AI Weekly

AUGUST 17, 2023

cmswire.com Why humans can't use NLP to speak with the animals We’ve already got machine-learning systems and natural language processors that can translate human speech into any number of existing languages, and adapting that process to convert animal calls into human-interpretable signals doesn’t seem that big of a stretch.

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Convolutional Neural Networks Robotics Artificial Intelligence Artificial Intelligence 245

Unite.AI

AUGUST 1, 2023

Prompt 1 : “Tell me about Convolutional Neural Networks.” ” Response 1 : “Convolutional Neural Networks (CNNs) are multi-layer perceptron networks that consist of fully connected layers and pooling layers. They are commonly used in image recognition tasks. .”

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Marktechpost

JANUARY 30, 2025

Natural Language Processing (NLP): Text data and voice inputs are transformed into tokens using tools like spaCy. Tokenization and Word Embeddings: In NLP, tokenization divides text into meaningful units (words, subwords). Preprocessing images might involve resizing, color normalization, or filtering out noise.

Robotics 113

Robotics Convolutional Neural Networks Large Language Models AI 113

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.

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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

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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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ODSC - Open Data Science

NOVEMBER 28, 2024

Calculating Receptive Field for Convolutional Neural Networks Convolutional neural networks (CNNs) differ from conventional, fully connected neural networks (FCNNs) because they process information in distinct ways. Receptive fields are the backbone of CNN efficacy.

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Pickl AI

NOVEMBER 22, 2024

Some notable applications include: Natural Language Processing (NLP) NLP is used for tasks such as sentiment analysis, machine translation, text summarization, and question answering systems. Applications of LSTM LSTMs have become a cornerstone in various fields due to their effectiveness in handling sequential data.

Neural Network 52

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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

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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

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Towards AI

MARCH 3, 2025

Early foundations of NLP were established by statistical and rule-based models like the Bag of Words (BoW). In this article, we will discuss what BoW is and how Transformers revolutionized the field of NLP over time. It is one of the widely used technique in NLP despite its simplicity.

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Neural Network Convolutional Neural Networks Natural Language Processing NLP 54

Unite.AI

AUGUST 2, 2023

To overcome the challenge presented by single modality models & algorithms, Meta AI released the data2vec, an algorithm that uses the same learning methodology for either computer vision , NLP or speech. For example, there are vocabulary of speech units in speech processing that can define a self-supervised learning task in NLP.

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Computer Vision Natural Language Processing Algorithm Convolutional Neural Networks 246

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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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). The methodology behind Mini-Gemini involves a dual-encoder system that includes a convolutional neural network for refined image processing, enhancing visual tokens without increasing their number.

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Artificial Intelligence Artificial Intelligence Convolutional Neural Networks Computer Vision 138

Unite.AI

SEPTEMBER 13, 2023

LLMs or Large Language Models have enjoyed tremendous success in the NLP industry, and they are now being explored for their applications in visual tasks. The prompt learner consists of learnable base prompt embeddings, and a convolutional neural network. Moving ahead, we have Large Vision Language Models or LVLMs.

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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. The voice assistants that 62% of U.S.

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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.

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Pickl AI

JANUARY 29, 2025

Cat vs. Dog Classification This project involves building a Convolutional Neural Network (CNN) to classify images as either cats or dogs. This project helps you understand how to process visual data while generating textual descriptions, bridging the gap between computer vision and natural language processing (NLP).

Deep Learning 52

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Marktechpost

MARCH 26, 2024

Observations indicate diminishing returns with increased model depth, mirroring challenges in deep convolutional neural networks for computer vision. Solutions like DenseNets, facilitating direct access to earlier layer outputs, have emerged to tackle this issue, reflecting parallels between NLP and computer vision advancements.

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Marktechpost

MAY 28, 2024

The Generative Pre-trained Transformer (GPT) series, developed by OpenAI, has revolutionized the field of NLP with its groundbreaking advancements in language generation and understanding. It achieved impressive results on various NLP tasks, such as text summarization, translation, and question answering. Model Size: 1.5

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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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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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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

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. It’s all about context!

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Marktechpost

JULY 31, 2024

Intro to TensorFlow for Deep Learning This course provides a hands-on introduction to deep learning with TensorFlow and Keras, covering neural networks, CNNs, transfer learning, and time series forecasting. It also delves into NLP with tokenization, embeddings, and RNNs and concludes with deploying models using TensorFlow Lite.

Neural Network 106

Neural Network Auto-classification Natural Language Processing Deep Learning 106

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.

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Convolutional Neural Networks Neural Network Computer Vision Natural Language Processing 116

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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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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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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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. Around 2020, their ability to efficiently handle long sequences spurred significant progress in adapting them for natural language processing (NLP).

Neural Network 59

Neural Network Convolutional Neural Networks Natural Language Processing NLP 59

IBM Journey to AI blog

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data. What is text mining?

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Marktechpost

SEPTEMBER 17, 2024

Although optimizers like Adam perform parameter updates iteratively to minimize errors gradually, the sheer size of models, especially in tasks like natural language processing (NLP) and computer vision, leads to long training cycles. reduction in training time.

Neural Network 111

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 111

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. Put simply, if we double the input size, the computational needs can increase fourfold. Initially, many AI algorithms operated within manageable complexity limits.

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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).

Computer Vision 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.

Convolutional Neural Networks 98

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ODSC - Open Data Science

FEBRUARY 13, 2023

We will take a gentle, detailed tour through a multilayer fully-connected neural network, backpropagation, and a convolutional neural network. At Facebook, we use deep neural networks as part of our effort to connect the entire world. To get the best results, it’s helpful to understand how they work.

Neural Network 98

Neural Network Convolutional Neural Networks Machine Learning Deep Learning 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). Using the coordinates of N cities (nodes, vertices, tokens), TSP determines the shortest Hamiltonian cycle that passes through each node.

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