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.

lisp代写 机器学习 K-NEAREST-NEIGHBOR 480 Assignment 3

机器学习代写代考 machine learning /

;;;;; ASSIGNMENT 3 ;;;;; K-NEAREST-NEIGHBOR ;;;;; This is a very short assignment and its intent is to get your feet wet in Lisp. ;;;;; Your task is to write three functions which will enable you to test K-Nearest Neighbor: ;;;;; ;;;;; 1. DIFFERENCE. This function tells you the difference (error) between two lists. ;;;;; You […]

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python tensorflow 深度学习代写 COMP9444 Neural Networks and Deep Learning Project 2 – Recurrent Networks and Sentiment Classification

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COMP9444 Neural Networks and Deep Learning Session 2, 2018 Project 2 – Recurrent Networks and Sentiment Classification Due: Sunday 23 September, 23:59 pm Marks: 15% of final assessment Introduction You should now have a good understanding of the internal dynamics of TensorFlow and how to implement, train and test various network architectures. In this assignment

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python 机器学习代写 COMP20008 Project Phase 2

机器学习代写代考 machine learning /

COMP20008 – 2018 – SM2 – Project Phase 2 Release Date: 11:59am Monday, 3rd September 2018 Due Date: 11:59am Friday, 21st September 2018 Submission is via the LMS Please, make sure you get a submission confirmation email once you submit your assignment. Otherwise, it will be considered as a late submission. Phase 2: Python Data

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python R语言 代写 机器学习代写 随机森林 random forest

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1. Use a programming language or package where random forests can be trained and applied. Examples include Python (scikit-learn package), R and Matlab. Using the training and test sets specified in the syllabus, perform the following tasks: a)  On the madelon dataset, for each of k ∈ {3, 10, 30, 100, 300} train a random

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python 机器学习 程序代写 QBUS6850 Assignment 2

机器学习代写代考 machine learning /

QBUS6850 Assignment 2: Notes to Students The assignment MUST be submitted electronically to Turnitin through QBUS6850 Canvas site. Please do NOT submit a zipped file. The assignment is due at 17:00pm on Monday, 15 October 2018. The late penalty for the assignment is 10% of the assigned mark per day, starting after 17:00pm on the

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机器学习 random forests python R代写 Homework 2

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Homework 2, due September 12th, 11:59pm August 30, 2018 1. Use a programming language or package where random forests can be trained and applied. Examples include Python (scikit-learn package), R and Matlab. Using the training and test sets specified in the syllabus, perform the following tasks: a)  On the madelon dataset, for each of k

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