with the elements of the vector 𝑥, and then add them

Supervised Learning 20 Data • Dataset: 𝒟𝑛={𝑥1,𝑦1 ,…,𝑥𝑛,𝑦𝑛 } 𝑥(𝑖) ∈ R𝑑 𝑦(𝑖) ∈ {+1, −1} • • 𝜑(𝑥): feature representation ∈ R𝑑 21 Hypotheses • A hypothesis: 𝑦 = h 𝑥;𝜃 • h ∈ H (hypothesis class) 22 Loss function • 𝐿 𝑔, 𝑎 𝑔 ∈ {+1, −1} 𝑎 ∈ {+1, −1} • How

Introduction Introduction to machine learning 10 Machine learning? • Learning from data • Large datasets, from the growth of the internet, medical records, cameras & images are ubiquitous, … • Applications we can’t program by hand • Handwriting recognition, NLP, Computer Vision, … • «Self-learning» algorithms • e.g. product or movie recommendations, spam filtering (with