Towards AI

APRIL 5, 2023

Rustic Learning: Machine Learning in Rust — Part 2: Regression and Classification An Introduction to Rust’s Machine Learning crates Photo by Malik Skydsgaard on Unsplash Rustic Learning is a series of articles that explores the use of Rust programming language for machine learning tasks.

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PyImageSearch

MARCH 30, 2023

If you want a gentle introduction to machine learning for computer vision, you’re in the right spot. Here at PyImageSearch we’ve been helping people just like you master deep learning for computer vision. Also, you might want to check out our computer vision for deep learning program before you go.

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Smart Data Collective

NOVEMBER 1, 2020

Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries. What Is Unsupervised Machine Learning?

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Mlearning.ai

JULY 17, 2023

Clustering — Beyonds KMeans+PCA… Perhaps the most popular way of clustering is K-Means. It is also very common as well to combine K-Means with PCA for visualizing the clustering results, and many clustering applications follow that path (e.g. this link ).

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Towards AI

JUNE 27, 2023

Last Updated on June 27, 2023 by Editorial Team Source: Unsplash This piece dives into the top machine learning developer tools being used by developers — start building! In the rapidly expanding field of artificial intelligence (AI), machine learning tools play an instrumental role.

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Dataconomy

FEBRUARY 28, 2023

Deep learning models have emerged as a powerful tool in the field of ML, enabling computers to learn from vast amounts of data and make decisions based on that learning. In this article, we will explore the importance of deep learning models and their applications in various fields.

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Pickl AI

AUGUST 1, 2023

As technology continues to impact how machines operate, Machine Learning has emerged as a powerful tool enabling computers to learn and improve from experience without explicit programming. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types.

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Mlearning.ai

APRIL 10, 2023

How this machine learning model has become a sustainable and reliable solution for edge devices in an industrial network An Introduction Clustering (cluster analysis - CA) and classification are two important tasks that occur in our daily lives. Thus, this type of task is very important for exploratory data analysis.

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Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. For reference, GPT-3, an earlier generation LLM has 175 billion parameters and requires months of non-stop training on a cluster of thousands of accelerated processors.

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ODSC - Open Data Science

MARCH 17, 2023

At the end of the day, why not use an AutoML package (Automated Machine Learning) or an Auto-Forecasting tool and let it do the job for you? However, we already know that: Machine Learning models deliver better results in terms of accuracy when we are dealing with interrelated series and complex patterns in our data.

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IBM Journey to AI blog

DECEMBER 19, 2023

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

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ODSC - Open Data Science

JULY 26, 2023

As a senior data scientist, I often encounter aspiring data scientists eager to learn about machine learning (ML). In this comprehensive guide, I will demystify machine learning, breaking it down into digestible concepts for beginners. What is Machine Learning?

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ODSC - Open Data Science

OCTOBER 18, 2023

Many companies are now utilizing data science and machine learning , but there’s still a lot of room for improvement in terms of ROI. Nevertheless, we are still left with the question: How can we do machine learning better? billion in 2022, an increase of 21.3%

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Pickl AI

SEPTEMBER 3, 2023

Introduction Anomaly detection is identified as one of the most common use cases in Machine Learning. The following blog will provide you a thorough evaluation on how Anomaly Detection Machine Learning works, emphasising on its types and techniques. Billion which is supposed to increase by 35.6% CAGR during 2022-2030.

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Heartbeat

SEPTEMBER 25, 2023

Photo by National Cancer Institute on Unsplash Machine learning has recently been a game-changer in healthcare and medical diagnosis. This is where machine learning comes in. Furthermore, machine learning algorithms are revolutionizing the concept of personalized medicine.

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Data Science Dojo

APRIL 27, 2023

Trending GitHub Repositories Scikit-learn:  A Python library for machine learning built on top of NumPy, SciPy, and matplotlib. It provides a range of algorithms for classification, regression, clustering, and more.   PyTorch:  An open-source machine learning library developed by Facebook’s AI research group.

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Data Science Blog

MAY 22, 2023

Vektor-Datenbanken sind ein weiterer Typ von Datenbank, die unter Einsatz von AI (Deep Learning, n-grams, …) Wissen in Vektoren übersetzen und damit vergleichbarer und wieder auffindbarer machen. der k-Nächste-Nachbarn -Prädiktionsalgorithmus (Regression/Klassifikation) oder K-Means-Clustering.

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Mlearning.ai

APRIL 1, 2023

Image taken from Efficient Estimation of Word Representation in Vector Space Top2Vec Top2Vec is an unsupervised machine-learning model designed for topic modelling and document clustering. For this, Top2Vec utilizes a manifold learning technique called UMAP. To achieve this, Top2Vec utilizes the doc2vec model.

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ODSC - Open Data Science

MARCH 27, 2023

The Next Generation of Low-Code Machine Learning Devvret Rishi | Co-founder and Chief Product Officer | Predibase In this session, you’ll explore declarative machine learning, a configuration-based modeling interface, which provides more flexibility and simplicity when implementing cutting-edge machine learning.

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AWS Machine Learning Blog

DECEMBER 22, 2023

As a result, machine learning practitioners must spend weeks of preparation to scale their LLM workloads to large clusters of GPUs. Integrating tensor parallelism to enable training on massive clusters This release of SMP also expands PyTorch FSDP’s capabilities to include tensor parallelism techniques.

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AWS Machine Learning Blog

OCTOBER 5, 2023

Our high-level training procedure is as follows: for our training environment, we use a multi-instance cluster managed by the SLURM system for distributed training and scheduling under the NeMo framework. Before that, he worked on developing machine learning methods for fraud detection for Amazon Fraud Detector.

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Mlearning.ai

JUNE 29, 2023

Photo by Aditya Chache on Unsplash DBSCAN in Density Based Algorithms : Density Based Spatial Clustering Of Applications with Noise. Earlier Topics: Since, We have seen centroid based algorithm for clustering like K-Means.Centroid based : K-Means, K-Means ++ , K-Medoids. & One among the many density based algorithms is “DBSCAN”.

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AWS Machine Learning Blog

FEBRUARY 1, 2023

Recent developments in deep learning have led to increasingly large models such as GPT-3, BLOOM, and OPT, some of which are already in excess of 100 billion parameters. Many enterprise customers choose to deploy their deep learning workloads using Kubernetes—the de facto standard for container orchestration in the cloud.

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The MLOps Blog

NOVEMBER 14, 2023

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. The reasons for this range from wrongly connected model components to misconfigured optimizers.

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AWS Machine Learning Blog

DECEMBER 12, 2023

In this post, we’ll summarize training procedure of GPT NeoX on AWS Trainium , a purpose-built machine learning (ML) accelerator optimized for deep learning training. In this post, we showed cost-efficient training of LLMs on AWS deep learning hardware. We’ll outline how we cost-effectively (3.2

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ODSC - Open Data Science

FEBRUARY 17, 2023

Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others. Machine & Deep Learning Machine learning is the fundamental data science skillset, and deep learning is the foundation for NLP.

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Data Science Blog

JUNE 30, 2023

The skill clusters are formed via the discipline of Topic Modelling , a method from unsupervised machine learning , which show the differences in the distribution of requirements between them.

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Heartbeat

AUGUST 14, 2023

Expertise in image segmentation requires knowledge of various techniques, ranging from traditional methods, such as thresholding and edge-based segmentation, to more advanced techniques, like deep learning-based segmentation. The resulting clusters can then be used to segment the image into different regions.

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Pickl AI

JUNE 7, 2023

Face Recognition One of the most effective Github Projects on Data Science is a Face Recognition project that makes use of Deep Learning and Histogram of Oriented Gradients (HOG) algorithm. Customer Segmentation using K-Means Clustering One of the most crucial uses of data science is customer segmentation.

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Smart Data Collective

JUNE 4, 2021

Basics of Machine Learning. Machine learning is the science of building models automatically. Whereas in machine learning, the algorithm understands the data and creates the logic. Whereas in machine learning, the algorithm understands the data and creates the logic. Semi-Supervised Learning.

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Dataconomy

MAY 16, 2023

Machine learning engineer vs data scientist: two distinct roles with overlapping expertise, each essential in unlocking the power of data-driven insights. As businesses strive to stay competitive and make data-driven decisions, the roles of machine learning engineers and data scientists have gained prominence.

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Dataconomy

AUGUST 9, 2023

This is where the power of machine learning (ML) comes into play. Machine learning algorithms, with their ability to recognize patterns, anomalies, and trends within vast datasets, are revolutionizing network traffic analysis by providing more accurate insights, faster response times, and enhanced security measures.

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DataRobot Blog

FEBRUARY 14, 2022

Because our dataset contains image data, DataRobot used models that contain deep learning based image featurizers. For clustering projects, we offer insights that can help you understand the makeup of the clusters. Learn More About Explainable AI. Learn more. Activation Maps. MLOps Explainability .

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ODSC - Open Data Science

JULY 22, 2023

We had bigger sessions on getting started with machine learning or SQL, up to advanced topics in NLP, and how to make deepfakes. Keynotes Our main keynote sessions were held on the virtual side of the conference.

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ODSC - Open Data Science

JUNE 7, 2023

Bilokon | Visiting Lecturer, CEO and Founder | Imperial College London, Thalesians Ltd Apache Kafka for Real-Time Machine Learning Without a Data Lake: Kai Waehner | Global Field CTO, Author, International Speaker Semantic Analysis and Procedural Language Understanding in the Era of Large Language Models: Dr. Gözde Gül Şahin | Assistant Professor, (..)

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DataRobot

DECEMBER 20, 2021

Image recognition is one of the most relevant areas of machine learning. Deep learning makes the process efficient. However, not everyone has deep learning skills or budget resources to spend on GPUs before demonstrating any value to the business. Multimodal Clustering. DataRobot Visual AI.

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Pickl AI

NOVEMBER 8, 2023

The Scikit-Learn cheat sheet is a concise reference guide for using Scikit-Learn , a popular Machine Learning library in Python. It offers quick access to key functions and concepts, including data preprocessing, supervised and unsupervised learning techniques, and model evaluation.

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