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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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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NLP Natural Language Processing Data Science Neural Network 400

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 […].

Neural Network 381

Neural Network Deep Learning Data Science NLP 381

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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Pickl AI

JANUARY 29, 2025

Summary: This article presents 10 engaging Deep Learning projects for beginners, covering areas like image classification, emotion recognition, and audio processing. Each project is designed to provide practical experience and enhance understanding of key concepts in Deep Learning. What is Deep Learning?

Deep Learning 52

Deep Learning Neural Network Natural Language Processing Computer Vision 52

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.

Deep Learning 52

Deep Learning Neural Network Natural Language Processing Computer Vision 52

Pickl AI

JANUARY 26, 2025

Summary: Deep Learning models revolutionise data processing, solving complex image recognition, NLP, and analytics tasks. Introduction Deep Learning models transform how we approach complex problems, offering powerful tools to analyse and interpret vast amounts of data. With a projected market growth from USD 6.4

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Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

Unite.AI

JANUARY 18, 2025

While Central Processing Units (CPUs) and Graphics Processing Units (GPUs) have historically powered traditional computing tasks and graphics rendering, they were not originally designed to tackle the computational intensity of deep neural networks.

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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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Towards AI

JANUARY 29, 2025

Generative AI is powered by advanced machine learning techniques, particularly deep learning and neural networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Learn about supervised, unsupervised, and reinforcement learning.

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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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Deep Learning Machine Learning Python Computer Vision 123

Bugra Akyildiz

FEBRUARY 26, 2025

Stanford CS224n: Natural Language Processing with Deep Learning Stanford’s CS224n stands as the gold standard for NLP education, offering a rigorous exploration of neural architectures, sequence modeling, and transformer-based systems. S191: Introduction to Deep Learning MIT’s 6.S191

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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

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 113

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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Towards AI

FEBRUARY 3, 2025

DeepSeek AI is an advanced AI genomics platform that allows experts to solve complex problems using cutting-edge deep learning, neural networks, and natural language processing (NLP). DeepSeek AI can learn and improve over time, as opposed to being governed by static, pre-defined principles. Lets begin!

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Natural Language Processing OpenAI Neural Network AI 125

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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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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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

Data Science Neural Network Convolutional Neural Networks Large Language Models 52

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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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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Towards AI

DECEMBER 16, 2024

Multi-Modal Applications: Combine with NLP models to create AI assistants that interpret both text and images. Kernel Arnold Networks (KAN) Summary: Kernel Arnold Networks (KAN) propose a new way of representing and processing data, challenging traditional deep neural networks.

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AI Research AI Researcher Computer Vision Neural Network 111

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

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

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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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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Neural Network Convolutional Neural Networks BERT Deep Learning 158

Marek Rei

NOVEMBER 4, 2024

Mind the gap: Challenges of deep learning approaches to Theory of Mind Jaan Aru, Aqeel Labash, Oriol Corcoll, Raul Vicente. link] An opinion paper on deep learning models in connection to the Theory of Mind – the skill of humans to understand the minds of others, imagine that they might have hidden knowledge or emotions.

Machine Learning 80

Machine Learning NLP Large Language Models LLM 80

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).

Deep Learning 98

Deep Learning Convolutional Neural Networks NLP Neural Network 98

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.

Computer Vision 122

Computer Vision Machine Learning NLP Neural Network 122

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.

Convolutional Neural Networks 122

Convolutional Neural Networks Neural Network Deep Learning Computer Vision 122