Unite.AI

JANUARY 18, 2025

While Central Processing Units (CPUs) and Graphics Processing Units (GPUs) have historically powered traditional computing tasks and graphics rendering, they were not originally designed to tackle the computational intensity of deep neural networks.

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Marktechpost

MAY 14, 2024

This article lists the top Deep Learning and Neural Networks books to help individuals gain proficiency in this vital field and contribute to its ongoing advancements and applications. Neural Networks and Deep Learning The book explores both classical and modern deep learning models, focusing on their theory and algorithms.

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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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Marktechpost

DECEMBER 10, 2024

Artificial Neural Networks (ANNs) have become one of the most transformative technologies in the field of artificial intelligence (AI). Artificial Neural Networks are computational systems inspired by the human brain’s structure and functionality. How Do Artificial Neural Networks Work?

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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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Towards AI

NOVEMBER 8, 2024

Automating Words: How GRUs Power the Future of Text Generation Isn’t it incredible how far language technology has come? Natural Language Processing, or NLP, used to be about just getting computers to follow basic commands. Author(s): Tejashree_Ganesan Originally published on Towards AI.

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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

JANUARY 22, 2025

The system works by actively listening during patient encounters, processing conversations through advanced AI algorithms to generate accurate medical notes as the visit unfolds. The system's intelligence stems from its neural network-based Concept Processor, which observes and learns from every interaction.

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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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Towards AI

FEBRUARY 3, 2025

DeepSeek AI is an advanced AI genomics platform that allows experts to solve complex problems using cutting-edge deep learning, neural networks, and natural language processing (NLP). Adaptive Learning Process: DeepSeek AI works in real-time with feedback loops, and it is not set once and done, like a model.

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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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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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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

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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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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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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Pickl AI

DECEMBER 29, 2024

Introduction Mathematics forms the backbone of Artificial Intelligence , driving its algorithms and enabling systems to learn and adapt. Core areas like linear algebra, calculus, and probability empower AI models to process data, optimise solutions, and make accurate predictions.

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Pickl AI

JANUARY 29, 2025

Whether you’re interested in image recognition, natural language processing, or even creating a dating app algorithm, theres a project here for everyone. Natural Language Processing: Powers applications such as language translation, sentiment analysis, and chatbots.

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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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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 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

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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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 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

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.

Machine Learning 291

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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

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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AI News

OCTOBER 15, 2024

By leveraging vast amounts of data and powerful algorithms, ML enables companies to automate processes, make accurate predictions, and uncover hidden patterns to optimise performance. Unsupervised machine learning systems use artificial neural networks to continue interacting with customers and retain existing customers.

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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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Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

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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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

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

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

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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