Search results for curse of dimensionality

Found 52 documents, 5925 searched:

• Must-Know: What is the curse of dimensionality?

  What is the curse of dimensionality? This post gives a no-nonsense overview of the concept, plain and simple.

  https://www.kdnuggets.com/2017/04/must-know-curse-dimensionality.html

• Deep Learning, The Curse of Dimensionality, and Autoencoders

  Autoencoders are an extremely exciting new approach to unsupervised learning and for many machine learning tasks they have already surpassed the decades of progress made by researchers handpicking features.

  https://www.kdnuggets.com/2015/03/deep-learning-curse-dimensionality-autoencoders.html

• Dimensionality Reduction Techniques in Data Science

  Dimensionality reduction techniques are basically a part of the data pre-processing step, performed before training the model.

  https://www.kdnuggets.com/2022/09/dimensionality-reduction-techniques-data-science.html

• Data Compression via Dimensionality Reduction: 3 Main Methods

  Lift the curse of dimensionality by mastering the application of three important techniques that will help you reduce the dimensionality of your data, even if it is not linearly separable.

  https://www.kdnuggets.com/2020/12/data-compression-dimensionality-reduction.html

• Dimensionality Reduction with Principal Component Analysis (PCA)

  This article focuses on design principles of the PCA algorithm for dimensionality reduction and its implementation in Python from scratch.

  https://www.kdnuggets.com/2020/05/dimensionality-reduction-principal-component-analysis.html

• The Practical Importance of Feature Selection">The Practical Importance of Feature Selection

  Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing generalizability.

  https://www.kdnuggets.com/2017/06/practical-importance-feature-selection.html

• 17 More Must-Know Data Science Interview Questions and Answers, Part 2

  The second part of 17 new must-know Data Science Interview questions and answers covers overfitting, ensemble methods, feature selection, ground truth in unsupervised learning, the curse of dimensionality, and parallel algorithms.

  https://www.kdnuggets.com/2017/02/17-data-science-interview-questions-answers-part-2.html

• The Only Interview Prep Course You Need for Deep Learning

  Dive into the 50 most popular deep-learning questions to get you ready for your interview.

  https://www.kdnuggets.com/the-only-interview-prep-course-you-need-for-deep-learning

• 7 Steps to Mastering Exploratory Data Analysis

  A Step-by-Step Approach to Unearthing Trends, Outliers, and Insights in your Data.

  https://www.kdnuggets.com/7-steps-to-mastering-exploratory-data-analysis

• A Guide to Data Science Project Management Methodologies

  Project management can be one of the biggest challenges in data science projects. Learn how you can ensure your project management methods are down-packed and effective.

  https://www.kdnuggets.com/2023/07/guide-data-science-project-management-methodologies.html

• From Theory to Practice: Building a k-Nearest Neighbors Classifier

  The k-Nearest Neighbors Classifier is a machine learning algorithm that assigns a new data point to the most common class among its k closest neighbors. In this tutorial, you will learn the basic steps of building and applying this classifier in Python.

  https://www.kdnuggets.com/2023/06/theory-practice-building-knearest-neighbors-classifier.html

• RAPIDS cuDF to Speed up Your Next Data Science Workflow

  This article will explain how RAPIDS can help you speed up your next data science workflow. RAPIDS cuDF is a GPU DataFrame library that allows you to produce your end-to-end data science pipeline development all on GPU.

  https://www.kdnuggets.com/2023/04/rapids-cudf-speed-next-data-science-workflow.html

• Everything You’ve Ever Wanted to Know About Machine Learning

  Putting the fun in fundamentals! A collection of short videos to amuse beginners and experts alike.

  https://www.kdnuggets.com/2022/09/everything-youve-ever-wanted-to-know-about-machine-learning.html

• The Difference Between Training and Testing Data in Machine Learning

  When building a predictive model, the quality of the results depends on the data you use. In order to do so, you need to understand the difference between training and testing data in machine learning.

  https://www.kdnuggets.com/2022/08/difference-training-testing-data-machine-learning.html

• Essential Math for Data Science: Eigenvectors and Application to PCA

  In this article, you’ll learn about the eigendecomposition of a matrix.

  https://www.kdnuggets.com/2022/06/essential-math-data-science-eigenvectors-application-pca.html

• Popular Machine Learning Algorithms

  This guide will help aspiring data scientists and machine learning engineers gain better knowledge and experience. I will list different types of machine learning algorithms, which can be used with both Python and R.

  https://www.kdnuggets.com/2022/05/popular-machine-learning-algorithms.html

• Hands-on Reinforcement Learning Course Part 3: SARSA

  This is part 3 of my hands-on course on reinforcement learning, which takes you from zero to HERO . Today we will learn about SARSA, a powerful RL algorithm.

  https://www.kdnuggets.com/2022/01/handson-reinforcement-learning-course-part-3-sarsa.html

• 10 Simple Things to Try Before Neural Networks

  Below are 10 simple things you should remember to try first before throwing in the towel and jumping straight to neural networks.

  https://www.kdnuggets.com/2021/12/10-simple-things-try-neural-networks.html

• How causal inference lifts augmented analytics beyond flatland

  In our quest to better understand and predict business outcomes, traditional predictive modeling tends to fall flat. However, causal inference techniques along with business analytics approaches can unravel what truly changes your KPIs.

  https://www.kdnuggets.com/2021/08/causal-inference-augmented-analytics-beyond-flatland.html

• Geometric foundations of Deep Learning">Geometric foundations of Deep Learning

  Geometric Deep Learning is an attempt for geometric unification of a broad class of machine learning problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.

  https://www.kdnuggets.com/2021/07/geometric-foundations-deep-learning.html

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 4

  With the right software, hardware, and techniques at your fingertips, your capability to effectively develop high-performing models now hinges on leveraging automation to expedite the experimental process and building with the most efficient model architectures for your data.

  https://www.kdnuggets.com/2021/07/high-performance-deep-learning-part4.html

• Feature Selection – All You Ever Wanted To Know

  Although your data set may contain a lot of information about many different features, selecting only the "best" of these to be considered by a machine learning model can mean the difference between a model that performs well--with better performance, higher accuracy, and more computational efficiency--and one that falls flat. The process of feature selection guides you toward working with only the data that may be the most meaningful, and to accomplish this, a variety of feature selection types, methodologies, and techniques exist for you to explore.

  https://www.kdnuggets.com/2021/06/feature-selection-overview.html

• Must Know for Data Scientists and Data Analysts: Causal Design Patterns">Must Know for Data Scientists and Data Analysts: Causal Design Patterns

  Industry is a prime setting for observational causal inference, but many companies are blind to causal measurement beyond A/B tests. This formula-free primer illustrates analysis design patterns for measuring causal effects from observational data.

  https://www.kdnuggets.com/2021/03/causal-design-patterns.html

• Popular Machine Learning Interview Questions">Popular Machine Learning Interview Questions

  Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions.

  https://www.kdnuggets.com/2021/01/popular-machine-learning-interview-questions.html

• Monte Carlo integration in Python">Monte Carlo integration in Python

  A famous Casino-inspired trick for data science, statistics, and all of science. How to do it in Python?

  https://www.kdnuggets.com/2020/12/monte-carlo-integration-python.html

• How to Deal with Missing Values in Your Dataset

  In this article, we are going to talk about how to identify and treat the missing values in the data step by step.

  https://www.kdnuggets.com/2020/06/missing-values-dataset.html

• Diffusion Map for Manifold Learning, Theory and Implementation

  This article aims to introduce one of the manifold learning techniques called Diffusion Map. This technique enables us to understand the underlying geometric structure of high dimensional data as well as to reduce the dimensions, if required, by neatly capturing the non-linear relationships between the original dimensions.

  https://www.kdnuggets.com/2020/03/diffusion-map-manifold-learning-theory-implementation.html

• Exoplanet Hunting Using Machine Learning

  Search for exoplanets — those planets beyond our own solar system — using machine learning, and implement these searches in Python.

  https://www.kdnuggets.com/2020/01/exoplanet-hunting-machine-learning.html

• Neural Networks 201: All About Autoencoders

  Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problems, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation.

  https://www.kdnuggets.com/2019/11/all-about-autoencoders.html

• Understanding NLP and Topic Modeling Part 1

  In this post, we seek to understand why topic modeling is important and how it helps us as data scientists.

  https://www.kdnuggets.com/2019/11/understanding-nlp-topic-modeling-part-1.html

• Introduction to Artificial Neural Networks

  In this article, we’ll try to cover everything related to Artificial Neural Networks or ANN.

  https://www.kdnuggets.com/2019/10/introduction-artificial-neural-networks.html

• Explore the world of Bioinformatics with Machine Learning">Explore the world of Bioinformatics with Machine Learning

  The article contains a brief introduction of Bioinformatics and how a machine learning classification algorithm can be used to classify the type of cancer in each patient by their gene expressions.

  https://www.kdnuggets.com/2019/09/explore-world-bioinformatics-machine-learning.html

• Classifying Heart Disease Using K-Nearest Neighbors

  I have written this post for the developers and assumes no background in statistics or mathematics. The focus is mainly on how the k-NN algorithm works and how to use it for predictive modeling problems.

  https://www.kdnuggets.com/2019/07/classifying-heart-disease-using-k-nearest-neighbors.html

• Beyond Siri, Google Assistant, and Alexa – what you need to know about AI Conversational Applications

  We discuss industry trends in Artificial Intelligence with Vijay Ramakrishnan, a machine learning engineer and expert in conversational applications.

  https://www.kdnuggets.com/2019/04/ai-conversational-applications.html

• What Is Dimension Reduction In Data Science?

  An extensive introduction into Dimension Reduction, including a look at some of the different techniques, linear discriminant analysis, principal component analysis, kernel principal component analysis, and more.

  https://www.kdnuggets.com/2019/01/dimension-reduction-data-science.html

• Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies

  The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.

  https://www.kdnuggets.com/2018/12/explainable-ai-model-interpretation-strategies.html

• Adversarial Examples, Explained

  Deep neural networks—the kind of machine learning models that have recently led to dramatic performance improvements in a wide range of applications—are vulnerable to tiny perturbations of their inputs. We investigate how to deal with these vulnerabilities.

  https://www.kdnuggets.com/2018/10/adversarial-examples-explained.html

• Deep Learning for NLP: An Overview of Recent Trends">Deep Learning for NLP: An Overview of Recent Trends

  A new paper discusses some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks and some of the current best practices for applying deep learning in NLP.

  https://www.kdnuggets.com/2018/09/deep-learning-nlp-overview-recent-trends.html

• How GOAT Taught a Machine to Love Sneakers

  Embeddings are a fantastic tool to create reusable value with inherent properties similar to how humans interpret objects. GOAT uses deep learning to generate these for their entire sneaker catalogue.

  https://www.kdnuggets.com/2018/08/goat-taught-machine-love-sneakers.html

• fast.ai Machine Learning Course Notes

  This posts is a collection of a set of fantastic notes on the fast.ai machine learning MOOC freely available online, as written and shared by a student. These notes are a valuable learning resource either as a supplement to the courseware or on their own.

  https://www.kdnuggets.com/2018/07/suenaga-fast-ai-machine-learning-notes.html

• The Book of Why

  Judea Pearl has made noteworthy contributions to artificial intelligence, Bayesian networks, and causal analysis. These achievements notwithstanding, Pearl holds some views many statisticians may find odd or exaggerated.

  https://www.kdnuggets.com/2018/06/gray-pearl-book-of-why.html

• Ten Machine Learning Algorithms You Should Know to Become a Data Scientist">Ten Machine Learning Algorithms You Should Know to Become a Data Scientist

  It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.

  https://www.kdnuggets.com/2018/04/10-machine-learning-algorithms-data-scientist.html

• Understanding Feature Engineering: Deep Learning Methods for Text Data

  Newer, advanced strategies for taming unstructured, textual data: In this article, we will be looking at more advanced feature engineering strategies which often leverage deep learning models.

  https://www.kdnuggets.com/2018/03/understanding-feature-engineering-deep-learning-methods-text-data.html

• New-Age Machine Learning Algorithms in Retail Lending">New-Age Machine Learning Algorithms in Retail Lending

  We review the application of new age Machine Learning algorithms for better Customer Analytics in Lending and Credit Risk Assessment.

  https://www.kdnuggets.com/2017/09/machine-learning-algorithms-lending.html

• The Truth About Bayesian Priors and Overfitting

  Many of the considerations we will run through will be directly applicable to your everyday life of applying Bayesian methods to your specific domain.

  https://www.kdnuggets.com/2017/07/truth-about-bayesian-priors-overfitting.html

• Tree Kernels: Quantifying Similarity Among Tree-Structured Data

  An in-depth, informative overview of tree kernels, both theoretical and practical. Includes a use case and some code after the discussion.

  https://www.kdnuggets.com/2016/02/tree-kernels-quantifying-similarity-tree-structured-data.html

• Beyond One-Hot: an exploration of categorical variables

  Coding categorical variables into numbers, by assign an integer to each category ordinal coding of the machine learning algorithms. Here, we explore different ways of converting a categorical variable and their effects on the dimensionality of data.

  https://www.kdnuggets.com/2015/12/beyond-one-hot-exploration-categorical-variables.html

• Why Deep Learning Works – Key Insights and Saddle Points

  A quality discussion on the theoretical motivations for deep learning, including distributed representation, deep architecture, and the easily escapable saddle point.

  https://www.kdnuggets.com/2015/11/theoretical-deep-learning.html

• Data Science 101: Preventing Overfitting in Neural Networks

  Overfitting is a major problem for Predictive Analytics and especially for Neural Networks. Here is an overview of key methods to avoid overfitting, including regularization (L2 and L1), Max norm constraints and Dropout.

  https://www.kdnuggets.com/2015/04/preventing-overfitting-neural-networks.html

• Deep Learning for Text Understanding from Scratch

  Forget about the meaning of words, forget about grammar, forget about syntax, forget even the very concept of a word. Now let the machine learn everything by itself.

  https://www.kdnuggets.com/2015/03/deep-learning-text-understanding-from-scratch.html

• 9 Reasons why your machine learning project will fail

  This article explains in detail some of the issues that you may face during your machine learning project.

  https://www.kdnuggets.com/2018/07/why-machine-learning-project-fail.html

• Getting Started with Machine Learning in One Hour!

  Here is a machine learning getting started guide which grew out of the author's notes for a one hour talk on the subject. Hopefully you find the path helpful.

  https://www.kdnuggets.com/2017/11/getting-started-machine-learning-one-hour.html