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.

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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.

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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.

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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.

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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.

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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?

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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).

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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.

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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).

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Pickl AI

NOVEMBER 22, 2024

This leads to the vanishing gradient problem, making it difficult for RNNs to retain information from earlier time steps when processing long sequences. LSTMs are crucial for natural language processing tasks. Key Takeaways LSTMs address the vanishing gradient problem in RNNs. In What Applications Are LSTMS Commonly Used?

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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).

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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.

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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).

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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

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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.

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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. So, let’s get started! What are Graphs?

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Marktechpost

NOVEMBER 27, 2024

Recurrent Neural Networks (RNNs): Designed to process sequential data, such as time series or natural language. Convolutional Neural Networks (CNNs): Specialized for image and video analysis.

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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.

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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.,

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Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. Transformer models have become the de-facto status quo in Natural Language Processing (NLP).

Convolutional Neural Networks 52

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Pickl AI

AUGUST 14, 2024

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. Models such as Long Short-Term Memory (LSTM) networks and Transformers (e.g.,

Neural Network 52

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Pickl AI

SEPTEMBER 16, 2024

Summary : Deep Belief Networks (DBNs) are Deep Learning models that use Restricted Boltzmann Machines and feedforward networks to learn hierarchical features and model complex data distributions. They are effective in image recognition, NLP, and speech recognition. They can also model semantic structures and syntactic patterns.

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Explosion

AUGUST 22, 2017

Catastrophic forgetting problems have become more relevant for spaCy users lately, because spaCy v2’s part-of-speech, named entity, syntactic dependency and sentence segmentation models all share an input representation, produced by a convolutional neural network.

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Snorkel AI

MARCH 1, 2023

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al.

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Viso.ai

APRIL 26, 2024

Thus, these systems are grounded in traditional object detection and natural language processing frameworks. Object detection systems typically use frameworks like Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs).

Convolutional Neural Networks 52

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John Snow Labs

JUNE 2, 2023

Spark NLP’s deep learning models have achieved state-of-the-art results on sentiment analysis tasks, thanks to their ability to automatically learn features and representations from raw text data. Spark NLP has multiple approaches for detecting the sentiment (which is actually a text classification problem) in a text.

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Heartbeat

MARCH 8, 2023

Heartbeat these past few weeks has had lots of great articles covering the latest research, NLP use-cases, and Comet tutorials. Happy Reading, Emilie, Abby & the Heartbeat Team Natural Language Processing With SpaCy (A Python Library) — by Khushboo Kumari This post goes over how the most cutting-edge NLP software, SpaCy , operates.

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Pickl AI

NOVEMBER 20, 2024

Example In Natural Language Processing (NLP), word embeddings are often represented as vectors. Real-World Examples This section unfolds the real-world examples of Linear Algebra in action within Machine Learning, highlighting applications in image recognition, natural language processing, and recommendation systems.

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