AI News

JULY 22, 2024

By leveraging data analytics, machine learning, and real-time processing, AI is turning the traditional approach to sports betting on its head. This article delves into how AI algorithms are transforming sports betting, providing actual data, statistics, and insights that demonstrate their impact.

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Unite.AI

DECEMBER 7, 2024

These innovative platforms combine advanced AI and natural language processing (NLP) with practical features to help brands succeed in digital marketing, offering everything from real-time safety monitoring to sophisticated creator verification systems.

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Analytics Vidhya

JANUARY 17, 2022

Introduction A few days ago, I came across a question on “Quora” that boiled down to: “How can I learn Natural Language Processing in just only four months?” This article was published as a part of the Data Science Blogathon. ” Then I began to write a brief response.

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Unite.AI

NOVEMBER 6, 2023

Traditional machine learning is a broad term that covers a wide variety of algorithms primarily driven by statistics. The two main types of traditional ML algorithms are supervised and unsupervised. These algorithms are designed to develop models from structured datasets. Do We Still Need Traditional Machine Learning Algorithms?

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Unite.AI

SEPTEMBER 15, 2024

Their findings, recently published in Nature , represent a significant leap forward in the field of neuromorphic computing – a branch of computer science that aims to mimic the structure and function of biological neural networks.

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Marktechpost

JANUARY 24, 2024

With the growth of Deep learning, it is used in many fields, including data mining and natural language processing. However, deep neural networks are inaccurate and can produce unreliable outcomes. It can improve deep neural networks’ reliability in inverse imaging issues.

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Marktechpost

DECEMBER 23, 2023

Deep Neural Networks (DNNs) represent a powerful subset of artificial neural networks (ANNs) designed to model complex patterns and correlations within data. These sophisticated networks consist of multiple layers of interconnected nodes, enabling them to learn intricate hierarchical representations.

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Unite.AI

OCTOBER 27, 2024

The ecosystem has rapidly evolved to support everything from large language models (LLMs) to neural networks, making it easier than ever for developers to integrate AI capabilities into their applications. is its intuitive approach to neural network training and implementation. environments. TensorFlow.js

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Unite.AI

JUNE 26, 2023

AI models are extremely good at solving narrow problems, such as image classification, natural language processing , speech recognition, etc., They lack subjective experience, self-consciousness, or an understanding of context beyond what they have been trained to process. but they don’t possess consciousness.

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IBM Journey to AI blog

DECEMBER 20, 2023

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. What is machine learning?

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Unite.AI

SEPTEMBER 21, 2023

Organizations and practitioners build AI models that are specialized algorithms to perform real-world tasks such as image classification, object detection, and natural language processing. Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models.

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AssemblyAI

MAY 3, 2023

With these fairly complex algorithms often being described as “giant black boxes” in news and media, a demand for clear and accessible resources is surging. This concept is not exclusive to natural language processing, and has also been employed in other domains.

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IBM Journey to AI blog

JANUARY 10, 2024

AI operates on three fundamental components: data, algorithms and computing power. Algorithms: Algorithms are the sets of rules AI systems use to process data and make decisions. The category of AI algorithms includes ML algorithms, which learn and make predictions and decisions without explicit programming.

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Marktechpost

NOVEMBER 27, 2024

At its core, machine learning algorithms seek to identify patterns within data, enabling computers to learn and adapt to new information. 2) Logistic regression Logistic regression is a classification algorithm used to model the probability of a binary outcome. Sigmoid Kernel: Inspired by neural networks.

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Unite.AI

DECEMBER 18, 2023

Transformers vs Mamba Transformers, like GPT-4, have set benchmarks in natural language processing. Here's where Mamba leaps ahead, with its ability to process long sequences more efficiently and its unique architecture that simplifies the entire process. However, their efficiency dips with longer sequences.

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Unite.AI

AUGUST 2, 2023

To tackle the issue of single modality, Meta AI released the data2vec, the first of a kind, self supervised high-performance algorithm to learn patterns information from three different modalities: image, text, and speech. Why Does the AI Industry Need the Data2Vec Algorithm?

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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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Unite.AI

AUGUST 5, 2024

However, as AI technology has progressed, so have robots' audio processing capabilities. Key advancements in this field include the development of sensitive microphones, sophisticated sound recognition algorithms, and the application of machine learning and neural networks.

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Marktechpost

DECEMBER 12, 2024

A Deep Neural Network (DNN) is an artificial neural network that features multiple layers of interconnected nodes, also known as neurons. Each neuron processes input data by applying weights, biases, and an activation function to generate an output. These layers include an input, multiple hidden, and output layers.

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Marktechpost

OCTOBER 7, 2024

Recurrent neural networks (RNNs) have been foundational in machine learning for addressing various sequence-based problems, including time series forecasting and natural language processing. Other methods, like linear attention models, optimize training by reducing the computation required for longer sequences.

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IBM Journey to AI blog

AUGUST 13, 2024

Where it all started During the second half of the 20 th century, IBM researchers used popular games such as checkers and backgammon to train some of the earliest neural networks, developing technologies that would become the basis for 21 st -century AI.

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Marktechpost

MAY 20, 2024

Deep learning is a subset of machine learning that involves training neural networks with multiple layers to recognize patterns and make data-based decisions. TensorFlow Developer Professional Certificate This course teaches how to build and train neural networks using TensorFlow through a hands-on program.

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Unite.AI

FEBRUARY 16, 2024

Despite achieving remarkable results in areas like computer vision and natural language processing , current AI systems are constrained by the quality and quantity of training data, predefined algorithms, and specific optimization objectives.

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Unite.AI

OCTOBER 22, 2024

This term refers to how much time, memory, or processing power an algorithm requires as the size of the input grows. AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands.

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Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

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AI Weekly

JULY 13, 2023

plos.org Screening for Chagas disease from the electrocardiogram using a deep neural network Worldwide, it is estimated that over 6 million people are infected with Chagas disease (ChD). We explore the use of deep neural networks to detect ChD from electrocardiograms (ECGs) to aid in the early detection of the disease.

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Marktechpost

JUNE 14, 2024

This article lists the top AI courses by Stanford that provide essential training in machine learning, deep learning, natural language processing, and other key AI technologies, making them invaluable for anyone looking to excel in the field. This beginner-friendly program, developed by DeepLearning.AI

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Marktechpost

MAY 5, 2024

Machine Learning with Python This course covers the fundamentals of machine learning algorithms and when to use each of them. The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow. and evaluating the same.

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Unite.AI

FEBRUARY 19, 2024

RPA Bots Becoming Super Bots: Driving Intelligent Decision Making RPA bots that originally operated on rule-based programs through learning patterns and emulating human behavior for performing repetitive and menial tasks have become super bots, with Conversational AI and Neural Network algorithms coming into force.

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Marktechpost

AUGUST 7, 2023

As the world of technology continues to evolve, Perfusion stands as a testament to the incredible possibilities at the intersection of natural language processing and image generation. Check out the Paper and Project Page.

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Marktechpost

MARCH 28, 2024

Python: Advanced Guide to Artificial Intelligence This book helps individuals familiarize themselves with the most popular machine learning (ML) algorithms and delves into the details of deep learning, covering topics like CNN, RNN, etc. The book prepares its readers for the moral uncertainties of a world run by code.

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Marktechpost

APRIL 12, 2024

Deep learning architectures have revolutionized the field of artificial intelligence, offering innovative solutions for complex problems across various domains, including computer vision, natural language processing, speech recognition, and generative models.

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Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

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IBM Journey to AI blog

OCTOBER 3, 2023

Using neural networks and advanced natural language processing, Waiver Grades give a personalized rating for the value a player would add to your team. This nuanced algorithm delves deep into your roster to make realistic projections based on your team’s fluctuating strengths and weaknesses.

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Marktechpost

DECEMBER 16, 2023

Trained on a dataset from six UK hospitals, the system utilizes neural networks, X-Raydar and X-Raydar-NLP, for classifying common chest X-ray findings from images and their free-text reports. An NLP algorithm, X-Raydar-NLP, was trained on 23,230 manually annotated reports to extract labels.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

Generative AI for coding is possible because of recent breakthroughs in large language model (LLM) technologies and natural language processing (NLP). It uses deep learning algorithms and large neural networks trained on vast datasets of diverse existing source code.

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Pickl AI

NOVEMBER 26, 2024

Summary: Recurrent Neural Networks (RNNs) are specialised neural networks designed for processing sequential data by maintaining memory of previous inputs. They excel in natural language processing, speech recognition, and time series forecasting applications.

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