Viso.ai

MAY 17, 2024

Neural Networks have changed the way we perform model training. Neural networks, sometimes referred to as Neural Nets, need large datasets for efficient training. So, what if we have a neural network that can adapt itself to new data and has less complexity? What is a Liquid Neural Network?

Neural Network 52

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

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Analytics Vidhya

MAY 17, 2024

Introduction In natural language processing (NLP), sequence-to-sequence (seq2seq) models have emerged as a powerful and versatile neural network architecture.

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Unite.AI

NOVEMBER 8, 2023

Natural Language Processing (NLP) has experienced some of the most impactful breakthroughs in recent years, primarily due to the the transformer architecture. The introduction of word embeddings, most notably Word2Vec, was a pivotal moment in NLP. One-hot encoding is a prime example of this limitation. in 2017.

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Marktechpost

JANUARY 23, 2024

There are two major challenges in visual representation learning: the computational inefficiency of Vision Transformers (ViTs) and the limited capacity of Convolutional Neural Networks (CNNs) to capture global contextual information. A team of researchers at UCAS, in collaboration with Huawei Inc.

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Unite.AI

MARCH 24, 2024

Bridging the Gap with Natural Language Processing Natural Language Processing (NLP) stands at the forefront of bridging the gap between human language and AI comprehension. NLP enables machines to understand, interpret, and respond to human language in a meaningful way.

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Analytics Vidhya

MAY 28, 2019

The post Decoding the Best Papers from ICLR 2019 – Neural Networks are Here to Rule appeared first on Analytics Vidhya. Introduction I love reading and decoding machine learning research papers. There is so much incredible information to parse through – a goldmine for us.

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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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Analytics Vidhya

JANUARY 17, 2022

The post Roadmap to Master NLP in 2022 appeared first on Analytics Vidhya. 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?” ” Then I began to write a brief response.

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Mlearning.ai

MAY 30, 2023

Recurrent Neural Networks (RNNs) have become a potent tool for analysing sequential data in the large subject of artificial intelligence and machine learning. As we know that Convolutional Neural Network (CNN) is used for structured arrays of data such as image data. RNN is used for sequential data.

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Neural Network Convolutional Neural Networks Natural Language Processing Data Analysis 52

Mlearning.ai

JULY 4, 2023

Recurrent Neural Networks (RNNs) have gained a lot of popularity in the field of Natural Language Processing (NLP) because of their… Continue reading on MLearning.ai »

Neural Network 52

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Bugra Akyildiz

APRIL 6, 2024

This makes it difficult to use traditional techniques like natural language processing (NLP) to understand the content. By combining the strengths of BNN(Bayesian Neural Network)s and GP(Gaussian Process)s with compositional kernels, AutoBNN opens a world of possibilities for understanding and forecasting complex data.

Neural Network 52

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Analytics Vidhya

JULY 22, 2022

He is a Data Scientist @Mckinsey & Company with over 5 years of experience, primarily working on NLP and related problems. The post The DataHour: Bias and Fairness in NLP appeared first on Analytics Vidhya.

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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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Marktechpost

JUNE 27, 2023

Activation functions for neural networks are an essential part of deep learning since they decide the accuracy and efficiency of the training model used to create or split a large-scale neural network and the output of deep learning models. An artificial neural network contains a large number of linked individual neurons.

Neural Network 52

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Marktechpost

APRIL 6, 2024

Transformers have transformed the field of NLP over the last few years, with LLMs like OpenAI’s GPT series, BERT, and Claude Series, etc. Let’s delve into the role of transformers in NLP and elucidate the process of training LLMs using this innovative architecture. appeared first on MarkTechPost.

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Towards AI

JULY 20, 2023

The Lookout — “All’s Well” | Homer NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER The NLP Cypher | 03.07.21 Oh and by the way, Maybe… the universe is just a giant neural network… ?‍♂️ ‍♂️ The Universe Might Be One Big Neural Network, Study Finds One scientist says the universe is a giant neural net.

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Marktechpost

DECEMBER 19, 2023

Recurrent Neural Networks (RNNs) and Transformers are the most common methods; each has advantages and disadvantages. By comparing the suggested architecture to SoTA, the researchers find that it performs similarly while being more cost-effective across a range of natural language processing (NLP) workloads.

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Mlearning.ai

APRIL 21, 2023

Natural Language Processing Transformers, the neural network architecture, that has taken the world of natural language processing (NLP) by storm, is a class of models that can be used for both language and image processing. One of the earliest representation models used in NLP was the Bag of Words (BoW) model.

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Marktechpost

JANUARY 15, 2024

Transformers have taken over from recurrent neural networks (RNNs) as the preferred architecture for natural language processing (NLP). Transformers stand out conceptually because they directly access each token in a sequence, unlike RNNs that rely on maintaining a recurring state of past inputs.

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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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Towards AI

JULY 21, 2023

Ivan Aivazovsky — Istanbul NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER NLP News Cypher | 09.06.20 nlp("Transformers and onnx runtime is an awesome combo!") ") GitHub: patil-suraj/onnx_transformers Accelerated NLP pipelines for fast inference ? Revival Welcome back, and what a week?!?!

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Marktechpost

FEBRUARY 27, 2024

This advancement not only sets a new precedent for the input sequence size a neural network can process but also paves the way for models to delve into more intricate and realistic scenarios. In conclusion, exploring and implementing recurrent memory augmentations for transformer models signify a pivotal development in NLP.

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Marktechpost

NOVEMBER 3, 2023

The speech audio language music open neural network that is proposed in this study is a single audio-text multimodal LLM that can recognize and comprehend speech, audio events, and music—the three main categories of sounds. These tasks need multilingual and high-quality alignments between voice and text tokens.

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ODSC - Open Data Science

SEPTEMBER 18, 2023

Getting started with natural language processing (NLP) is no exception, as you need to be savvy in machine learning, deep learning, language, and more. To get you started on your journey, we’ve released a new on-demand Introduction to NLP course. Here are some more details.

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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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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 109

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Towards AI

JULY 19, 2023

John on Patmos | Correggio NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER The NLP Cypher | 02.14.21 github.com Their core repos consist of SparseML: a toolkit that includes APIs, CLIs, scripts and libraries that apply optimization algorithms such as pruning and quantization to any neural network.

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Unite.AI

MAY 29, 2024

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. Named Entity Recognition ( NER) Named entity recognition (NER), an NLP technique, identifies and categorizes key information in text.

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John Snow Labs

APRIL 30, 2024

In this article, we will explore the significance of table extraction and demonstrate the application of John Snow Labs’ NLP library with visual features installed for this purpose. We will delve into the key components within the John Snow Labs NLP pipeline that facilitate table extraction. How does Visual NLP come into action?

Deep Learning 52

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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.

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Heartbeat

FEBRUARY 7, 2023

image by rawpixel.com Understanding the concept of language models in natural language processing (NLP) is very important to anyone working in the Deep learning and machine learning space. They are essential to a variety of NLP activities, including speech recognition, machine translation, and text summarization.

NLP 52

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Analytics Vidhya

MAY 29, 2023

A recurrent neural network is a class of artificial neural networks where connections between nodes can create a cycle, allowing output from some nodes to affect subsequent input to the same nodes.

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Towards AI

JULY 19, 2023

Ivan Aivazovsky — Istanbul NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER NLP News Cypher | 09.06.20 nlp("Transformers and onnx runtime is an awesome combo!") ") GitHub: patil-suraj/onnx_transformers Accelerated NLP pipelines for fast inference ? Revival Welcome back, and what a week?!?!

NLP 40

NLP Neural Network BERT Natural Language Processing 40

ODSC - Open Data Science

APRIL 11, 2023

A lot goes into NLP. Going beyond NLP platforms and skills alone, having expertise in novel processes, and staying afoot in the latest research are becoming pivotal for effective NLP implementation. We have seen these techniques advancing multiple fields in AI such as NLP, Computer Vision, and Robotics.

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Heartbeat

JUNE 6, 2023

Photo by GuerrillaBuzz on Unsplash Graph Convolutional Networks (GCNs) are a type of neural network that operates on graphs, which are mathematical structures consisting of nodes and edges. GCNs have been successfully applied to many domains, including computer vision and social network analysis.

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Heartbeat

NOVEMBER 21, 2023

One of the most significant breakthroughs in this field is the convolutional neural network (CNN). In stark contrast, deep learning algorithms take a radically different approach, particularly convolutional neural networks (CNNs).

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