Heartbeat

SEPTEMBER 22, 2023

I write about Machine Learning on Medium || Github || Kaggle || Linkedin. ? Introduction In the world of machine learning, where algorithms learn from data to make predictions, it’s important to get the best out of our models. Comet ML provides a platform for test tracking and hyperparameter optimization.

Machine Learning 64

Machine Learning Machine Learning ML ML 64

Mlearning.ai

JULY 6, 2023

NOTES, DEEP LEARNING, REMOTE SENSING, ADVANCED METHODS, SELF-SUPERVISED LEARNING A note of the paper I have read Photo by Kelly Sikkema on Unsplash Hi everyone, In today’s story, I would share notes I took from 32 pages of Wang et al., 2022 Deep learning notoriously needs a lot of data in training. 2022’s paper.

Supervised Learning 52

Supervised Learning Deep Learning Deep Learning K-nearest Neighbors 52

AWS Machine Learning Blog

APRIL 5, 2023

Amazon SageMaker Serverless Inference is a purpose-built inference service that makes it easy to deploy and scale machine learning (ML) models. k-NN index query – This is the inference phase of the application. Then, you use those embeddings to query the reference k-NN index stored in OpenSearch Service.

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ML ML AWS K-nearest Neighbors 105

ODSC - Open Data Science

JANUARY 5, 2024

With the explosion of AI across industries TensorFlow has also grown in popularity due to its robust ecosystem of tools, libraries, and community that keeps pushing machine learning advances. As anyone knows it’s important to have the ability to handle high traffic, and Django allows for easy scalability for web applications.

Python 52

Python K-nearest Neighbors Data Science Data Visualization 52

Snorkel AI

APRIL 11, 2023

I’m Cody Coleman and I’m really excited to share my research on how careful data selection can make ML development faster, cheaper, and better by focusing on quality rather than quantity. Active learning is a really powerful data selection technique for reducing labeling costs.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Deep Learning Deep Learning 52

Snorkel AI

APRIL 11, 2023

I’m Cody Coleman and I’m really excited to share my research on how careful data selection can make ML development faster, cheaper, and better by focusing on quality rather than quantity. Active learning is a really powerful data selection technique for reducing labeling costs.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Deep Learning Deep Learning 52

Snorkel AI

APRIL 11, 2023

I’m Cody Coleman and I’m really excited to share my research on how careful data selection can make ML development faster, cheaper, and better by focusing on quality rather than quantity. Active learning is a really powerful data selection technique for reducing labeling costs.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Deep Learning Deep Learning 52

Pickl AI

JULY 17, 2023

It aims to partition a given dataset into K clusters, where each data point belongs to the cluster with the nearest mean. K-NN (k nearest neighbors): K-Nearest Neighbors (K-NN) is a simple yet powerful algorithm used for both classification and regression tasks in Machine Learning.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Clustering 52

Google Research AI blog

AUGUST 15, 2023

Effective recommendations that present students with relevant reading material helps keep students reading, and this is where machine learning (ML) can help. ML has been widely used in building recommender systems for various types of digital content, ranging from videos to books to e-commerce items.

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K-nearest Neighbors ML ML Algorithm 75

AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data.

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ML ML Machine Learning Machine Learning 73

Pickl AI

SEPTEMBER 3, 2023

49% of companies in the world that use Machine Learning and AI in their marketing and sales processes apply it to identify the prospects of sales. On the other hand, 48% use ML and AI for gaining insights into the prospects and customers. Anomalies might lead to deviations from the normal patterns the model has learned.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Mlearning.ai

MAY 23, 2023

With the advancement of technology, machine learning, and computer vision techniques can be used to develop automated solutions for leaf disease detection. In this article, we will discuss the development of a Leaf Disease Detection Flask App that uses a deep learning model to automatically detect the presence of leaf diseases.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Python 52

DagsHub

DECEMBER 23, 2024

Key Characteristics Static Dataset : Works with a predefined set of unlabeled examples Batch Selection : Can select multiple samples simultaneously for labeling because of which it is widely used by deep learning models. Pool-Based Active Learning Scenario : Classifying images of artwork styles for a digital archive.  

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Clustering 52

Mlearning.ai

FEBRUARY 15, 2023

K-Nearest Neighbou r: The k-Nearest Neighbor algorithm has a simple concept behind it. The method seeks the k nearest neighbours among the training documents to classify a new document and uses the categories of the k nearest neighbours to weight the category candidates [3].

Cross Validation 52

Cross Validation Decision Trees Algorithm Natural Language Processing 52

Mlearning.ai

MAY 21, 2023

With the advancement of technology, machine learning, and computer vision techniques can be used to develop automated solutions for leaf disease detection. In this article, we will discuss the development of a Leaf Disease Detection Flask App that uses a deep learning model to automatically detect the presence of leaf diseases.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Python 52

Mlearning.ai

JUNE 6, 2023

In today’s blog, we will see some very interesting Python Machine Learning projects with source code. This list will consist of Machine learning projects, Deep Learning Projects, Computer Vision Projects , and all other types of interesting projects with source codes also provided. This is a simple project.

Machine Learning 52

Machine Learning Machine Learning Python Deep Learning 52

Mlearning.ai

MAY 23, 2023

Let us first understand the meaning of bias and variance in detail: Bias: It is a kind of error in a machine learning model when an ML Algorithm is oversimplified. It is introduced into an ML Model when an ML algorithm is made highly complex. This model also learns noise from the data set that is meant for training.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Pickl AI

JULY 26, 2023

Highly Flexible Neural Networks Deep neural networks with a large number of layers and parameters have the potential to memorize the training data, resulting in high variance. K-Nearest Neighbors with Small k I n the k-nearest neighbours algorithm, choosing a small value of k can lead to high variance.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

DagsHub

JUNE 24, 2024

The Technology Behind the Tool Image embeddings are powered by advances in deep learning , particularly through the use of Convolutional Neural Networks (CNNs); great advancements have also come with Transformer architectures. They also enable few-shot learning for training ML models, reducing the number of examples needed.

Clustering 40

Clustering Machine Learning Machine Learning K-nearest Neighbors 40

Google Research AI blog

NOVEMBER 28, 2022

Organizing Committee General Chairs includes: Sanmi Koyejo Program Chairs include: Alekh Agarwal Workshop Chairs include: Hanie Sedghi Tutorial Chairs include: Adji Bousso Dieng , Jessica Schrouff Affinity Workshop Chair: Adji Bousso Dieng , Jessica Schrouff Program Committee, Senior Area Chairs include: Corinna Cortes , Claudio Gentile , Mohammad (..)

Machine Learning 52

Machine Learning Machine Learning Algorithm Clustering 52