Kunal Kumar Sahookunalkumarsahooai.hashnode.dev·Mar 9, 2024t-SNE: A Comprehensive GuideIntroduction In the ever-expanding domain of machine learning and data analysis, uncovering the the underlying structure of high-dimensional datasets is a daunting task. t-distributed Stochastic Neighbor Embedding (t-SNE) is a powerful dimensionality...Discuss·67 readst-sne
K Ahameddatailm.hashnode.dev·Jan 13, 2024Unraveling the Unseen: Revealing Hidden Patterns in Unlabeled DataUnsupervised learning is a branch of machine learning that explores patterns and structures within data without the presence of labeled outputs. Unlike supervised learning, where the algorithm is provided with labeled training data to learn and make ...DiscussMachine LearningUnsupervised learning
K Ahameddatailm.hashnode.dev·Jan 9, 2024Unveiling the Power of Dimensionality ReductionIn the vast landscape of data science and machine learning, the abundance of features within datasets presents both an opportunity and a challenge. While having a rich set of features allows models to capture intricate patterns, it also introduces th...DiscussMachine LearningDimensionality Reduction
Juan Carlos Olamendyjuancolamendy.hashnode.dev·Dec 6, 2023Unraveling the Mysteries of Kernel PCA: A Leap Beyond Conventional PCAIntroduction Have you ever wondered how to distill complex, high-dimensional data into something more manageable and insightful? This is where the magic of Principal Component Analysis (PCA) comes into play. But we're not stopping at traditional PCA;...DiscussMachine Learning
Juan Carlos Olamendyjuancolamendy.hashnode.dev·Nov 14, 2023Dimensionality Reduction in Machine LearningHave you ever faced the daunting challenge of understanding data with countless features? Welcome to the world of 'Dimensionality Reduction', a crucial concept in Machine Learning that transforms complexity into simplicity. Let's go together on a jou...DiscussMachine Learning
Juan Carlos Olamendyjuancolamendy.hashnode.dev·Oct 27, 2023Deep Dive into Embeddings in Machine LearningEver crossed paths with the concept of embeddings in Machine Learning? Wondered how it opens up new horizons in natural language processing, image recognition, and recommender systems? Deep learning, over the years, has undoubtedly transformed the la...DiscussDeep Learning
Juan Carlos Olamendyjuancolamendy.hashnode.dev·Oct 19, 2023Deep TDA. A new dimensionality reduction algorithmIntroduction 🚀 Dive into the world of Topological Data Analysis (TDA) paired with Self-supervised ML! TDA, with its knack for understanding data's shape and structure, meets the power of models learning from unlabeled data. 🧠 The result? Deep TDA! ...Discuss·30 readsMachine Learning
Saurabh Naiksaurabhz.hashnode.dev·Sep 30, 2023From Overwhelm to Optimization: Taming High-Dimensional DataIntroduction: In the vast realm of machine learning and data analysis, one fundamental challenge often arises: the curse of dimensionality. As datasets grow in size and complexity, the number of features or dimensions can explode, leading to increase...DiscussML algorithm intuitions with essential conceptsDimensionality Reduction
Saurabh Naiksaurabhz.hashnode.dev·Sep 30, 2023PCA Explained: The Key to Unlocking Insights in Multidimensional DataIntroduction: In the vast landscape of machine learning, dealing with high-dimensional data is a common challenge. As datasets grow in complexity, the curse of dimensionality can lead to increased computational demands, overfitting, and difficulty in...DiscussML algorithm intuitions with essential conceptsPca
Rhythm Rawatrhythmblogs.hashnode.dev·May 16, 2023Unsupervised Machine Learning Series: Dimensionality Reduction(5th algorithm)In the previous article, we understood the 4th Unsupervised ml algo: K-means . In this blog, we will cover our 5th unsupervised algorithm, dimensionality reduction. What is dimensionality reduction? Dimensionality reduction is a technique for reducin...Discuss·104 readsDimensionality Reduction
