Analytics Vidhya

JANUARY 17, 2022

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

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Analytics Vidhya

SEPTEMBER 10, 2022

Introduction Deep learning is one of the hottest fields in the past decade, with applications in industry and research. However, even though it’s easy to delve into the topic, many people are confused by the terminology and end up only implementing neural network […].

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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.

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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. It was introduced in the paper “Attention is All You Need” by Vaswani et al.

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Analytics Vidhya

JULY 22, 2024

Introduction In this article, we dive into the top 10 publications that have transformed artificial intelligence and machine learning. We’ll take you through a thorough examination of recent advancements in neural networks and algorithms, shedding light on the key ideas behind modern AI.

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Marktechpost

MARCH 30, 2024

In 2024, the landscape of Python libraries for machine learning and deep learning continues to evolve, integrating more advanced features and offering more efficient and easier ways to build, train, and deploy models. PyTorch PyTorch is a widely used open-source machine learning library based on the Torch library.

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Unite.AI

MAY 29, 2024

With nine times the speed of the Nvidia A100, these GPUs excel in handling deep learning workloads. This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction.

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Marktechpost

MAY 5, 2024

In deep learning, especially in NLP, image analysis, and biology, there is an increasing focus on developing models that offer both computational efficiency and robust expressiveness. Orchid sets a new benchmark in sequence modeling, enabling more efficient deep-learning models to process ever-larger datasets.

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Marktechpost

SEPTEMBER 17, 2024

In deep learning, neural network optimization has long been a crucial area of focus. Training large models like transformers and convolutional networks requires significant computational resources and time. One of the central challenges in this field is the extended time needed to train complex neural networks.

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Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 116

Unite.AI

OCTOBER 27, 2024

The ecosystem has rapidly evolved to support everything from large language models (LLMs) to neural networks, making it easier than ever for developers to integrate AI capabilities into their applications. is its intuitive approach to neural network training and implementation. environments. TensorFlow.js TensorFlow.js

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The MLOps Blog

NOVEMBER 14, 2023

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. The reasons for this range from wrongly connected model components to misconfigured optimizers.

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AI Weekly

AUGUST 10, 2023

dezeen.com New deep learning model can predict passwords from keystroke sound with 95% accuracy A team of researchers in the United Kingdom developed a deep learning model that can accurately predict what you are typing based on the sounds from keyboard keystrokes, Bleeping Computer reported on August 5. 1.41%) (BRK.B

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Unite.AI

SEPTEMBER 18, 2023

Artificial Intelligence is a very vast branch in itself with numerous subfields including deep learning, computer vision , natural language processing , and more. NLP in particular has been a subfield that has been focussed heavily in the past few years that has resulted in the development of some top-notch LLMs like GPT and BERT.

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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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Analytics Vidhya

JANUARY 4, 2022

Knowledge Distillation aims to transfer knowledge from a large deep learning model to a small […]. Image Source: Alpha Coders What is Knowledge Distillation? The post Knowledge Distillation: Theory and End to End Case Study appeared first on Analytics Vidhya.

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Marktechpost

DECEMBER 19, 2023

Advancements in deep learning have influenced a wide variety of scientific and industrial applications in artificial intelligence. Recurrent Neural Networks (RNNs) and Transformers are the most common methods; each has advantages and disadvantages.

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Analytics Vidhya

JUNE 26, 2023

Introduction Attention models, also known as attention mechanisms, are input processing techniques used in neural networks. They allow the network to focus on different aspects of complex input individually until the entire data set is categorized.

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Becoming Human

MARCH 16, 2023

Photo by NASA on Unsplash Hello and welcome to this post, in which I will study a relatively new field in deep learning involving graphs — a very important and widely used data structure. This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications.

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AssemblyAI

MAY 3, 2023

This process of adapting pre-trained models to new tasks or domains is an example of Transfer Learning , a fundamental concept in modern deep learning. Transfer learning allows a model to leverage the knowledge gained from one task and apply it to another, often with minimal additional training.

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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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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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NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

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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NLP Neural Network Natural Language Processing Machine Learning 98

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

NOVEMBER 24, 2024

Summary: Autoencoders are powerful neural networks used for deep learning. Their applications include dimensionality reduction, feature learning, noise reduction, and generative modelling. An autoencoder is a neural network designed to learn a compressed representation of input data.

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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. In this post, I’ll be demonstrating two deep learning approaches to sentiment analysis. deep” architecture).

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Marktechpost

AUGUST 3, 2024

Multi-layer perceptrons (MLPs) have become essential components in modern deep learning models, offering versatility in approximating nonlinear functions across various tasks. However, these neural networks face challenges in interpretation and scalability. Check out the Paper and GitHub.

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Marktechpost

JUNE 3, 2024

Its AI courses offer hands-on training for real-world applications, enabling learners to effectively use Intel’s portfolio in deep learning, computer vision, and more. It covers AI fundamentals, including supervised learning and deep learning basics, without complex math.

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Marktechpost

JULY 31, 2024

TensorFlow is a powerful open-source framework for building and deploying machine learning models. Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics.

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Neural Network Auto-classification Natural Language Processing Convolutional Neural Networks 110

IBM Journey to AI blog

DECEMBER 20, 2023

Many retailers’ e-commerce platforms—including those of IBM, Amazon, Google, Meta and Netflix—rely on artificial neural networks (ANNs) to deliver personalized recommendations. They’re also part of a family of generative learning algorithms that model the input distribution of a given class or/category.

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Unite.AI

AUGUST 28, 2023

With advancements in deep learning, natural language processing (NLP), and AI, we are in a time period where AI agents could form a significant portion of the global workforce. Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience.

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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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Analytics Vidhya

JULY 28, 2019

Overview Learn how to build your own text generator in Python using OpenAI’s GPT-2 framework GPT-2 is a state-of-the-art NLP framework – a truly. The post OpenAI’s GPT-2: A Simple Guide to Build the World’s Most Advanced Text Generator in Python appeared first on Analytics Vidhya.

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Unite.AI

NOVEMBER 5, 2024

The need for specialized AI accelerators has increased as AI applications like machine learning, deep learning , and neural networks evolve. The Artificial Intelligence (AI) chip market has been growing rapidly, driven by increased demand for processors that can handle complex AI tasks.

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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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Pickl AI

NOVEMBER 26, 2024

Summary: Recurrent Neural Networks (RNNs) are specialised neural networks designed for processing sequential data by maintaining memory of previous inputs. Introduction Neural networks have revolutionised data processing by mimicking the human brain’s ability to recognise patterns.

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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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PyImageSearch

JANUARY 30, 2023

This last blog of the series will cover the benefits, applications, challenges, and tradeoffs of using deep learning in the education sector. To learn about Computer Vision and Deep Learning for Education, just keep reading. As soon as the system adapts to human wants, it automates the learning process accordingly.

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