机器学习代写代考 machine learning

机器学习作为CS必修课程之一, 一般包括supervised learning监督学习, unsupervised learning 无监督学习和reinforcement learning(RL)增强学习3个大方向.

supervised learning通常需要学习linear regression, decision tree, SVM(support vector machine), logistic regression, naive bayes, random forest,
Neural Networks神经网络等内容.

深度学习deep learning是现在非常热门的方向, 需要学习CNN (convolution neural network), RNN (recurrent neural network)等网络架构. 知识点包括dropout, backpropagation, pooling, convolutional layer等. 深度学习现在已经广泛应用到计算机视觉 (computer vision) 和 NLP (自然语言处理).

unsupervised learning通常要学习principal component analysis (PCA), factor analysis, clustering algorithm such as K-means, EM (Expectation–maximization algorithm), GMM (gaussian mixture model)等.

reinforcement learning一般会学习Q-learning和Deep Q-learning.

R语言机器学习代写: Exercise 2 Section A. Model Complexity and Model Selection

机器学习代写代考 machine learning /

Exercise 2 Section A. Model Complexity and Model Selection In this section, you study the effect of model complexity on the training and testing error. You also demonstrate your programming skills by developing a regression algorithm and a cross-validation technique that will be used to select the models with the most effective complexity. Background A […]

R语言机器学习代写: Exercise 2 Section A. Model Complexity and Model Selection Read More »

python自然语言处理代写 Lab 7: Named entity recognition with the structured perceptron

机器学习代写代考 machine learning, 自然语言处理 NLP natural language processing /

 lab7_ner.md Lab 7: Named entity recognition with the structured perceptron Andreas Vlachos The goal of this lab session is learn a named entity recognizer (NER) using the structured perceptron. The named entity recognizer will need to predict for each word one of the following labels: O: not a named entity PER: part of a person’s

python自然语言处理代写 Lab 7: Named entity recognition with the structured perceptron Read More »

机器学习神经网络代写: feedforward Neural Network (FNN)

机器学习代写代考 machine learning /

The purpose of this homework assignment is to apply the feedforward Neural Network (FNN) to solve a practical classification problem. Overview In this homework project, you will need to apply the feedforward neural network (FNN) on the MNIST dataset for hand written digit recognition.   Dataset   The MNIST database of handwritten digits has a

机器学习神经网络代写: feedforward Neural Network (FNN) Read More »

深度学习代写: COMP9444 Neural Networks and Deep Learning Recurrent Networks and Sentiment Classification

机器学习代写代考 machine learning

Introduction COMP9444 Neural Networks and Deep Learning Session 2, 2017 Project 2 – Recurrent Networks and Sentiment Classification Due Dates: Stage 1: Sunday 8 October, 23:59 pm Stage 2: Sunday 15 October, 23:59 pm Marks: 15% of final assessment You should now have a good understanding of the internal dynamics of TensorFlow and how to

深度学习代写: COMP9444 Neural Networks and Deep Learning Recurrent Networks and Sentiment Classification Read More »

R语言机器学习代写: Latent Variables and Neural Networks

机器学习代写代考 machine learning /

Assessment 2 Latent Variables and Neural Networks Objectives This assignment consists of three parts (A,B,C), which cover latent variables models and neural networks (Modules 4 and 5). The total marks of this assessment is 100. Part A. Document Clustering In this part, you solve a document clustering problem using unsupervised learning algorithms (i.e., soft and

R语言机器学习代写: Latent Variables and Neural Networks Read More »

MPI并行计算代写: STAT/BIOSTAT 534 Statistical Computing Final Project

并行计算, 机器学习代写代考 machine learning /

1 Rules STAT/BIOSTAT 534 Statistical Computing Final Project Adrian Dobra adobra@uw.edu This is your final examination for this course. The work you will submit needs to reflect your own understanding of the material covered in class. It is fine to talk with each other about the project. However, when you actually do the work, you

MPI并行计算代写: STAT/BIOSTAT 534 Statistical Computing Final Project Read More »

机器学习代写: K-means Clustering and Principal Component Analysis

机器学习代写代考 machine learning

Programming Exercise 7: K-means Clustering and Principal Component Analysis Machine Learning Introduction In this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images. Before starting on the programming exercise, we strongly

机器学习代写: K-means Clustering and Principal Component Analysis Read More »

机器学习并行计算代写：Stats 141C High Performance Statistical Computing

并行计算, 机器学习代写代考 machine learning

Stats 141C High Performance Statistical Computing Spring 2018 Lecturer: Cho-Jui Hsieh Keywords: Multicore Programming Date Due: May 22, 10:20am, 2018 Homework 2 For this homework, we will use the data and code downloaded from http://www.stat.ucdavis.edu/~chohsieh/ teaching/STA141C_Spring2018/hw2_code.zip. In this folder, we provide the code for the nearest neighbor clas- sification algorithm in “go knn.py” (you can

机器学习并行计算代写：Stats 141C High Performance Statistical Computing Read More »