Towards AI

AUGUST 25, 2023

Machine learning (ML) is a subset of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed. Deep learning (DL) is a subset of machine learning that uses neural networks which have a structure similar to the human neural system.

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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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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. Recent advances in supervised and unsupervised machine learning techniques brought breakthroughs in the research field, and more and more accurate systems are emerging every year.

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Pickl AI

NOVEMBER 28, 2024

Summary: Generative Adversarial Network (GANs) in Deep Learning generate realistic synthetic data through a competitive framework between two networks: the Generator and the Discriminator. In answering the question, “What is a Generative Adversarial Network (GAN) in Deep Learning?”

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Mlearning.ai

APRIL 1, 2023

NLP A Comprehensive Guide to Word2Vec, Doc2Vec, and Top2Vec for Natural Language Processing In recent years, the field of natural language processing (NLP) has seen tremendous growth, and one of the most significant developments has been the advent of word embedding techniques.

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Becoming Human

MARCH 16, 2023

Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. All pharma giants, including Bayer, AstraZeneca, Takeda, Sanofi, Merck, and Pfizer, have stepped up spending in the hope to create new-age AI solutions that will bring cost efficiency, speed, and precision to the process.

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How to Learn Machine Learning

JULY 20, 2024

It falls under machine learning and uses deep learning algorithms and programs to create music, art, and other creative content based on the user’s input. However, significant strides were made in 2014 when Lan Goodfellow and his team introduced Generative adversarial networks (GANs).

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AWS Machine Learning Blog

JANUARY 25, 2023

Apart from supporting explanations for tabular data, Clarify also supports explainability for both computer vision (CV) and natural language processing (NLP) using the same SHAP algorithm. It is constructed by selecting 14 non-overlapping classes from DBpedia 2014.

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ML Review

JUNE 4, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part Two) This is the second instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP).

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Lexalytics

APRIL 5, 2021

They were not wrong: the results they found about the limitations of perceptrons still apply even to the more sophisticated deep-learning networks of today. And indeed we can see other machine learning topics arising to take their place, like “optimization” in the mid-’00s, with “deep learning” springing out of nowhere in 2012.

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Mlearning.ai

MARCH 9, 2023

Recent studies have demonstrated that deep learning-based image segmentation algorithms are vulnerable to adversarial attacks, where carefully crafted perturbations to the input image can cause significant misclassifications (Xie et al., 2018; Sitawarin et al., 2018; Papernot et al., 2013; Goodfellow et al., For instance, Xu et al.

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Heartbeat

AUGUST 21, 2023

AlexNet significantly improved performance over previous approaches and helped popularize deep learning and CNNs. GoogLeNet: is a highly optimized CNN architecture developed by researchers at Google in 2014. VGG-16: does the Visual Geometry Group develop an intense CNN architecture at the University of Oxford?

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AWS Machine Learning Blog

NOVEMBER 15, 2023

As an example downstream application, the fine-tuned model can be used in pre-labeling workflows such as the one described in Auto-labeling module for deep learning-based Advanced Driver Assistance Systems on AWS. His core interests include deep learning and serverless technologies.

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Mlearning.ai

FEBRUARY 15, 2023

Introduction In natural language processing, text categorization tasks are common (NLP). Uysal and Gunal, 2014). Deep learning models with multilayer processing architecture are now outperforming shallow or standard classification models in terms of performance [5]. Ensemble deep learning: A review.

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ML Review

FEBRUARY 26, 2018

Tasks such as “I’d like to book a one-way flight from New York to Paris for tomorrow” can be solved by the intention commitment + slot filing matching or deep reinforcement learning (DRL) model. Chitchatting, such as “I’m in a bad mood”, pulls up a method that marries the retrieval model with deep learning (DL).

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Sebastian Ruder

AUGUST 23, 2021

Does this mean that we have solved natural language processing? For instance, the AI Index Report 2021 uses SuperGLUE and SQuAD as a proxy for overall progress in natural language processing. Such information may provide a useful learning signal ( Plank et al., Far from it.

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Sebastian Ruder

MAY 22, 2020

This advice should be most relevant to people studying machine learning (ML) and natural language processing (NLP) as that is what I did in my PhD. If you are an independent researcher, want to start a PhD in the future or simply want to learn, then you will find most of this advice applicable.

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Sebastian Ruder

NOVEMBER 14, 2022

Developing models that work for more languages is important in order to offset the existing language divide and to ensure that speakers of non-English languages are not left behind, among many other reasons. This post is partially based on a keynote I gave at the Deep Learning Indaba 2022. Kolesnikov, A.,

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ODSC - Open Data Science

JANUARY 29, 2024

Knowledge in these areas enables prompt engineers to understand the mechanics of language models and how to apply them effectively. GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. NLP skills have long been essential for dealing with textual data.

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John Snow Labs

AUGUST 7, 2024

A significant milestone was reached in 2014 with the introduction of Generative Adversarial Networks (GANs). Healthcare NLP (Natural Language Processing) technologies extract insights from physician records, patient histories and diagnostic reports facilitating precise diagnosis. This improves access to care.

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Viso.ai

JANUARY 2, 2024

VGGNet , introduced by Simonyan and Zisserman in 2014, emphasized the importance of depth in CNN architectures through its 16-19 layer CNN network. GoogleNet (or Inception) brought the novel concept of inception modules, enabling efficient computation and deeper networks without a significant increase in parameters.

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John Snow Labs

FEBRUARY 29, 2024

A significant milestone was reached in 2014 with the introduction of Generative Adversarial Networks (GANs). Healthcare NLP (Natural Language Processing) technologies extract insights from physician records, patient histories and diagnostic reports facilitating precise diagnosis. This improves access to care.

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ML Review

MAY 3, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part One) This is the first instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Thanks for reading!

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Probably Approximately a Scientific Blog

JANUARY 3, 2016

We're already after 6 posts on the topic of natural language processing, and I can't believe I haven't discussed this basic topic yet. So today I'm going to discuss words; More accurately - I will discuss how words are represented in natural language processing. EMNLP 2014. Distributional structure."

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Bugra Akyildiz

AUGUST 10, 2024

Articles Quantization in deep learning refers to the process of reducing the precision of the numbers used to represent the model's parameters and activations. Typically, deep learning models use 32-bit floating-point numbers (float32) for computations. million per year in 2014 currency) in Shanghai.

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NLP People

JULY 24, 2019

NLP, a major buzzword in today’s tech discussion, deals with how computers can understand and generate language. The rise of NLP in the past decades is backed by a couple of global developments – the universal hype around AI, exponential advances in the field of Deep Learning and an ever-increasing quantity of available text data.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Large-scale deep learning has recently produced revolutionary advances in a vast array of fields. is a startup dedicated to the mission of democratizing artificial intelligence technologies through algorithmic and software innovations that fundamentally change the economics of deep learning. Founded in 2021, ThirdAI Corp.

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Viso.ai

DECEMBER 21, 2023

His doctoral thesis studied the design of convolutional/recurrent neural networks and their applications across computer vision, natural language processing, and their intersections. Karpathy began his journey with Google DeepMind, focusing on model-based deep reinforcement learning.

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ODSC - Open Data Science

FEBRUARY 29, 2024

The Stanford AI Lab Founded in 1963, the Stanford AI Lab has made significant contributions to various domains, including natural language processing, computer vision, and robotics. Their research encompasses a broad spectrum of AI disciplines, including AI theory, reinforcement learning, and robotics. But that’s not all.

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Snorkel AI

MAY 24, 2023

From generative modeling to automated product tagging, cloud computing, predictive analytics, and deep learning, the speakers present a diverse range of expertise. He leads corporate strategy for machine learning, natural language processing, information retrieval, and alternative data.

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Snorkel AI

MAY 24, 2023

From generative modeling to automated product tagging, cloud computing, predictive analytics, and deep learning, the speakers present a diverse range of expertise. He leads corporate strategy for machine learning, natural language processing, information retrieval, and alternative data.

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TheSequence

AUGUST 23, 2023

I launched The Allen Institute of AI (AI2) in 2014 for the late Paul Allen and it’s grown to 250+ and over $100M in annual funding. With the rise of deep learning, Beaker evolved to primarily support GPU jobs and manage workloads across our dedicated GPU cluster.

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Flipboard

JANUARY 15, 2025

Previously, Patrick was a data scientist specializing in natural language processing and AI-driven insights at Hyper Anna (acquired by Alteryx) and holds a Bachelors degree from the University of Sydney. He is now leading the development of GraphStorm, an open source graph machine learning framework for enterprise use cases.

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