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- The document summarizes key concepts from chapters 1.1 to 1.6 of the book "Pattern Recognition and Machine Learning" by Christopher M. Bishop. - It introduces polynomial curve fitting, Bayesian curve fitting, decision theory, and information theory concepts such as entropy, Kullback-Leibler divergence, and their applications in machine learning. - Key algorithms covered include linear and polynomial regression, maximum likelihood estimation, and using entropy and KL divergence to model probability distributions.

Signaler

Partager

Bayesian networks

Bayesian networks

Support Vector Machines

Support Vector Machines

Binary Class and Multi Class Strategies for Machine Learning

Binary Class and Multi Class Strategies for Machine Learning

Signaler

Partager

Bayesian networks

In this presentation is given an introduction to Bayesian networks and basic probability theory. Graphical explanation of Bayes' theorem, random variable, conditional and joint probability. Spam classifier, medical diagnosis, fault prediction. The main software for Bayesian Networks are presented.

Support Vector Machines

This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.

Binary Class and Multi Class Strategies for Machine Learning

This presentation discusses the following -
Possible strategies to follow when working on a new machine learning problem.
The common problems with classifiers (how to detect them and eliminate them).
Popular approaches on how to use binary classifiers to problems with multi class classification.

Regularization in deep learning

Presentation in Vietnam Japan AI Community in 2019-05-26.
The presentation summarizes what I've learned about Regularization in Deep Learning.
Disclaimer: The presentation is given in a community event, so it wasn't thoroughly reviewed or revised.

PRML Chapter 12

This chapter discusses continuous latent variable models including principal component analysis (PCA), probabilistic PCA, and factor analysis. PCA finds projections of data that maximize variance or minimize error through eigenvectors of the covariance matrix. Probabilistic PCA places a probabilistic treatment on PCA by modeling the data and latent variables as Gaussian distributions. Factor analysis similarly models the data as a linear combination of latent factors plus noise.

Artificial neural network

Neural networks can be biological models of the brain or artificial models created through software and hardware. The human brain consists of interconnected neurons that transmit signals through connections called synapses. Artificial neural networks aim to mimic this structure using simple processing units called nodes that are connected by weighted links. A feed-forward neural network passes information in one direction from input to output nodes through hidden layers. Backpropagation is a common supervised learning method that uses gradient descent to minimize error by calculating error terms and adjusting weights between layers in the network backwards from output to input. Neural networks have been applied successfully to problems like speech recognition, character recognition, and autonomous vehicle navigation.

Confusion Matrix

A confusion matrix is a tool used to evaluate the performance of a supervised machine learning model for classification problems. It allows visualization of correct and incorrect predictions compared to the actual classifications in a test dataset. The confusion matrix shows the true positives, false positives, true negatives, and false negatives. This helps determine the accuracy, precision, recall, F1 score and area under the curve (AUC) of the model, which are more comprehensive metrics for evaluation than accuracy alone.

Masked Autoencoders Are Scalable Vision Learners.pptx

He, Kaiming, et al. "Masked autoencoders are scalable vision learners." arXiv preprint arXiv:2111.06377 (2021).

Associative memory network

This presentation discusses about various techniques of Associative memory network under Neural Networks

Introduction to Linear Discriminant Analysis

This document provides an introduction and overview of linear discriminant analysis (LDA). It discusses that LDA is a dimensionality reduction technique used to separate classes of data. The document outlines the 5 main steps to performing LDA: 1) calculating class means, 2) computing scatter matrices, 3) finding linear discriminants using eigenvalues/eigenvectors, 4) determining the transformation subspace, and 5) projecting the data onto the subspace. Examples using the Iris dataset are provided to illustrate how LDA works step-by-step to find projection directions that separate the classes.

Gradient descent method

The document discusses gradient descent methods for unconstrained convex optimization problems. It introduces gradient descent as an iterative method to find the minimum of a differentiable function by taking steps proportional to the negative gradient. It describes the basic gradient descent update rule and discusses convergence conditions such as Lipschitz continuity, strong convexity, and condition number. It also covers techniques like exact line search, backtracking line search, coordinate descent, and steepest descent methods.

Pattern recognition and Machine Learning.

Machine learning involves using examples to generate a program or model that can classify new examples. It is useful for tasks like recognizing patterns, generating patterns, and predicting outcomes. Some common applications of machine learning include optical character recognition, biometrics, medical diagnosis, and information retrieval. The goal of machine learning is to build models that can recognize patterns in data and make predictions.

Independent Component Analysis

This document discusses independent component analysis (ICA) for blind source separation. ICA is a method to estimate original signals from observed signals consisting of mixed original signals and noise. It introduces the ICA model and approach, including whitening, maximizing non-Gaussianity using kurtosis and negentropy, and fast ICA algorithms. The document provides examples applying ICA to separate images and discusses approaches to improve ICA, including using differential filtering. ICA is an important technique for blind source separation and independent component estimation from observed signals.

Uncertainty in Deep Learning

In this presentation, we provide a quick intro do bayesian inference, Gaussian Processes and then later relate to the latest state of the art research on Bayesian Deep Learning, in order to include uncertainty in deep neural net predictions

Feature Extraction

The document describes two feature extraction methods: attention based and statistics based. The attention based method models how human vision finds salient regions using an architecture that decomposes images into channels and creates image pyramids, then combines the information to generate saliency maps. This method was applied to face recognition but had problems with pose and expression changes. The statistics based method aims to select a subset of important features using criteria based on how well the features represent the original data.

Explicit Density Models

This document summarizes recent advances in deep generative models with explicit density estimation. It discusses variational autoencoders (VAEs), including techniques to improve VAEs such as importance weighting, semi-amortized inference, and mitigating posterior collapse. It also covers energy-based models, autoregressive models, flow-based models, vector-quantized VAEs, hierarchical VAEs, and diffusion probabilistic models. The document provides an overview of these generative models with a focus on density estimation and generation quality.

Introduction to Machine Learning Classifiers

You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.

Lecture 7: Hidden Markov Models (HMMs)

This document is a lecture on hidden Markov models (HMMs) given by Marina Santini at Uppsala University. The lecture covers the basics of HMMs, including Markov assumptions, observation sequences, problems with HMMs, the Viterbi, forward, and backward algorithms, modeling for part-of-speech tagging, learning, smoothing, and inference in HMMs, as well as applications of HMMs. The lecture acknowledges Joakim Nivre for course design and materials.

Instance Based Learning in Machine Learning

Slides were formed by referring to the text Machine Learning by Tom M Mitchelle (Mc Graw Hill, Indian Edition) and by referring to Video tutorials on NPTEL

Uncertainty Quantification in AI

With the advent of Deep Learning (DL), the field of AI made a giant leap forward and it is nowadays applied in many industrial use-cases. Especially critical systems like autonomous driving, require that DL methods not only produce a prediction but also state the certainty about the prediction in order to assess risks and failure.
In my talk, I will give an introduction to different kinds of uncertainty, i.e. epistemic and aleatoric. To have a baseline for comparison, the classical method of Gaussian Processes for regression problems is presented. I then elaborate on different DL methods for uncertainty quantification like Quantile Regression, Monte-Carlo Dropout, and Deep Ensembles. The talk is concluded with a comparison of these techniques to Gaussian Processes and the current state of the art.

Bayesian networks

Bayesian networks

Support Vector Machines

Support Vector Machines

Binary Class and Multi Class Strategies for Machine Learning

Binary Class and Multi Class Strategies for Machine Learning

Regularization in deep learning

Regularization in deep learning

PRML Chapter 12

PRML Chapter 12

Artificial neural network

Artificial neural network

Confusion Matrix

Confusion Matrix

Masked Autoencoders Are Scalable Vision Learners.pptx

Masked Autoencoders Are Scalable Vision Learners.pptx

Associative memory network

Associative memory network

Introduction to Linear Discriminant Analysis

Introduction to Linear Discriminant Analysis

Gradient descent method

Gradient descent method

Pattern recognition and Machine Learning.

Pattern recognition and Machine Learning.

Independent Component Analysis

Independent Component Analysis

Uncertainty in Deep Learning

Uncertainty in Deep Learning

Feature Extraction

Feature Extraction

Explicit Density Models

Explicit Density Models

Introduction to Machine Learning Classifiers

Introduction to Machine Learning Classifiers

Lecture 7: Hidden Markov Models (HMMs)

Lecture 7: Hidden Markov Models (HMMs)

Instance Based Learning in Machine Learning

Instance Based Learning in Machine Learning

Uncertainty Quantification in AI

Uncertainty Quantification in AI

PRML Chapter 8

This document summarizes key concepts from Chapter 8 of the book "Pattern Recognition and Machine Learning" regarding probabilistic graphical models. It introduces directed and undirected graphical models as visualization tools for probabilistic relationships between random variables. It provides examples of Bayesian networks and conditional independence. Key points covered include using graphs to factorize joint probabilities, the d-separation criteria for identifying conditional independence based on a graph, and applying these concepts to linear Gaussian models and discrete variable models.

PRML Chapter 4

This chapter discusses classification methods including linear discriminant functions and probabilistic generative and discriminative models. It covers linear decision boundaries, perceptrons, Fisher's linear discriminant, logistic regression, and the use of sigmoid and softmax activation functions. The key points are:
1) Classification involves dividing the input space into decision regions using linear or nonlinear boundaries.
2) Perceptrons and Fisher's linear discriminant find linear decision boundaries by updating weights to minimize misclassification.
3) Generative models like naive Bayes estimate joint probabilities while discriminative models like logistic regression directly model posterior probabilities.
4) Sigmoid and softmax functions are used to transform linear outputs into probabilities for binary and multiclass classification respectively.

Topic 1 __basic_probability_concepts

This document provides an introduction to probability concepts including:
- Random experiments, sample spaces, events, and set operations used to define events.
- Interpretations and axioms of probability, and examples of assigning probabilities.
- Conditional probability defined as the probability of one event occurring given that another event has occurred, and the formula for calculating conditional probabilities.
- Independence of events defined as events whose joint probability equals the product of their individual probabilities, and examples.
- The law of total probability derived from partitioning events, used to calculate probabilities of complex events.

Fuzzy portfolio optimization_Yuxiang Ou

The document describes a fuzzy portfolio optimization model using trapezoidal possibility distributions to account for uncertainty in asset returns. The model formulates the portfolio selection problem as a mathematical optimization that maximizes expected return minus risk. Lagrange multipliers and Karush-Kuhn-Tucker conditions are used to derive the optimal solution. Real stock market data is used to provide a numerical example.

A Probabilistic Attack On NP-Complete Problems

This document discusses reformulating NP-complete problems in terms of continuous mathematics using probability theory. Specifically, it considers the 3-SAT NP-complete problem and introduces new probability variables to represent bit assignments. A cost function is constructed as a sum of clause satisfaction probabilities. Key properties of the cost function are that it is harmonic over subsets of variables and its Hessian has zero diagonal entries. The cost function is always positive inside the problem's domain and achieves its min/max on the boundary. The spectrum of cost function values on vertices corresponds to number of unsatisfied clauses. Overall, the approach reformulates 3-SAT in terms of a harmonic cost function to manipulate solutions without examining them individually.

Line of best fit lesson

This document provides an overview of line of best fit and linear regression. It defines key concepts like linear regression, residuals, and scatter plots. It explains how to determine the line of best fit for a data set by finding the line that minimizes the sum of the squared residuals. An example is worked through showing how to calculate the line of best fit and make a prediction based on that line. The document emphasizes that linear regression is useful for understanding relationships between variables in fields like business and medical research.

Frequentist inference only seems easy By John Mount

This is part of Alpine ML Talk Series:
The talk is called “Frequentist inference only seems easy” and is about the theory of simple statistical inference (based on material from this article http://www.win-vector.com/blog/2014/07/frequenstist-inference-only-seems-easy/ ). The talk includes some simple dice games (I bring dice!) that really break the rote methods commonly taught as statistics. This is actually a good thing, as it gives you time and permission to work out how common statistical methods are properly derived from basic principles. This takes a little math (which I develop in the talk), but it changes some statistics from "do this" to "here is why you calculate like this.” It should appeal to people interested in the statistical and machine learning parts of data science.

PRML Chapter 9

1. The document discusses mixture models and the Expectation-Maximization (EM) algorithm. It covers K-means clustering, Gaussian mixture models, and applying EM to estimate parameters for these models.
2. EM is a general technique for finding maximum likelihood solutions for probabilistic models with latent variables. It works by iteratively computing expectations of the latent variables given current parameter estimates (E-step) and maximizing the likelihood function with respect to the parameters (M-step).
3. This process is guaranteed to increase the likelihood at each iteration until convergence. EM can be applied to problems like Gaussian mixtures, Bernoulli mixtures, and Bayesian linear regression by treating certain variables as latent.

A PROBABILISTIC ALGORITHM OF COMPUTING THE POLYNOMIAL GREATEST COMMON DIVISOR...

In the earlier work, subresultant algorithm was proposed to decrease the coefficient growth in the Euclidean algorithm of polynomials. However, the output polynomial remainders may have a small factor which can be removed to satisfy our needs. Then later, an improved subresultant algorithm was given by representing the subresultant algorithm in another way, where we add a variant called 𝜏 to express the small factor. There was a way to compute the variant proposed by Brown, who worked at IBM. Nevertheless, the way failed to determine each𝜏 correctly.

Explore ml day 2

This document provides an overview of linear regression and logistic regression concepts. It begins with an introduction to linear regression, discussing finding the best fit line to training data. It then covers the loss function and gradient descent optimization algorithm used to minimize loss and fit the model parameters. Next, it discusses logistic regression for classification problems, covering the sigmoid function for hypothesis representation and interpreting probabilities. It concludes by discussing feature scaling techniques like normalization and standardization to prepare data for modeling.

Large Deviations: An Introduction

This document contains slides from a presentation introducing the theory and applications of large deviations. It begins with an example using coin tosses to illustrate basic large deviation principles. It then discusses how large deviations can be used to study distributions like the binomial distribution. Applications discussed include risk management, information theory, and hydrodynamic limits in physics. Transformation techniques like Cramér's theorem and the contraction principle are also mentioned for applying large deviations to transformed sequences.

Module-2_Notes-with-Example for data science

The document discusses several key concepts in probability and statistics:
- Conditional probability is the probability of one event occurring given that another event has already occurred.
- The binomial distribution models the probability of success in a fixed number of binary experiments. It applies when there are a fixed number of trials, two possible outcomes, and the same probability of success on each trial.
- The normal distribution is a continuous probability distribution that is symmetric and bell-shaped. It is characterized by its mean and standard deviation. Many real-world variables approximate a normal distribution.
- Other concepts discussed include range, interquartile range, variance, and standard deviation. The interquartile range describes the spread of a dataset's middle 50%

Zain 333343

This document introduces probability and discusses different approaches to defining it. It notes that probability is used to describe variability and uncertainty when outcomes are not certain. Three common definitions of probability are discussed - classical, relative frequency, and subjective - along with their limitations. The document advocates treating probability as a mathematical system defined by axioms rather than worrying about numerical values until a specific application. It then outlines how to construct probability models using sample spaces and assigning probabilities to events based on their composition of simple events.

Intro to Quant Trading Strategies (Lecture 10 of 10)

This document provides an overview of risk management strategies for algorithmic trading. It discusses various risk measurement techniques including Value at Risk (VaR), Extreme Value Theory (EVT), and the generalized extreme value distribution. Specific risks for different asset classes like bonds, stocks, derivatives and currencies are outlined. Monte Carlo simulation is presented as a technique for modeling rare events and fat tails in return distributions. The document emphasizes that risk is multifaceted and not fully captured by any single measure.

PRML Chapter 10

This chapter discusses approximate inference methods for probabilistic models where exact inference is intractable. It introduces variational inference as a deterministic approximation approach. Variational inference works by restricting the distribution of latent variables to a simpler family that makes computation and optimization easier. The chapter provides examples of using variational inference for Gaussian mixtures and univariate Gaussian models. It explains how to derive a variational lower bound and optimize it using an iterative procedure similar to EM.

Montecarlophd

This document discusses Monte Carlo simulations in Stata to verify statistical properties like the weak law of large numbers and the central limit theorem. It introduces generating random numbers, simulations to prove the weak law using coin tosses, and simulations to demonstrate the central limit theorem using samples from a uniform distribution. The simulations allow investigating the theoretical properties of estimators by defining the data generating process.

chap4_Parametric_Methods.ppt

This document provides an overview of parametric methods in machine learning, including maximum likelihood estimation, evaluating estimators using bias and variance, the Bayes estimator, and parametric classification and regression. Key points covered include:
- Maximum likelihood estimation chooses parameters that maximize the likelihood function to produce the most probable distribution given observed data.
- Bias and variance are used to evaluate estimators, with the goal of minimizing both to improve accuracy. High bias or variance can indicate underfitting or overfitting.
- The Bayes estimator treats unknown parameters as random variables and uses prior distributions and Bayes' rule to estimate their expected values given data.

DIGITAL TEXT BOOK

This document is the preface to a textbook on number theory. It discusses the goals of the textbook, which are to encourage independent thinking and problem solving rather than rote memorization. Number theory is well-suited for this purpose as patterns in the natural numbers can be discerned through observation and experimentation, but proving theorems requires rigorous demonstration. The textbook was originally written for a course at Brown University designed to attract non-science majors to mathematics. The prerequisites are few, requiring only high school algebra and a willingness to experiment, make mistakes, learn from them, and persevere.

Advanced Econometrics L5-6.pptx

This document provides an overview and schedule for an advanced econometrics training using Stata. The training covers topics such as hypothesis testing, multiple regression, time series models, panel data models, and difference-in-differences. It discusses assumptions of classical linear regression models and how to perform statistical inference using estimates of variance, standard error, and hypothesis testing. The document explains how to construct t-statistics and compare them to critical values from the t-distribution to test hypotheses about population parameters.

Class9_PCA_final.ppt

PCA is a technique to reduce the dimensionality of multivariate data while retaining essential information. It works by transforming the data to a new coordinate system such that the greatest variance by any projection of the data lies on the first coordinate, called the first principal component. Subsequent components account for remaining variance while being orthogonal to previous components. PCA is performed by computing the eigenvalues and eigenvectors of the covariance matrix of the data, with the principal components being the eigenvectors. This allows visualization and interpretation of high-dimensional data in lower dimensions.

PRML Chapter 8

PRML Chapter 8

PRML Chapter 4

PRML Chapter 4

Topic 1 __basic_probability_concepts

Topic 1 __basic_probability_concepts

Fuzzy portfolio optimization_Yuxiang Ou

Fuzzy portfolio optimization_Yuxiang Ou

A Probabilistic Attack On NP-Complete Problems

A Probabilistic Attack On NP-Complete Problems

Line of best fit lesson

Line of best fit lesson

Frequentist inference only seems easy By John Mount

Frequentist inference only seems easy By John Mount

PRML Chapter 9

PRML Chapter 9

A PROBABILISTIC ALGORITHM OF COMPUTING THE POLYNOMIAL GREATEST COMMON DIVISOR...

A PROBABILISTIC ALGORITHM OF COMPUTING THE POLYNOMIAL GREATEST COMMON DIVISOR...

Explore ml day 2

Explore ml day 2

Large Deviations: An Introduction

Large Deviations: An Introduction

Module-2_Notes-with-Example for data science

Module-2_Notes-with-Example for data science

Zain 333343

Zain 333343

Intro to Quant Trading Strategies (Lecture 10 of 10)

Intro to Quant Trading Strategies (Lecture 10 of 10)

PRML Chapter 10

PRML Chapter 10

Montecarlophd

Montecarlophd

chap4_Parametric_Methods.ppt

chap4_Parametric_Methods.ppt

DIGITAL TEXT BOOK

DIGITAL TEXT BOOK

Advanced Econometrics L5-6.pptx

Advanced Econometrics L5-6.pptx

Class9_PCA_final.ppt

Class9_PCA_final.ppt

Welcome back to Instagram. Sign in to check out what your

Welcome back to Instagram. Sign in to check out what your

transgenders community data in india by govt

data about transgenders community

Seamlessly Pay Online, Pay In Stores or Send Money

Seamlessly Pay Online, Pay In Stores or Send Money

Fine-Tuning of Small/Medium LLMs for Business QA on Structured Data

Enabling business users to directly query their data sources is a significant advantage for organisations. The majority of enterprise data is housed within databases, requiring extensive procedures that involve intermediary layers for reporting and its related customization. The concept of enabling natural language queries, where a chatbot can interpret user questions into database queries and promptly return results, holds promise for expediting decision-making and enhancing business responsiveness. This approach empowers experienced users to swiftly obtain data-driven insights. The integration of Text-to-SQL and Large Language Model (LLM) capabilities represents a solution to this challenge, offering businesses a powerful tool for query automation. However, security concerns prevent organizations from granting direct database access akin to platforms like OpenAI. To address this limitation, this Paper proposes developing fine-tuned small/medium LLMs tailored to specific domains like retail and supply chain. These models would be trained on domain-specific questions and Queries that answer these questions based on the database table structures to ensure efficacy and security. A pilot study is undertaken to bridge this gap by fine-tuning selected LLMs to handle business-related queries and associated database structures, focusing on sales and supply chain domains. The research endeavours to experiment with zero-shot and fine-tuning techniques to identify the optimal model. Notably, a new dataset is curated for fine-tuning, comprising business-specific questions pertinent to the sales and supply chain sectors. This experimental framework aims to evaluate the readiness of LLMs to meet the demands for business query automation within these specific domains. The study contributes to the progression of natural language query processing and database interaction within the realm of business intelligence applications.

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the unexpected potential of Dijkstra's Algorithm

This explanation is related to the impact and solutions that will be provided by the algorithm

ch8_multiplexing cs553 st07 slide share ss

ch8_multiplexing

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Nipissing University degree offer Nipissing diploma Transcript

【添加微信：176555708】【诚招代理】办理毕业证 成绩单 文凭 学位证offer学生卡雅思托福：
办英国,美国,德国,法国,澳洲,加拿大,意大利,新西兰,西班牙等海外各高校文凭
高度还原各种防伪工艺：
（钢印,底纹,水印,防伪光标,热敏防伪,烫金烫银,LOGO烫金烫银复合重叠,文字图案浮雕,激光镭射,紫外荧光,温感,复印防伪等原版工艺）。
如果您是以下情况，我们都能竭诚为您解决实际问题：
1、在校期间，因各种原因未能顺利毕业，拿不到官方毕业证；
2、面对父母的压力，希望尽快拿到；
3、不清楚流程以及材料该如何准备；
4、回国时间很长，毕业证损坏丢失；
5、回国马上就要找工作，办给用人单位看
一比一原版【微信：176555708】办理毕业证 成绩单 文凭 学位证offer（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：176555708】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza

The OpenMetadata Community Meeting was held on July 10th, 2024. In the Community Spotlight, Vinol Joy Dsouza from Aspire (https://aspireapp.com/) spoke about their journey with OpenMetadata: What problems they were trying to solve, how they chose OpenMetadata, and how they integrated it to manage more than 6.000 Data Quality tests!

Universidad de Valladolid degree offer diploma Transcript

学历认证补办制【微信：A575476】【(UVA毕业证）巴利亚多利德大学毕业证成绩单offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张，特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：A575476】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理(UVA毕业证）巴利亚多利德大学毕业证【微信：A575476】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(UVA毕业证）巴利亚多利德大学毕业证【微信：A575476】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(UVA毕业证）巴利亚多利德大学毕业证【微信：A575476】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(UVA毕业证）巴利亚多利德大学毕业证【微信：A575476 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

Willis Tower //Sears Tower- Supertall Building .pdf

Sears Tower was the last supertall building constructed during the
International architecture period, and SOM's interpretation of the style is
remarkably bold and awe-inspiring.

Artificial Intelligence (AI) Technology Project Proposal _ by Slidesgo.pptx

Artificial Intelligence (AI) Technology Project Proposal _ by Slidesgo.pptx
Artificial Intelligence (AI) Technology Project Proposal _ by Slidesgo.pptx
Artificial Intelligence (AI) Technology Project Proposal _ by Slidesgo.pptx
Artificial Intelligence (AI) Technology Project Proposal _ by Slidesgo.pptx
Artificial Intelligence (AI) Technology Project Proposal _ by Slidesgo.pptx
Artificial Intelligence (AI) Technology Project Proposal _ by Slidesgo.pptx
Artificial Intelligence (AI) Technology Project Proposal _ by Slidesgo.pptx

MUMBAI MONTHLY RAINFALL CAPSTONE PROJECT

### Data Description and Analysis Summary for Presentation
#### 1. **Importing Libraries**
Libraries used:
- `pandas`, `numpy`: Data manipulation
- `matplotlib`, `seaborn`: Data visualization
- `scikit-learn`: Machine learning utilities
- `statsmodels`, `pmdarima`: Statistical modeling
- `keras`: Deep learning models
#### 2. **Loading and Exploring the Dataset**
**Dataset Overview:**
- **Source:** CSV file (`mumbai-monthly-rains.csv`)
- **Columns:**
- `Year`: The year of the recorded data.
- `Jan` to `Dec`: Monthly rainfall data.
- `Total`: Total annual rainfall.
**Initial Data Checks:**
- Displayed first few rows.
- Summary statistics (mean, standard deviation, min, max).
- Checked for missing values.
- Verified data types.
**Visualizations:**
- **Annual Rainfall Time Series:** Trends in annual rainfall over the years.
- **Monthly Rainfall Over Years:** Patterns and variations in monthly rainfall.
- **Yearly Total Rainfall Distribution:** Distribution and frequency of annual rainfall.
- **Box Plots for Monthly Data:** Spread and outliers in monthly rainfall.
- **Correlation Matrix of Monthly Rainfall:** Relationships between different months' rainfall.
#### 3. **Data Transformation**
**Steps:**
- Ensured 'Year' column is of integer type.
- Created a datetime index.
- Converted monthly data to a time series format.
- Created lag features to capture past values.
- Generated rolling statistics (mean, standard deviation) for different window sizes.
- Added seasonal indicators (dummy variables for months).
- Dropped rows with NaN values.
**Result:**
- Transformed dataset with additional features ready for time series analysis.
#### 4. **Data Splitting**
**Procedure:**
- Split the data into features (`X`) and target (`y`).
- Further split into training (80%) and testing (20%) sets without shuffling to preserve time series order.
**Result:**
- Training set: `(X_train, y_train)`
- Testing set: `(X_test, y_test)`
#### 5. **Automated Hyperparameter Tuning**
**Tool Used:** `pmdarima`
- Automatically selected the best parameters for the SARIMA model.
- Evaluated using metrics such as AIC and BIC.
**Output:**
- Best SARIMA model parameters and statistical summary.
#### 6. **SARIMA Model**
**Steps:**
- Fit the SARIMA model using the training data.
- Evaluated on both training and testing sets using MAE and RMSE.
**Output:**
- **Train MAE:** Indicates accuracy on training data.
- **Test MAE:** Indicates accuracy on unseen data.
- **Train RMSE:** Measures average error magnitude on training data.
- **Test RMSE:** Measures average error magnitude on testing data.
#### 7. **LSTM Model**
**Preparation:**
- Reshaped data for LSTM input.
- Converted data to `float32`.
**Model Building and Training:**
- Built an LSTM model with one LSTM layer and one Dense layer.
- Trained the model on the training data.
**Evaluation:**
- Evaluated on both training and testing sets using MAE and RMSE.
**Output:**
- **Train MAE:** Accuracy on training data.
- **T

DU degree offer diploma Transcript

学历定制【微信号:95270640】《(DU毕业证书)迪肯大学毕业证》【微信号:95270640】《毕业证、成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【关于学历材料质量】
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

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Harendra Singh, AI Strategy and Consulting Portfolio

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Welcome back to Instagram. Sign in to check out what your

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Nipissing University degree offer Nipissing diploma Transcript

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OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza

OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza

Universidad de Valladolid degree offer diploma Transcript

Universidad de Valladolid degree offer diploma Transcript

Willis Tower //Sears Tower- Supertall Building .pdf

Willis Tower //Sears Tower- Supertall Building .pdf

Introduction to the Red Hat Portfolio.pdf

Introduction to the Red Hat Portfolio.pdf

Artificial Intelligence (AI) Technology Project Proposal _ by Slidesgo.pptx

Artificial Intelligence (AI) Technology Project Proposal _ by Slidesgo.pptx

MUMBAI MONTHLY RAINFALL CAPSTONE PROJECT

MUMBAI MONTHLY RAINFALL CAPSTONE PROJECT

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Harendra Singh, AI Strategy and Consulting Portfolio

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- 1. Introduction + Chapter 1 Reviewer : Sunwoo Kim Christopher M. Bishop Pattern Recognition and Machine Learning Yonsei University Department of Applied Statistics 1
- 2. Study Introduction Study Objective - Assuming that we are familiar of mathematical statistics 1&2 + regression analysis + basic bayes, - Getting the intuition of the various algorithms. - Understanding mathematical concepts of the algorithms. - Reviewing the algorithms on the statistical perspective. Time - Fixed time TBD Method - A week before, we choose the scope of the sessions. - I will prepare the summary of the scope. - Every participants should study the scope and prepare some related questions! 2
- 3. Notation 𝑦 𝑥, 𝑤 : Estimated value with parameter w (which is y) 𝑡𝑛 : True value (which is 𝑦) 𝒕 ∶ Set of input and output vectors 𝐸 𝑤 = 𝐿(𝑦 𝑥, 𝑤 , 𝑡) : Error function, which measures the misfit between estimated value and the true value. 𝒘 = 𝒘𝑻𝒘 = 𝑤1 2 + 𝑤2 2 + ⋯ + 𝑤𝑛 2 = Euclidean norm (also called l2-norm) 𝝁, 𝚺, |𝚺| : Mean, covariance and determinant of variables. 𝛽 = Σ−1 (𝑓𝑜𝑟 𝑢𝑛𝑖𝑣𝑎𝑟𝑖𝑎𝑡𝑒 = 1 𝜎2) : Precision parameter (inverse of covariance) 𝜇𝑀𝐿 : Estimated mean by maximum likelihood estimation. 3
- 4. Chapter 1.1. Polynomial Curve Fitting We have already covered most of the sections in chapter 1 in our undergraduate classes. Thus, I would like to cover only the concepts which are unfamiliar to us. 4 Most of our regression model focuses on simple linear regression, that is 𝛽 = 𝑋𝑇 𝑋 −1 𝑋𝑇 𝑌 Above estimation could be achieved via normal equation. However, how can we set example like this? Here we construct model by using polynomial variables! We can still apply squared error!
- 5. Chapter 1.2.6 Bayesian Curve Fitting As we all know, we need to assume the distribution of parameters. Furthermore, we have to marginalize it out in order to make prediction! This process can be expressed by 5 This entire process will be covered in detail in chapter 3!
- 6. Chapter 1.5. Decision Theory Our Goal : Getting the 𝑝(𝑥, 𝑡), but in most case it is extremely hard. In fact, we estimate posterior, 𝑝 𝑡 𝑥 = 𝑝 𝐶𝑘 𝑥 = 𝑝 𝑥 𝐶𝑘 𝒑 𝑪𝒌 𝑝(𝑥) 6 For cancer diagnosis, we have some belief, A prior knowledge before taking X-ray. Consider we are trying to build a decision rule. For binary classification, we are dividing input space to 𝓡𝟏 & 𝓡𝟐. What we do in ML is “minimizing the misclassification rate”. Here, let’s consider the decision boundary to be 𝑥. Optimal boundary will be 𝑥 = 𝑥0
- 7. Chapter 1.5. Decision Theory We need a generalization of the concept “loss”. Here we define the “loss function”. 𝐿𝑘𝑗 : Element of a loss matrix. We are minimizing the average loss of the function. 7 In practical, estimating the mere probability is not enough. We need to assign a specific label! / That is, we need to decide a cut-off This threshold matter is called “reject option”. =
- 8. Chapter 1.5. Decision Theory Way of classification 8 (A) Generative Model (B) Discriminative Model (C) Direct classification - Estimating above probabilities. - We are modeling the distribution of the input & output. - It is possible to generate synthetic data. - Estimating the posterior only. - We calculate the probability of our interest. 𝒑 𝑪𝒌 𝒙) 𝑪𝒌 = 𝒇(𝒙) - We do not calculate the probability. - Directly yields the class label.
- 9. Chapter 1.6. Information Theory We are interested in ‘how much information is received when we observe specific event?’ This is something connected to the idea of uncertainty! Let ℎ(. ) be a function of information gain by observing specific event. If two events 𝑥 𝑎𝑛𝑑 𝑦 are independent, ℎ 𝑥, 𝑦 = ℎ 𝑥 + ℎ(𝑦) satisfies. However, unrelated events’ probabilities satisfy… 𝑝 𝑥, 𝑦 = 𝑝 𝑥 𝑝(𝑦) It is intuitive to use ℎ 𝑥 = − log2 𝑝(𝑥) What is an average achievement of information? It can be written as 9 Check how the entropy values change as the probability changes
- 10. Chapter 1.6. Information Theory Ideation. Consider the random variable which may have 8 possible states. 1st Case : All same probabilities 2nd Case : Probability of ( 1 2 , 1 4 , 1 8 , 1 16 , 1 64 , 1 64 , 1 64 , 1 64 ) See how the information gain is changing. At the same time, we can define entropy as ‘average amount of information needed to specify the state of a random variable.’ Now, consider multinomial distribution. 10 This can be interpreted as multi-version of 𝑛 𝑘 . Similarly, we are assigning each value to the different boxes.
- 11. Chapter 1.6. Information Theory Let’s take a deeper look at this equation. We are interested in how much information we need to achieve certain state. Thus, again we apply it in entropy with scale value N. By applying Stirling’s approximation… Then, when does this entropy is being maximized? We can optimize this by solving Here, maximized value is 𝒑 𝒙𝒊 = 𝟏 𝑴 11
- 12. Chapter 1.6. Information Theory Let’s extend this idea to the continuous variables. By using mean value theorem. ** From wiki, mean value theorem means for the closed interval [a, b], and function 𝑓(𝑥) is continuous in that interval. Then, for the value c that exists between a and b, following equation satisfies. (Something like 구분구적법) 𝑎 𝑏 𝑓 𝑥 𝑑𝑥 ≅ 𝑓 𝑐 ∗ (𝑏 − 𝑎) 12 We may simply extend it to Obviously, the interval Δ should be as small as possible to increase the accuracy of approximation, we consider Δ → 0 We can express continuous variable’s entropy in the discrete form.
- 13. Chapter 1.6. Information Theory Let’s again maximize this equation by using lagrangian multiplier. 13 Constraint of basic probability distribution. We are maximizing… We can set above equation by zero, and get And again, by doing some math (solving lagrangian issues, then we get) Oh… This is amazing… A probability distribution which gives maximum entropy is a gaussian distribution!!
- 14. Chapter 1.6. Information Theory Kullback-Leibler divergence(KL Divergence) 14 We all have heard of KL divergence for many times. But, what does it exactly indicates?? Let’s think of variable 𝑥 of probability distribution 𝑝(𝑥). We are trying to model this by using 𝑞(𝑥). It is the average additional amount of information required to specify the value of x as a result of using 𝑞(𝑥) instead of true distribution 𝑝(𝑥). In short, it indicates ‘How much information do we need more?’ Original entropy New estimated entropy We have covered the KL-divergence’s inequality in Mathematical Statistics I, by using Jensen inequality. Note that,
- 15. Chapter 1.6. Information Theory KL divergence in ML 15 = 𝐸𝑥[ln 𝑞 𝑥 𝑝(𝑥) ] By using the fact of Now, let’s think of data x which has an unknown distribution of 𝑝(𝑥). We are trying to model the distribution of 𝑝(𝑥) by using 𝑞(𝑥𝑛|𝜃). If distribution of 𝑞(𝑥𝑛|𝜃) is similar to 𝑝(𝑥), then its KL divergence is relatively small. Here, ln 𝑝(𝑥𝑛) does not depend on 𝜃, which is already fixed. Thus, we don’t need second term. Thus, we only need 𝑛=1 𝑁 {− ln 𝑞(𝑋𝑛|𝜃)}, which is related to the negative loge likelihood!