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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AI News

JULY 22, 2024

This article delves into how AI algorithms are transforming sports betting, providing actual data, statistics, and insights that demonstrate their impact. AI algorithms can analyse vast amounts of data, recognise patterns, and make predictions with remarkable accuracy. Data collection and processing AI algorithms thrive on data.

Machine Learning 297

Machine Learning Algorithm Neural Network Natural Language Processing 297

Towards AI

JANUARY 29, 2025

Generative AI is powered by advanced machine learning techniques, particularly deep learning and neural networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). These are essential for understanding machine learning algorithms. Study neural networks, including CNNs, RNNs, and LSTMs.

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AI Engineer Generative AI Neural Network BERT 98

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

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.

Automation 240

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Unite.AI

AUGUST 5, 2024

Key advancements in this field include the development of sensitive microphones, sophisticated sound recognition algorithms, and the application of machine learning and neural networks. Key features and capabilities of these robots include Natural Language Processing (NLP) , speech recognition, and audio synthesis.

Robotics 272

Robotics AI Developer AI Development NLP 272

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). What is DeepSeek AI? DeepSeek AI, on the other hand, isnt just another fancy AI gadget, its a revolutionary breakthrough.

Natural Language Processing 125

Natural Language Processing OpenAI Neural Network AI 125

Towards AI

NOVEMBER 8, 2024

Natural Language Processing, or NLP, used to be about just getting computers to follow basic commands. Text generation is said to be the branch of natural language processing (NLP) and it is primarily focused on creating coherent and contextually relevant texts automatically.

Automation 122

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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.

Neural Network 96

Neural Network Auto-classification NLP Natural Language Processing 96

Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

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?

Neural Network 87

Neural Network Auto-classification Convolutional Neural Networks Auto-complete 87

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.

Machine Learning 111

Machine Learning Algorithm Neural Network Convolutional Neural Networks 111

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

Machine Learning Neural Network Robotics Natural Language Processing 291

ODSC - Open Data Science

FEBRUARY 17, 2023

Natural language processing (NLP) has been growing in awareness over the last few years, and with the popularity of ChatGPT and GPT-3 in 2022, NLP is now on the top of peoples’ minds when it comes to AI. The chart below shows 20 in-demand skills that encompass both NLP fundamentals and broader data science expertise.

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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?

Computer Vision 246

Computer Vision Natural Language Processing Algorithm Convolutional Neural Networks 246

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. Artificial neural networks consist of interconnected layers of nodes, or “neurons” which work together to process and learn from data.

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Large Language Models Neural Network LLM ChatGPT 363

Marek Rei

NOVEMBER 4, 2024

The prompts are updated by randomly sampling permutations to the existing prompts, then approximating the gradient using the natural evolution strategy (NAS) algorithm. Applying NLP systems to analyse thousands of company reports and the sustainability initiatives described in those reports. Nature Communications 2024.

Machine Learning 80

Machine Learning NLP Large Language Models LLM 80

IBM Journey to AI blog

FEBRUARY 14, 2024

Voice-based queries use natural language processing (NLP) and sentiment analysis for speech recognition so their conversations can begin immediately. With text to speech and NLP, AI can respond immediately to texted queries and instructions. YouTube will deliver a curated feed of content suited to customer interests.

Computer Vision 247

Computer Vision NLP Robotics Automation 247

Unite.AI

SEPTEMBER 18, 2023

NLP in particular has been a subfield that has been focussed heavily in the past few years that has resulted in the development of some top-notch LLMs like GPT and BERT. The neural network consists of three types of layers including the hidden layer, the input payer, and the output layer.

Deep Learning 321

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Towards AI

OCTOBER 18, 2024

For training machine learning algorithms with datasets, Machines only understand numbers. The main theme is that it can contain semantic and meaningful contextual information about the objects so that ML algorithms can efficiently analyze and understand the data. What is Vector embedding and why do we need this?How

NLP 111

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Unite.AI

JULY 24, 2024

Over the past decade, advancements in machine learning, Natural Language Processing (NLP), and neural networks have transformed the field. Core ML brought powerful machine learning algorithms to the iOS platform, enabling apps to perform tasks such as image recognition, NLP, and predictive analytics.

Machine Learning 223

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Marktechpost

DECEMBER 13, 2024

Neural networks have become foundational tools in computer vision, NLP, and many other fields, offering capabilities to model and predict complex patterns. This understanding is essential for designing more efficient training algorithms and enhancing the interpretability and robustness of neural networks.

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Neural Network Machine Learning Computer Vision AI 77

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

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.

Deep Learning 126

Deep Learning Neural Network Computer Vision Natural Language Processing 126

Marktechpost

JANUARY 30, 2025

This paradigm shift is particularly visible in applications such as: Autonomous Vehicles Self-driving cars and drones rely on perception modules (sensors, cameras) fused with advanced algorithms to operate in dynamic traffic and weather conditions. Preprocessing images might involve resizing, color normalization, or filtering out noise.

Robotics 113

Robotics Convolutional Neural Networks Large Language Models AI 113

Unite.AI

DECEMBER 1, 2023

Machine learning , a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). The culmination of this training is a machine-learning model.

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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. Deep Learning is a specialized subset of Artificial Intelligence (AI) and machine learning that employs multilayered artificial neural networks to analyze and interpret complex data.

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Unite.AI

AUGUST 1, 2023

OpenAI has been instrumental in developing revolutionary tools like the OpenAI Gym, designed for training reinforcement algorithms, and GPT-n models. Prompt 1 : “Tell me about Convolutional Neural Networks.” Unlike other neural networks, they leverage convolutional layers and pooling layers to process images.

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Marktechpost

APRIL 12, 2024

This article explores some of the most influential deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformers, and Encoder-Decoder architectures, highlighting their unique features, applications, and how they compare against each other.

Deep Learning 136

Deep Learning Convolutional Neural Networks Neural Network Natural Language Processing 136

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. The deep aspect of DNNs comes from multiple hidden layers, which allow the network to learn and model complex patterns and relationships in data.

Neural Network 56

Neural Network Convolutional Neural Networks Natural Language Processing Artificial Intelligence 56

Pickl AI

NOVEMBER 13, 2024

Summary: Backpropagation in neural network optimises models by adjusting weights to reduce errors. Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems.

Neural Network 52

Neural Network Algorithm Convolutional Neural Networks Natural Language Processing 52

AssemblyAI

JANUARY 23, 2025

It works by analyzing audio signals, identifying patterns, and matching them to words and phrases using advanced algorithms. The primary drawbacks of cloud-based solutions are their cost and the lack of control over the underlying infrastructure and algorithms, as they are managed by the service provider.

Python 130

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Towards AI

JULY 20, 2023

It’s also an area that stands to benefit most from automated or semi-automated machine learning (ML) and natural language processing (NLP) techniques. As the capabilities of high-powered computers and ML algorithms have grown, so have opportunities to improve the SLR process. This study by Bui et al.

Data Extraction 98

Data Extraction NLP Natural Language Processing Automation 98

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.

Neural Network 147

Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 147

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. Introduction Neural networks have revolutionised data processing by mimicking the human brain’s ability to recognise patterns.

Neural Network 52

Neural Network Natural Language Processing NLP Deep Learning 52

Unite.AI

DECEMBER 11, 2023

Power of Multimodality: At its core, Gemini utilizes a transformer-based architecture, similar to those employed in successful NLP models like GPT-3. The development of Gemini involved innovations in training algorithms and infrastructure, using Google’s latest TPUs.

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Marktechpost

MARCH 30, 2024

It gives access to various classification, regression, and clustering algorithms, including SVM, random forests, gradient boosting, k-means, and DBSCAN. This library is built upon the foundation provided by NumPy and SciPy, and it offers a set of supervised and unsupervised learning algorithms through a consistent interface.

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