Marktechpost

NOVEMBER 27, 2024

In today’s world, you’ve probably heard the term “Machine Learning” more than once. Machine Learning, a subset of Artificial Intelligence, has emerged as a transformative force, empowering machines to learn from data and make intelligent decisions without explicit programming. housing prices, stock prices).

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Marktechpost

JULY 18, 2024

This Paper addresses the limitations of classical machine learning approaches primarily developed for data lying in Euclidean space. Modern machine learning increasingly encounters richly structured data that is inherently non-Euclidean, exhibiting intricate geometric, topological, and algebraic structures.

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Marktechpost

SEPTEMBER 26, 2023

Many graphical models are designed to work exclusively with continuous or categorical variables, limiting their applicability to data that spans different types. Moreover, specific restrictions, such as continuous variables not being allowed as parents of categorical variables in directed acyclic graphs (DAGs), can hinder their flexibility.

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Marktechpost

SEPTEMBER 1, 2023

Artificial intelligence’s allure has long been shrouded in mystique, especially within the enigmatic realm of deep learning. These intricate neural networks, with their complex processes and hidden layers, have captivated researchers and practitioners while obscuring their inner workings. Check out the Paper.

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Becoming Human

MARCH 16, 2023

This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications. Through the next series of this post here , I will try to make an implementation of Graph Convolutional Neural Network. How do Graph Neural Networks work?

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Marktechpost

MAY 18, 2024

Graph Neural Networks GNNs are advanced tools for graph classification, leveraging neighborhood aggregation to update node representations iteratively. Effective graph pooling is essential for downsizing and learning representations, categorized into global and hierarchical pooling.

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Towards AI

APRIL 7, 2024

With the emergence of ARCGISpro which will replace ArcMap by 2026 mainly focusing on data science and machine learning, all the signs that machine learning is the future of GIS and you might have to learn some principles of data science, but where do you start, let us have a look.

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Marktechpost

NOVEMBER 26, 2023

The capacity of a model to use inputs at inference time to modify its behavior without updating its weights to tackle problems that were not present during training is known as in-context learning or ICL. Due to these convincing discoveries, emergent capabilities in massive neural networks have been the subject of study.

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IBM Journey to AI blog

OCTOBER 16, 2023

Machine learning (ML)—the artificial intelligence (AI) subfield in which machines learn from datasets and past experiences by recognizing patterns and generating predictions—is a $21 billion global industry projected to become a $209 billion industry by 2029.

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Marktechpost

DECEMBER 24, 2023

In this critical realm, the transformative power of machine learning is reshaping the landscape. As the demand for sustainable agriculture grows, machine learning emerges as a vital force, reshaping the future of food security and cultivation.

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Marktechpost

OCTOBER 18, 2023

The recent results of machine learning in drug discovery have been largely attributed to graph and geometric deep learning models. Like other deep learning techniques, they need a lot of training data to provide excellent modeling accuracy. If you like our work, you will love our newsletter. We are also on WhatsApp.

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Marktechpost

DECEMBER 11, 2023

Although measuring the degree of uncertainty in human judgments is challenging, machine learning methods offer assurance in their forecasts. Nevertheless, many machine learning applications use pretrained models that don’t provide any measure of atypicality. Rather, they only offer confidence levels.

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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. You might be using machine learning algorithms from everything you see on OTT or everything you shop online.

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Marktechpost

JULY 23, 2023

Artificial intelligence and machine learning are two innovative leaders as the world benefits from technology’s draw to sectors globally. You choose your future when you select a machine learning tool. We’ll look at some well-known machine-learning tools in this article.

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AssemblyAI

JANUARY 23, 2025

Broadly, Python speech recognition and Speech-to-Text solutions can be categorized into two main types: open-source libraries and cloud-based services. Modern speech recognition systems often leverage machine learning and artificial intelligence, allowing them to handle various accents, languages, and speaking styles with impressive accuracy.

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Unite.AI

JULY 25, 2024

This new technique leverages X-ray photon correlation spectroscopy (XPCS), artificial intelligence, and machine learning to create “fingerprints” of different materials. These fingerprints can then be analyzed by a neural network, unveiling previously inaccessible information about material behavior.

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Marktechpost

OCTOBER 19, 2024

While neural network approaches like Transformers offer more flexibility, they are limited by fixed input dimensions, restricting their application to tasks with structured inputs. The results show that the method effectively handles a mix of continuous and categorical parameters in optimization scenarios.

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Towards AI

FEBRUARY 19, 2024

As machine learning advances, we see a big increase in their use for predicting time series data. The following article is an experimental study that uses both statistical forecasts and machine learning-based forecasts to predict the future for a practical use case. This is why they’re top choices for future predictions.

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Heartbeat

JUNE 27, 2023

Photo by Resource Database on Unsplash Introduction Neural networks have been operating on graph data for over a decade now. Neural networks leverage the structure and properties of graph and work in a similar fashion. Graph Neural Networks are a class of artificial neural networks that can be represented as graphs.

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Towards AI

JANUARY 23, 2025

When we talk about building powerful machine learning solutions, like large language models or retrieval-augmented generation, one key element that often flies under the radar is how to connect all the data and models and deploy them in a real product. If this sounds interesting, reach out in the thread! Meme of the week!

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Marktechpost

MARCH 11, 2024

Value functions are a core component of deep reinforcement learning (RL). Value functions, implemented with neural networks, undergo training via mean squared error regression to align with bootstrapped target values. The HL-Gauss approach, in particular, yields significant improvements across diverse tasks and domains.

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Towards AI

APRIL 4, 2024

Created by the author with DALL E-3 Machine learning algorithms are the “cool kids” of the tech industry; everyone is talking about them as if they were the newest, greatest meme. Amidst the hoopla, do people actually understand what machine learning is, or are they just using the word as a text thread equivalent of emoticons?

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Pickl AI

DECEMBER 4, 2024

Summary: A perceptron is the simplest form of an artificial neural network, designed to classify input data into two categories. Perceptrons are foundational in Machine Learning, paving the way for more complex models. Learning involves adjusting weights based on prediction errors. How Does a Perceptron Learn?

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ODSC - Open Data Science

JUNE 6, 2023

Be sure to check out their talk, “ Getting Up to Speed on Real-Time Machine Learning ,” there! The benefits of real-time machine learning are becoming increasingly apparent. Anomaly detection, including fraud detection and network intrusion monitoring, particularly exemplifies the challenges of real-time machine learning.

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Unite.AI

AUGUST 22, 2023

You spent over 7 years at Google, where you helped to build and lead teams working on strategy, operations, big data and machine learning. What was your favorite project and what did you learn from this experience? Outside of Neuralsearch™, what are some of the other machine learning methodologies that are used?

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Marktechpost

JUNE 8, 2024

Combinatorial Optimization (CO) can be seen as a distribution learning problem where the samples correspond to solutions of CO problems, but it is challenging to achieve unbiased samples. Areas like CO or lattice models in physics involve discrete target distributions, which can be approximated using products of categorical distributions.

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Marktechpost

MARCH 21, 2025

In this tutorial, we explore an innovative and practical application of IBM’s open-source ResNet-50 deep learning model, showcasing its capability to classify satellite imagery for disaster management rapidly. With minimal setup, we now have a powerful tool at our disposal. Here is the Colab Notebook.

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ML Review

MARCH 31, 2019

Are the machine learning models we use intrinsically flawed? Up to this point, machine learning algorithms simply didn’t work well enough for anyone to be surprised when it failed to do the right thing. We start with an image of a panda, which our neural network correctly recognizes as a “panda” with 57.7%

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IBM Journey to AI blog

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. A foundation model is built on a neural network model architecture to process information much like the human brain does.

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Viso.ai

DECEMBER 19, 2023

Today, the use of convolutional neural networks (CNN) is the state-of-the-art method for image classification. Image Classification Using Machine Learning CNN Image Classification (Deep Learning) Example applications of Image Classification Let’s dive deep into it! How Does Image Classification Work?

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AWS Machine Learning Blog

FEBRUARY 25, 2025

As AIDAs interactions with humans proliferated, a pressing need emerged to establish a coherent system for categorizing these diverse exchanges. The main reason for this categorization was to develop distinct pipelines that could more effectively address various types of requests.

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Marktechpost

MARCH 8, 2025

The brains language network (LN), a collection of left-lateralized frontotemporal regions, is crucial in processing linguistic input. Recent advancements in machine learning have positioned LLMs, which are trained on vast text corpora using next-word prediction, as promising computational models for studying LN functions.

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Viso.ai

OCTOBER 10, 2024

The identification of regularities in data can then be used to make predictions, categorize information, and improve decision-making processes. While explorative pattern recognition aims to identify data patterns in general, descriptive pattern recognition starts by categorizing the detected patterns.

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Heartbeat

AUGUST 21, 2023

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs are artificial neural networks built to handle data having a grid-like architecture, such as photos or movies.

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Towards AI

JULY 9, 2024

Advances in artificial intelligence and machine learning have led to the development of increasingly complex object detection algorithms, which allow us to efficiently and precisely interpret large volumes of geographical data. Another method for figuring out which category a detected object belongs to is object categorization.

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Unite.AI

MAY 24, 2024

In modern machine learning and artificial intelligence frameworks, transformers are one of the most widely used components across various domains including GPT series, and BERT in Natural Language Processing, and Vision Transformers in computer vision tasks. So let’s get started. MambaOut: Is Mamba Really Needed for Vision?

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Pickl AI

AUGUST 28, 2024

It also includes practical implementation steps and discusses the future of classification in Machine Learning. Introduction Machine Learning has revolutionised the way we analyse and interpret data, enabling machines to learn from historical data and make predictions or decisions without explicit programming.

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