A short presentation on preprocessing data and feature scaling. It highlights the rationale behind scaling your continuous variables and the different methods for doing so.
This document contains instructions for a take-home final exam for an IE 535 computer simulation course. It includes 5 questions - the first asks students to analyze a single queueing system using given arrival and service times, the second through fourth ask students to solve problems from a textbook on simulation modeling, and the fifth asks students to perform a literature survey on a simulation modeling topic and summarize their findings. It also provides a format for students to follow when reporting on their network modeling problems.
This document discusses data preprocessing techniques. It describes how real-world data can be incomplete, noisy, or inconsistent. The main tasks in data preprocessing are cleaning the data by handling missing values and outliers, integrating and transforming the data by normalization and aggregation, reducing the data through dimensionality reduction, and discretizing numeric values. Specific techniques discussed include binning, clustering, regression, principle component analysis, and information-based discretization. The goal is to prepare raw data for further analysis.
Data Preprocessing- Data Warehouse & Data MiningTrinity Dwarka
Data Preprocessing- Data Warehouse & Data Mining
Data Quality
Reasons for inaccurate data
Reasons for incomplete data
Major Tasks in Data Preprocessing
Forms of Data Preprocessing
Data Cleaning
Incomplete (Missing) Data
The document introduces data preprocessing techniques for data mining. It discusses why data preprocessing is important due to real-world data often being dirty, incomplete, noisy, inconsistent or duplicate. It then describes common data types and quality issues like missing values, noise, outliers and duplicates. The major tasks of data preprocessing are outlined as data cleaning, integration, transformation and reduction. Specific techniques for handling missing values, noise, outliers and duplicates are also summarized.
Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
This document provides an overview of deep learning concepts including neural networks, supervised and unsupervised learning, and key terms. It explains that deep learning uses neural networks with many hidden layers to learn features directly from raw data. Supervised learning algorithms learn from labeled examples to perform classification or regression on unseen data. Unsupervised learning finds patterns in unlabeled data. Key terms defined include neurons, activation functions, loss functions, optimizers, epochs, batches, and hyperparameters.
This document contains instructions for a take-home final exam for an IE 535 computer simulation course. It includes 5 questions - the first asks students to analyze a single queueing system using given arrival and service times, the second through fourth ask students to solve problems from a textbook on simulation modeling, and the fifth asks students to perform a literature survey on a simulation modeling topic and summarize their findings. It also provides a format for students to follow when reporting on their network modeling problems.
This document discusses data preprocessing techniques. It describes how real-world data can be incomplete, noisy, or inconsistent. The main tasks in data preprocessing are cleaning the data by handling missing values and outliers, integrating and transforming the data by normalization and aggregation, reducing the data through dimensionality reduction, and discretizing numeric values. Specific techniques discussed include binning, clustering, regression, principle component analysis, and information-based discretization. The goal is to prepare raw data for further analysis.
Data Preprocessing- Data Warehouse & Data MiningTrinity Dwarka
Data Preprocessing- Data Warehouse & Data Mining
Data Quality
Reasons for inaccurate data
Reasons for incomplete data
Major Tasks in Data Preprocessing
Forms of Data Preprocessing
Data Cleaning
Incomplete (Missing) Data
The document introduces data preprocessing techniques for data mining. It discusses why data preprocessing is important due to real-world data often being dirty, incomplete, noisy, inconsistent or duplicate. It then describes common data types and quality issues like missing values, noise, outliers and duplicates. The major tasks of data preprocessing are outlined as data cleaning, integration, transformation and reduction. Specific techniques for handling missing values, noise, outliers and duplicates are also summarized.
Data preprocessing involves transforming raw data into an understandable and consistent format. It includes data cleaning, integration, transformation, and reduction. Data cleaning aims to fill missing values, smooth noise, and resolve inconsistencies. Data integration combines data from multiple sources. Data transformation handles tasks like normalization and aggregation to prepare the data for mining. Data reduction techniques obtain a reduced representation of data that maintains analytical results but reduces volume, such as through aggregation, dimensionality reduction, discretization, and sampling.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
This document provides an overview of deep learning concepts including neural networks, supervised and unsupervised learning, and key terms. It explains that deep learning uses neural networks with many hidden layers to learn features directly from raw data. Supervised learning algorithms learn from labeled examples to perform classification or regression on unseen data. Unsupervised learning finds patterns in unlabeled data. Key terms defined include neurons, activation functions, loss functions, optimizers, epochs, batches, and hyperparameters.
Copy of CRICKET MATCH WIN PREDICTOR USING LOGISTIC ...PATHALAMRAJESH
This project uses logistic regression to build a cricket match win predictor. It analyzes match and ball-by-ball data to extract important features, performs exploratory data analysis to derive additional predictive features, and fits a logistic regression model to predict the winning probability of teams based on the game situation. The model achieves an accuracy of 86% on the test data. Future work includes predicting the winner based only on the first innings and adding a user interface to allow custom predictions.
This document discusses training deep neural network (DNN) models. It explains that DNNs have an input layer, multiple hidden layers, and an output layer connected by weights and biases. Training a DNN involves initializing the weights and biases randomly, passing inputs through the network to get outputs, calculating the loss between actual and predicted outputs, and updating the weights to minimize loss using gradient descent and backpropagation. Gradient descent with backpropagation calculates the gradient of the loss with respect to each weight and bias by applying the chain rule to propagate loss backwards through the network.
Feature Engineering - Getting most out of data for predictive models - TDC 2017Gabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
- Adaline neural networks are single-layer neural networks that use an adaptive linear neuron as the processing unit. They can be trained using the Delta rule or least mean square rule to minimize errors.
- The architecture of an Adaline network consists of an input layer, weighted sum function, activation function, bias node, error function, and gradient descent training algorithm.
- Adaline networks were applied to problems like adaptive filtering, system modeling, statistical prediction, noise cancellation, and pattern recognition.
- A Python implementation of Adaline for breast cancer classification is presented, including methods for model training, prediction, and evaluating accuracy.
This document discusses developing classifiers for a census income dataset using the WEKA data mining tool. It summarizes preprocessing steps like handling missing values, removing outliers, and balancing class distributions. It then evaluates various classifiers like J48 decision trees and k-nearest neighbors (IBk) on the preprocessed data. The best performing model was an ensemble "vote" classifier that combined the predictions of J48, IBk, and logistic regression models, achieving 87.3% accuracy and an ROC area of 0.947.
The document discusses machine learning concepts and the machine learning pipeline using the scikit-learn library in Python. It covers different machine learning techniques like decision trees, logistic regression, and neural networks. It also outlines the standard machine learning steps of gathering and preprocessing data, selecting a model, training and evaluating models, and tuning hyperparameters.
Bei diesem Hackathon sollen Ideen mit Artifical-Intelligence-Bezug in 24 Stunden umgesetzt werden. Bildet Teams von 2-5 Personen und baut rund um die Uhr an eurem Prototypen. Die fertigen Projekte werden dann am Schluss vor einer Jury vorgestellt und kämpfen um das Preisgeld im Wert von 10.000 €.
https://devpost.com/software/laserchallenge
The document discusses various machine learning algorithms and techniques used for plant disease detection, with a focus on mango plants. It describes image acquisition, pre-processing techniques like cropping and resizing, segmentation methods including thresholding and edge detection, feature extraction using GLCM, and classification algorithms such as neural networks, KNN, K-means clustering, and support vector machines. SVM is highlighted as an effective approach for identifying plant diseases from images by learning from labeled training data.
This document discusses data preprocessing techniques for machine learning. It covers common preprocessing steps like normalization, encoding categorical features, and handling outliers. Normalization techniques like StandardScaler, MinMaxScaler and RobustScaler are described. Label encoding and one-hot encoding are covered for processing categorical variables. The document also discusses polynomial features, custom transformations, and preprocessing text and image data. The goal of preprocessing is to prepare data so it can be better consumed by machine learning algorithms.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Deep learning uses neural networks with multiple layers to simulate the human brain. It consists of an input layer, hidden layers, and an output layer with data passed between each layer. The example task uses a neural network for multi-class classification of handwritten digits, with an output softmax activation and minimization of error through gradient descent backpropagation. After experimentation, the model achieved 98% validation accuracy. Deep learning is widely used for computer vision, natural language processing, and reinforcement learning problems.
The document discusses the key steps involved in data pre-processing for machine learning:
1. Data cleaning involves removing noise from data by handling missing values, smoothing outliers, and resolving inconsistencies.
2. Data transformation strategies include data aggregation, feature scaling, normalization, and feature selection to prepare the data for analysis.
3. Data reduction techniques like dimensionality reduction and sampling are used to reduce large datasets size by removing redundant features or clustering data while maintaining most of the information.
Advanced regression and model selection - For Upgrad. This discussed various techniques of model selection and general methodology to select a machine learning algorithm
Feature Engineering - Getting most out of data for predictive modelsGabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Deep learning uses multilayered neural networks to process information in a robust, generalizable, and scalable way. It has various applications including image recognition, sentiment analysis, machine translation, and more. Deep learning concepts include computational graphs, artificial neural networks, and optimization techniques like gradient descent. Prominent deep learning architectures include convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks.
TensorFlow is a software library for machine learning and deep learning. It uses tensors as multi-dimensional data arrays to represent mathematical expressions in neural networks. TensorFlow is popular due to its extensive documentation, machine learning libraries, and ability to train deep neural networks for tasks like image recognition. Tensors have a rank defining their dimensionality, a shape defining their rows and columns, and a data type. Common tensor operations include addition, subtraction, multiplication, and transposition.
This presentation gives an overview of client-side machine learning techniques with the focus on TensorFlow Lite. Various tools and implementations of TFLite are explained. It also gives an overview of the current client-side machine learning project carried out at Mercari Inc, Japan.
how,when and why to perform Feature scaling?
Different type of feature scaling Technique.
when to perform feature scaling?
why to perform feature scaling?
MinMax feature scaling techniques.
Unit vector scaling.
The document discusses various techniques for optimizing query performance in MySQL, including using indexes appropriately, avoiding full table scans, and tools like EXPLAIN, Performance Schema, and pt-query-digest for analyzing queries and identifying optimization opportunities. It provides recommendations for index usage, covering indexes, sorting and joins, and analyzing slow queries.
Copy of CRICKET MATCH WIN PREDICTOR USING LOGISTIC ...PATHALAMRAJESH
This project uses logistic regression to build a cricket match win predictor. It analyzes match and ball-by-ball data to extract important features, performs exploratory data analysis to derive additional predictive features, and fits a logistic regression model to predict the winning probability of teams based on the game situation. The model achieves an accuracy of 86% on the test data. Future work includes predicting the winner based only on the first innings and adding a user interface to allow custom predictions.
This document discusses training deep neural network (DNN) models. It explains that DNNs have an input layer, multiple hidden layers, and an output layer connected by weights and biases. Training a DNN involves initializing the weights and biases randomly, passing inputs through the network to get outputs, calculating the loss between actual and predicted outputs, and updating the weights to minimize loss using gradient descent and backpropagation. Gradient descent with backpropagation calculates the gradient of the loss with respect to each weight and bias by applying the chain rule to propagate loss backwards through the network.
Feature Engineering - Getting most out of data for predictive models - TDC 2017Gabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
- Adaline neural networks are single-layer neural networks that use an adaptive linear neuron as the processing unit. They can be trained using the Delta rule or least mean square rule to minimize errors.
- The architecture of an Adaline network consists of an input layer, weighted sum function, activation function, bias node, error function, and gradient descent training algorithm.
- Adaline networks were applied to problems like adaptive filtering, system modeling, statistical prediction, noise cancellation, and pattern recognition.
- A Python implementation of Adaline for breast cancer classification is presented, including methods for model training, prediction, and evaluating accuracy.
This document discusses developing classifiers for a census income dataset using the WEKA data mining tool. It summarizes preprocessing steps like handling missing values, removing outliers, and balancing class distributions. It then evaluates various classifiers like J48 decision trees and k-nearest neighbors (IBk) on the preprocessed data. The best performing model was an ensemble "vote" classifier that combined the predictions of J48, IBk, and logistic regression models, achieving 87.3% accuracy and an ROC area of 0.947.
The document discusses machine learning concepts and the machine learning pipeline using the scikit-learn library in Python. It covers different machine learning techniques like decision trees, logistic regression, and neural networks. It also outlines the standard machine learning steps of gathering and preprocessing data, selecting a model, training and evaluating models, and tuning hyperparameters.
Bei diesem Hackathon sollen Ideen mit Artifical-Intelligence-Bezug in 24 Stunden umgesetzt werden. Bildet Teams von 2-5 Personen und baut rund um die Uhr an eurem Prototypen. Die fertigen Projekte werden dann am Schluss vor einer Jury vorgestellt und kämpfen um das Preisgeld im Wert von 10.000 €.
https://devpost.com/software/laserchallenge
The document discusses various machine learning algorithms and techniques used for plant disease detection, with a focus on mango plants. It describes image acquisition, pre-processing techniques like cropping and resizing, segmentation methods including thresholding and edge detection, feature extraction using GLCM, and classification algorithms such as neural networks, KNN, K-means clustering, and support vector machines. SVM is highlighted as an effective approach for identifying plant diseases from images by learning from labeled training data.
This document discusses data preprocessing techniques for machine learning. It covers common preprocessing steps like normalization, encoding categorical features, and handling outliers. Normalization techniques like StandardScaler, MinMaxScaler and RobustScaler are described. Label encoding and one-hot encoding are covered for processing categorical variables. The document also discusses polynomial features, custom transformations, and preprocessing text and image data. The goal of preprocessing is to prepare data so it can be better consumed by machine learning algorithms.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Deep learning uses neural networks with multiple layers to simulate the human brain. It consists of an input layer, hidden layers, and an output layer with data passed between each layer. The example task uses a neural network for multi-class classification of handwritten digits, with an output softmax activation and minimization of error through gradient descent backpropagation. After experimentation, the model achieved 98% validation accuracy. Deep learning is widely used for computer vision, natural language processing, and reinforcement learning problems.
The document discusses the key steps involved in data pre-processing for machine learning:
1. Data cleaning involves removing noise from data by handling missing values, smoothing outliers, and resolving inconsistencies.
2. Data transformation strategies include data aggregation, feature scaling, normalization, and feature selection to prepare the data for analysis.
3. Data reduction techniques like dimensionality reduction and sampling are used to reduce large datasets size by removing redundant features or clustering data while maintaining most of the information.
Advanced regression and model selection - For Upgrad. This discussed various techniques of model selection and general methodology to select a machine learning algorithm
Feature Engineering - Getting most out of data for predictive modelsGabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Deep learning uses multilayered neural networks to process information in a robust, generalizable, and scalable way. It has various applications including image recognition, sentiment analysis, machine translation, and more. Deep learning concepts include computational graphs, artificial neural networks, and optimization techniques like gradient descent. Prominent deep learning architectures include convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks.
TensorFlow is a software library for machine learning and deep learning. It uses tensors as multi-dimensional data arrays to represent mathematical expressions in neural networks. TensorFlow is popular due to its extensive documentation, machine learning libraries, and ability to train deep neural networks for tasks like image recognition. Tensors have a rank defining their dimensionality, a shape defining their rows and columns, and a data type. Common tensor operations include addition, subtraction, multiplication, and transposition.
This presentation gives an overview of client-side machine learning techniques with the focus on TensorFlow Lite. Various tools and implementations of TFLite are explained. It also gives an overview of the current client-side machine learning project carried out at Mercari Inc, Japan.
how,when and why to perform Feature scaling?
Different type of feature scaling Technique.
when to perform feature scaling?
why to perform feature scaling?
MinMax feature scaling techniques.
Unit vector scaling.
The document discusses various techniques for optimizing query performance in MySQL, including using indexes appropriately, avoiding full table scans, and tools like EXPLAIN, Performance Schema, and pt-query-digest for analyzing queries and identifying optimization opportunities. It provides recommendations for index usage, covering indexes, sorting and joins, and analyzing slow queries.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
2. What is feature scaling?
Feature scaling is a method used to
standardize the range of independent
variables or features of data. In data
processing, it is also known as data
normalization and is generally performed
during the data preprocessing step.
3. ● Many estimators require it
○ Regression
○ SVM
○ kNN
○ Neural networks
● Image processing
Why use feature scaling?
4. ● Methods
○ Calculate z-scores of your feature variables
○ Min-max method
How to use feature scaling?
5. ● Andrew Ng
○ Okay if variables in the [-⅓ , ⅓ ] to [-3 , 3]
range
○ Otherwise, scale
When to use feature scaling?
6. ● preprocessing.scale
○ by default scales feature to mean 0 and
variance 1
○ i.e calculates the Z-score
● preprocessing.normalize
○ scales each feature to unit vector
○ useful for text classification/clustering
sklearn.preprocessing
9. Fit scaler only on training set
std_scale = preprocessing.StandardScaler().fit(X_train)
X_train_std = std_scale.transform(X_train)
X_test_std = std_scale.transform(X_test)
also MinMaxScaler()