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. Industrial Internet of Things (IIoT) The Constraints Within the area of Industry 4.0,

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Towards AI

MAY 3, 2023

Solving Machine Learning Tasks with MLCoPilot: Harnessing Human Expertise for Success Many of us have made use of large language models (LLMs) like ChatGPT to generate not only text and images but also code, including machine learning code. This is where ML CoPilot enters the scene.

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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. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks.

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Mlearning.ai

NOVEMBER 3, 2023

I think I managed to get most of the ML players in there…?? And who by gradient descent, who by quick segment Who in the cluster, who in the top percent Who by linear regression, who by SVM Who in random forest, who in deep learning And who shall I say is normalizing?

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Towards AI

APRIL 7, 2024

Additionally, the elimination of human loop processes has made it possible for AI/ML to construct training data for data annotation and labeling, which has a major influence on geospatial data. This function can be improved by AI and ML, which allow GIS to produce insights, automate procedures, and learn from data.

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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

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. Typical unsupervised learning tasks include clustering (e.g.,

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Mlearning.ai

AUGUST 28, 2023

I recently took the Azure Data Scientist Associate certification exam DP-100, thankfully I passed after about 3–4 months for studying the Microsoft Data Science Learning Path and the Coursera Microsoft Azure Data Scientist Associate Specialization. Resources include the: Resource group, Azure ML studio, Azure Compute Cluster.

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Towards AI

JUNE 27, 2023

This list, encompassing the most effective machine learning tools, serves as a valuable guide for developers seeking to enhance their machine learning toolkit and drive the AI revolution forward. This open-source library is renowned for its capabilities in numerical computation, particularly in large-scale machine learning projects.

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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

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. We recently developed four more new models.

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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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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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Smart Data Collective

NOVEMBER 1, 2020

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? If you’ve ever come across deep learning, you might have heard about two methods to teach machines: supervised and unsupervised. Unsupervised ML: The Basics.

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Dataconomy

MAY 16, 2023

Mathematical and statistical knowledge: A solid foundation in mathematical concepts, linear algebra, calculus, and statistics is necessary to understand the underlying principles of machine learning algorithms. Combining their complementary skills and expertise leads to comprehensive and impactful data-driven solutions.

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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. This is where visualizations in ML come in.

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ODSC - Open Data Science

MARCH 27, 2023

You’ll hear how declarative machine learning has been essential to the speedy adoption at leading institutions such as Apple and Meta, as well as learn about Ludwig, the open-source declarative machine learning framework. Botnets Detection at Scale — Lesson Learned from Clustering Billions of Web Attacks into Botnets.

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The MLOps Blog

DECEMBER 19, 2022

Classification is one of the most widely applied areas in Machine Learning. As Data Scientists, we all have worked on an ML classification model. Traditional Machine Learning and Deep Learning methods are used to solve Multiclass Classification problems, but the model’s complexity increases as the number of classes increases.

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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. The primary goal of the Kaggle competition is creating an ML Model that can predict the total number of bikes rented.

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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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ODSC - Open Data Science

JUNE 6, 2023

And use the deep probabilistic logic programming languages DeepProbLog and DeepStochLog to illustrate his point. Why GPU Clusters Don’t Need to Go Brrr? Unifying ML With One Line of Code Daniel Lenton, PhD | CEO | Ivy Why should we try to unify the ML frameworks? From Correlation to Causality in AI Dr.

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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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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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Towards AI

APRIL 5, 2023

The articles cover a range of topics, from the basics of Rust to more advanced machine learning concepts, and provide practical examples to help readers get started with implementing ML algorithms in Rust. One of the unique features of SmartCore is its emphasis on interpretability.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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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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ODSC - Open Data Science

JULY 14, 2023

Probabilistic Machine Learning for Finance and Investing Deepak Kanungo | Founder and CEO, Advisory Board Member | Hedged Capital LLC, AIKON This session will introduce you to the reasons why probabilistic machine learning is the next generation of AI in finance and investing.

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ODSC - Open Data Science

JUNE 7, 2023

Gözde Gül Şahin | Assistant Professor, KUIS AI Fellow | KOC University Fraud Detection with Machine Learning: Laura Mitchell | Senior Data Science Manager | MoonPay Deep Learning and Comparisons between Large Language Models: Hossam Amer, PhD | Applied Scientist | Microsoft Multimodal Video Representations and Their Extension to Visual Language Navigation: (..)

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ODSC - Open Data Science

JULY 22, 2023

Keynotes Our main keynote sessions were held on the virtual side of the conference.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

Sharing in-house resources with other internal teams, the Ranking team machine learning (ML) scientists often encountered long wait times to access resources for model training and experimentation – challenging their ability to rapidly experiment and innovate. If it shows online improvement, it can be deployed to all the users.

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Defined.ai blog

OCTOBER 9, 2022

Fight sophisticated cyber attacks with AI and ML When “virtual” became the standard medium in early 2020 for business communications from board meetings to office happy hours, companies like Zoom found themselves hot in demand. There is also concern that attackers are using AI and ML technology to launch smarter, more advanced attacks.

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ODSC - Open Data Science

AUGUST 15, 2023

You’ll get hands-on practice with unsupervised learning techniques, such as K-Means clustering, and classification algorithms like decision trees and random forest. Finally, you’ll explore how to handle missing values and training and validating your models using PySpark.

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Towards AI

JULY 19, 2023

Please refer to Part 1– to understand what is Sales Prediction/Forecasting, the Basic concepts of Time series modeling, and EDA I’m working on Part 3 where I will be implementing Deep Learning and Part 4 where I will be implementing a supervised ML model.

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Mlearning.ai

APRIL 8, 2023

In this article, we’ll look at the evolution of these state-of-the-art (SOTA) models and algorithms, the ML techniques behind them, the people who envisioned them, and the papers that introduced them. 2017) “ BERT: Pre-training of deep bidirectional transformers for language understanding ” by Devlin et al.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. For more information, refer to Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

The BigBasket team was running open source, in-house ML algorithms for computer vision object recognition to power AI-enabled checkout at their Fresho (physical) stores. Their objective was to fine-tune an existing computer vision machine learning (ML) model for SKU detection. Log model training metrics.

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phData

JUNE 28, 2023

Access to GPUs within Snowflake will allow organizations to build and harness AI, machine learning (ML), and large language models (LLM) right within Snowflake. Today’s most cutting-edge Generative AI and LLM applications are all trained using large clusters of GPU-accelerated hardware.

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