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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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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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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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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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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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. A feedforward neural network comes next.

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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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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.

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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.

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Pickl AI

NOVEMBER 22, 2024

Summary: Long Short-Term Memory (LSTM) networks are a specialised form of Recurrent Neural Networks (RNNs) that excel in learning long-term dependencies in sequential data. Understanding Recurrent Neural Networks (RNNs) To appreciate LSTMs, it’s essential to understand RNNs.

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Analytics Vidhya

SEPTEMBER 2, 2021

This article was published as a part of the Data Science Blogathon Overview Sentence classification is one of the simplest NLP tasks that have a wide range of applications including document classification, spam filtering, and sentiment analysis. A sentence is classified into a class in sentence classification.

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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.

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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

JANUARY 29, 2025

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. Cat vs. Dog Classification This project involves building a Convolutional Neural Network (CNN) to classify images as either cats or dogs.

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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. Deep Learning for Robotics This course teaches applying machine learning to robotics.

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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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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.

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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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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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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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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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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. This innovative design uses Monarch matrices to achieve sub-quadratic scaling in neural networks, exhibiting the practical benefits of structured sparsity.

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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

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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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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Viso.ai

OCTOBER 9, 2024

Hence, deep neural network face recognition and visual Emotion AI analyze facial appearances in images and videos using computer vision technology to analyze an individual’s emotional status. With the rapid development of Convolutional Neural Networks (CNNs) , deep learning became the new method of choice for emotion analysis tasks.

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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.

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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

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.

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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. This will allow us to understand how SSMs operate within deep neural networks and why they hold promise for efficient sequence modeling. Time-scale adaptation, as in Neural Differential Equations (NDEs).

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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.

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Viso.ai

DECEMBER 10, 2023

The most popular machine learning method is deep learning, where multiple hidden layers of a neural network are used in a model. Neural networks need those training images from an acquired dataset to create perceptions of how certain classes look. CNNs are unmatched by traditional machine learning methods.

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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. Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e.

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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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ODSC - Open Data Science

FEBRUARY 13, 2023

How Deep Neural Networks Work and How We Put Them to Work at Facebook Deep learning is the technology driving today’s artificial intelligence boom. We will take a gentle, detailed tour through a multilayer fully-connected neural network, backpropagation, and a convolutional neural network.

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