Mlearning.ai

NOVEMBER 29, 2023

How to Use Machine Learning (ML) for Time Series Forecasting — NIX United The modern market pace calls for a respective competitive edge. ML-based predictive models nowadays may consider time-dependent components — seasonality, trends, cycles, irregular components, etc. — to

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Flipboard

NOVEMBER 17, 2023

Amazon SageMaker enables enterprises to build, train, and deploy machine learning (ML) models. Amazon SageMaker JumpStart provides pre-trained models and data to help you get started with ML. This type of data is often used in ML and artificial intelligence applications.

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AWS Machine Learning Blog

NOVEMBER 18, 2024

Amazon OpenSearch Serverless is a serverless deployment option for Amazon OpenSearch Service, a fully managed service that makes it simple to perform interactive log analytics, real-time application monitoring, website search, and vector search with its k-nearest neighbor (kNN) plugin.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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Mlearning.ai

FEBRUARY 17, 2023

ML algorithms can be broadly divided into supervised learning , unsupervised learning , and reinforcement learning. How is it actually looks in a real life process of ML investigation? In this article, I will cover all of them. Reward(1) or punishment(0).

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Snorkel AI

APRIL 28, 2023

The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. You can approximate your machine learning training components into some simpler classifiers—for example, a k-nearest neighbors classifier. A transcript of the talk follows.

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Snorkel AI

APRIL 28, 2023

The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. You can approximate your machine learning training components into some simpler classifiers—for example, a k-nearest neighbors classifier. A transcript of the talk follows.

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Towards AI

JULY 15, 2024

We shall look at various machine learning algorithms such as decision trees, random forest, K nearest neighbor, and naïve Bayes and how you can install and call their libraries in R studios, including executing the code. I wrote about Python ML here. Join thousands of data leaders on the AI newsletter.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

Amazon Rekognition makes it easy to add image analysis capability to your applications without any machine learning (ML) expertise and comes with various APIs to fulfil use cases such as object detection, content moderation, face detection and analysis, and text and celebrity recognition, which we use in this example.

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Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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AWS Machine Learning Blog

APRIL 3, 2024

We detail the steps to use an Amazon Titan Multimodal Embeddings model to encode images and text into embeddings, ingest embeddings into an OpenSearch Service index, and query the index using the OpenSearch Service k-nearest neighbors (k-NN) functionality. In her free time, she likes to go for long runs along the beach.

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Pickl AI

AUGUST 22, 2023

This harmonization is particularly critical in algorithms such as k-Nearest Neighbors and Support Vector Machines, where distances dictate decisions. To start your learning journey in Machine Learning, you can opt for a free course in ML. Having expertise in this domain will give you an edge over your competitors.

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The MLOps Blog

DECEMBER 19, 2022

As Data Scientists, we all have worked on an ML classification model. In this article, we will talk about feasible techniques to deal with such a large-scale ML Classification model. In this article, you will learn: 1 What are some examples of large-scale ML classification models? Let’s take a look at some of them.

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Mlearning.ai

FEBRUARY 2, 2023

Define the classifiers: Choose a set of classifiers that you want to use, such as support vector machine (SVM), k-nearest neighbors (KNN), or decision tree, and initialize their parameters. bag of words or TF-IDF vectors) and splitting the data into training and testing sets.

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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.

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Heartbeat

SEPTEMBER 22, 2023

K-Nearest Neighbors (KNN) Classifier: The KNN algorithm relies on selecting the right number of neighbors and a power parameter p. Automating Hyperparameter Tuning with Comet ML To streamline the hyperparameter tuning process, tools like Comet ML come into play. Follow “Nhi Yen” for future updates!

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AWS Machine Learning Blog

SEPTEMBER 6, 2023

A k-Nearest Neighbor (k-NN) index is enabled to allow searching of embeddings from the OpenSearch Service. Shikhar Kwatra is an AI/ML Specialist Solutions Architect at Amazon Web Services, working with a leading Global System Integrator.

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Mlearning.ai

JULY 4, 2023

By exploring a range of classification algorithms, we ultimately identified the k-nearest neighbor (KNN) algorithm as remarkably successful in predicting the races of individuals. BECOME a WRITER at MLearning.ai // invisible ML // Detect AI img Mlearning.ai

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

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AWS Machine Learning Blog

APRIL 19, 2024

We perform a k-nearest neighbor (k-NN) search to retrieve the most relevant embeddings matching the user query. As per the AI/ML flywheel, what do the AWS AI/ML services provide? Based on the summary, the AWS AI/ML services provide a range of capabilities that fuel an AI/ML flywheel.

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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. So have you tried other clustering approaches other than K-means, and how does that impact this entire process? AB : Got it. Thank you.

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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. So have you tried other clustering approaches other than K-means, and how does that impact this entire process? AB : Got it. Thank you.

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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. So have you tried other clustering approaches other than K-means, and how does that impact this entire process? AB : Got it. Thank you.

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Mlearning.ai

APRIL 6, 2023

⚠ You can solve the below-mentioned questions from this blog ⚠ ✔ What if I am building Low code — No code ML automation tool and I do not have any orchestrator or memory management system ? ✔ how to reduce the complexity and computational expensiveness of ML models ? will my data help in this ?

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AWS Machine Learning Blog

NOVEMBER 30, 2023

The previous post discussed how you can use Amazon machine learning (ML) services to help you find the best images to be placed along an article or TV synopsis without typing in keywords. Amazon Rekognition automatically recognizes tens of thousands of well-known personalities in images and videos using ML.

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AWS Machine Learning Blog

JANUARY 6, 2023

In Part 2 , we demonstrated how to use Amazon Neptune ML (in Amazon SageMaker ) to train the KG and create KG embeddings. This mapping can be done by manually mapping frequent OOC queries to catalog content or can be automated using machine learning (ML). Matthew Rhodes is a Data Scientist I working in the Amazon ML Solutions Lab.

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ODSC - Open Data Science

JANUARY 5, 2024

PyTorch This essential library is an open-source ML framework capable of speeding up research prototyping, allowing companies to enter the production deployment phase. Scikit-learn is also open-source, which makes it a popular choice for both academic and commercial use. Currently, Django is still at over 74,000 stars on GitHub.

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AWS Machine Learning Blog

JANUARY 30, 2024

We perform a k-nearest neighbor (k=1) search to retrieve the most relevant embedding matching the user query. Setting k=1 retrieves the most relevant slide to the user question. As per the AI/ML flywheel, what do the AWS AI/ML services provide? get('hits')[0].get('_source').get('image_path')

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AWS Machine Learning Blog

JUNE 3, 2024

Kinesis Video Streams makes it straightforward to securely stream video from connected devices to AWS for analytics, machine learning (ML), playback, and other processing. He is passionate about IoT, AI/ML and building smart home devices. It enables real-time video ingestion, storage, encoding, and streaming across devices.

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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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Mlearning.ai

JULY 6, 2023

Some common quantitative evaluations are linear probing , K nearest neighbors (KNN), and fine-tuning. D BECOME a WRITER at MLearning.ai // invisible ML // Detect AI img Mlearning.ai Evaluation Matrix In SSL, the performance evaluation is usually conducted while doing transfer learning in the downstream task.

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Mlearning.ai

MARCH 28, 2023

For each sample in the minority class, it selects k nearest neighbors from the same class. It then selects one of these k neighbors at random and computes the difference between the feature vector of the original sample and the selected neighbor.

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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. In this post, we deep dive into the technical details of this ML model.

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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. PyTorch is an open-source ML framework that accelerates the path from research prototyping to production deployment. You can use CLIP with Amazon SageMaker to perform encoding.

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AWS Machine Learning Blog

AUGUST 26, 2024

This includes sales collateral, customer engagements, external web data, machine learning (ML) insights, and more. AI-driven recommendations – By combining generative AI with ML, we deliver intelligent suggestions for products, services, applicable use cases, and next steps.

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Pickl AI

SEPTEMBER 3, 2023

On the other hand, 48% use ML and AI for gaining insights into the prospects and customers. k-Nearest Neighbors (k-NN): In the supervised approach, k-NN assigns labels to instances based on their k-nearest neighbours. An ensemble of decision trees is trained on both normal and anomalous data.

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