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.

Deep Learning 137

Deep Learning Convolutional Neural Networks Neural Network Natural Language Processing 137

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.

Deep Learning 52

Deep Learning Neural Network Natural Language Processing Computer Vision 52

AssemblyAI

JANUARY 23, 2025

Unlike many natural language processing (NLP) models, which were historically dominated by recurrent neural networks (RNNs) and, more recently, transformers, wav2letter is designed entirely using convolutional neural networks (CNNs). What sets wav2letter apart is its unique architecture.

Python 130

Python Convolutional Neural Networks Neural Network OpenAI 130

Towards AI

MARCH 3, 2025

These questions are addressed by the field of Natural Language processing, which allows machines to mimic human comprehension and usage of natural language. Early foundations of NLP were established by statistical and rule-based models like the Bag of Words (BoW).

Neural Network 54

Neural Network Convolutional Neural Networks Natural Language Processing NLP 54

Pickl AI

JANUARY 26, 2025

Summary: Deep Learning models revolutionise data processing, solving complex image recognition, NLP, and analytics tasks. These models mimic the human brain’s neural networks, making them highly effective for image recognition, natural language processing, and predictive analytics.

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

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

Marktechpost

JUNE 3, 2024

This article lists top Intel AI courses, including those on deep learning, NLP, time-series analysis, anomaly detection, robotics, and edge AI deployment, providing a comprehensive learning path for leveraging Intel’s AI technologies. It also explores CNNs, TFRecord, and transfer learning.

Convolutional Neural Networks 121

Convolutional Neural Networks Neural Network Deep Learning Computer Vision 121

ODSC - Open Data Science

NOVEMBER 28, 2024

NVIDIA’s AI Tools Suite to Aid in Accelerated Humanoid Robotics Development NVIDIA’s AI tools suite may drive developers toward complex machine learning and natural language processing solutions. Discover how benchmarks, evaluation practices, and common tasks reveal progress and pitfalls in language model research.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network LLM Natural Language Processing 52

ODSC - Open Data Science

MARCH 10, 2025

By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow. Sessions on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) started gaining popularity, marking the beginning of data sciences shift toward AI-driven methods.

Data Science 52

Data Science Neural Network Convolutional Neural Networks Large Language Models 52

Marktechpost

MARCH 30, 2024

Vision Language Models (VLMs) emerge as a result of a unique integration of Computer Vision (CV) and Natural Language Processing (NLP). These innovations enable Mini-Gemini to process high-resolution images effectively and generate context-rich visual and textual content, setting it apart from existing models.

Artificial Intelligence 138

Artificial Intelligence Artificial Intelligence Convolutional Neural Networks Computer Vision 138

Marktechpost

MARCH 26, 2024

The transformer architecture has improved natural language processing, with recent advancements achieved through scaling efforts from millions to billion-parameter models. Observations indicate diminishing returns with increased model depth, mirroring challenges in deep convolutional neural networks for computer vision.

Convolutional Neural Networks 130

Convolutional Neural Networks Natural Language Processing Computer Vision Neural Network 130

ODSC - Open Data Science

FEBRUARY 10, 2025

adults use only work when they can turn audio data into words, and then apply natural language processing (NLP) to understand it. Mono sound channels are the best option for speech intelligibility , so theyre ideal for NLP applications, but stereo inputs will improve copyright detection use cases.

Machine Learning 52

Machine Learning Convolutional Neural Networks Neural Network Natural Language Processing 52

Marktechpost

JULY 31, 2024

Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics. It also delves into NLP with tokenization, embeddings, and RNNs and concludes with deploying models using TensorFlow Lite.

Neural Network 107

Neural Network Auto-classification Natural Language Processing Deep Learning 107

Artificial Corner

JANUARY 23, 2025

Subscribe now #3 Natural Language Processing Course in Python This is a short yet useful 2-hour NLP course for anyone interested in the field of Natural Language Processing. NLP is a branch of artificial intelligence that allows machines to understand human language.

Data Science 95

Data Science Python Convolutional Neural Networks Natural Language Processing 95

Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. Image captioning integrates computer vision, which interprets visual information, and NLP, which produces human language.

Natural Language Processing 52

Natural Language Processing Computer Vision NLP Algorithm 52

Marktechpost

NOVEMBER 10, 2023

Traditionally, Convolutional Neural Networks (CNNs) have been the go-to models for processing image data, leveraging their ability to extract meaningful features and classify visual information.

Convolutional Neural Networks 108

Convolutional Neural Networks Computer Vision Natural Language Processing Neural Network 108

Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

Neural Network 156

Neural Network Convolutional Neural Networks BERT Deep Learning 156

Unite.AI

APRIL 26, 2024

The advancements in large language models have significantly accelerated the development of natural language processing , or NLP. These extend far beyond the traditional text-based processing of LLMs to include multimodal interactions.

Large Language Models 162

Large Language Models Natural Language Processing Convolutional Neural Networks Neural Network 162

Unite.AI

OCTOBER 22, 2024

AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands. Computer vision tasks rely heavily on matrix operations and have also used sub-quadratic techniques to streamline convolutional processes.

Neural Network 147

Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 147

The MLOps Blog

MARCH 6, 2025

While transformer-based models are in the limelight of the NLP community, a quiet revolution in sequence modeling is underway. State space models for natural language processing State Space Models (SSMs), long established in time series analysis, have been utilized as trainable sequence models for decades.

Neural Network 59

Neural Network Convolutional Neural Networks Natural Language Processing NLP 59

Unite.AI

MARCH 26, 2024

The development of Large Language Models (LLMs) built from decoder-only transformer models has played a crucial role in transforming the Natural Language Processing (NLP) domain, as well as advancing diverse deep learning applications including reinforcement learning , time-series analysis, image processing, and much more.

Natural Language Processing 147

Natural Language Processing Neural Network Convolutional Neural Networks Large Language Models 147

Towards AI

JANUARY 14, 2025

Transformers have revolutionized natural language processing (NLP), powering models like GPT and BERT. The goal was to see if I could accurately identify these digits using a Transformer-based approach, which feels quite different from the traditional Convolutional Neural Network (CNN) methods I was more familiar with.

Convolutional Neural Networks 116

Convolutional Neural Networks Neural Network Computer Vision Natural Language Processing 116

ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! If a Natural Language Processing (NLP) system does not have that context, we’d expect it not to get the joke. I’ll be making use of the powerful SpaCy library which makes swapping architectures in NLP pipelines a breeze.

Deep Learning 98

Deep Learning Convolutional Neural Networks NLP Neural Network 98

Heartbeat

JUNE 6, 2023

In recent years, researchers have also explored using GCNs for natural language processing (NLP) tasks, such as text classification , sentiment analysis , and entity recognition. GCNs use a combination of graph-based representations and convolutional neural networks to analyze large amounts of textual data.

NLP 59

NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

ODSC - Open Data Science

JULY 18, 2023

Here at ODSC, we couldn’t be more excited to announce Microsoft Azure’s tutorial series on Deep Learning and NLP, now available for free on Ai+. This course series was created by a team of experts from the Microsoft community, who have brought their knowledge and experience in AI and deep learning to create an insightful learning experience.

Deep Learning 52

Deep Learning Convolutional Neural Networks NLP Neural Network 52

Marktechpost

SEPTEMBER 17, 2024

One of the central challenges in this field is the extended time needed to train complex neural networks. In one experiment on a language task, the baseline Adam optimizer required 23,500 steps to reach the target perplexity, while NINO achieved the same performance in just 11,500 steps. reduction in training time.

Neural Network 112

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 112

IBM Journey to AI blog

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. Machine learning algorithms like Naïve Bayes and support vector machines (SVM), and deep learning models like convolutional neural networks (CNN) are frequently used for text classification.

Data Mining 170

Data Mining Convolutional Neural Networks Categorization Machine Learning 170

John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. A VQA system takes free-form, text-based questions about an input image and presents answers in a natural language format.

Natural Language Processing 40

Natural Language Processing Deep Learning NLP Convolutional Neural Networks 40

Viso.ai

OCTOBER 9, 2024

With the rapid development of Convolutional Neural Networks (CNNs) , deep learning became the new method of choice for emotion analysis tasks. Generally, the classifiers used for AI emotion recognition are based on Support Vector Machines (SVM) or Convolutional Neural Networks (CNN).

Convolutional Neural Networks 98

Convolutional Neural Networks Neural Network Computer Vision Emotion AI 98

Marktechpost

JUNE 3, 2024

Numerous groundbreaking models—including ChatGPT, Bard, LLaMa, AlphaFold2, and Dall-E 2—have surfaced in different domains since the Transformer’s inception in Natural Language Processing (NLP).

Convolutional Neural Networks 102

Convolutional Neural Networks Neural Network Large Language Models Algorithm 102

Heartbeat

NOVEMBER 21, 2023

From object detection and recognition to natural language processing, deep reinforcement learning, and generative models, we will explore how deep learning algorithms have conquered one computer vision challenge after another. One of the most significant breakthroughs in this field is the convolutional neural network (CNN).

Computer Vision 52

Computer Vision Deep Learning NLP Neural Network 52

Topbots

OCTOBER 4, 2023

Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g., spam vs. not spam), while generative NLP models can create new text based on a given prompt (e.g., a social media post or product description).

Generative AI 119

Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 119

Viso.ai

DECEMBER 10, 2023

Training of Neural Networks for Image Recognition The images from the created dataset are fed into a neural network algorithm. The training of an image recognition algorithm makes it possible for convolutional neural network image recognition to identify specific classes.

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 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. So, let’s get started! What are Graphs?

Neural Network 87

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

Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). 2020 ) and language modelling ( Khandelwal et al., In NLP, Gunel et al. 2020 ; Lewis et al.,

NLP 52

NLP ML Computer Vision Natural Language Processing 52

Explosion

NOVEMBER 9, 2016

Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. Most NLP problems can be reduced to machine learning problems that take one or more texts as input. This has always been a huge weakness of NLP models. Now we have a solution.

Deep Learning 52

Deep Learning NLP Convolutional Neural Networks Neural Network 52

Marktechpost

DECEMBER 12, 2024

The deep aspect of DNNs comes from multiple hidden layers, which allow the network to learn and model complex patterns and relationships in data. DNNs are the backbone of many advanced artificial intelligence applications, including image recognition, natural language processing, and autonomous systems.

Neural Network 57

Neural Network Convolutional Neural Networks Natural Language Processing Artificial Intelligence 57