Dataconomy

OCTOBER 2, 2024

cum laude in machine learning from the University of Amsterdam in 2017. His academic work, particularly in deep learning and generative models, has had a profound impact on the AI community. In 2015, Kingma co-founded OpenAI, a leading research organization in AI, where he led the algorithms team. He earned his Ph.D.

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Towards AI

DECEMBER 28, 2023

Black box algorithms such as xgboost emerged as the preferred solution for a majority of classification and regression problems. Later, Python gained momentum and surpassed all programming languages, including Java, in popularity around 2018–19. CS6910/CS7015: Deep Learning Mitesh M. Khapra Homepage www.cse.iitm.ac.in

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Heartbeat

OCTOBER 18, 2023

In this article, we embark on a journey to explore the transformative potential of deep learning in revolutionizing recommender systems. However, deep learning has opened new horizons, allowing recommendation engines to unravel intricate patterns, uncover latent preferences, and provide accurate suggestions at scale.

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Data Science Dojo

JUNE 26, 2023

In order to learn the nuances of language and to respond coherently and pertinently, deep learning algorithms are used along with a large amount of data. The BERT algorithm has been trained on 3.3 A prompt is given to GPT-3 and it produces very accurate human-like text output based on deep learning.

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PyImageSearch

JANUARY 2, 2023

Health startups and tech companies aiming to integrate AI technologies account for a large proportion of AI-specific investments, accounting for up to $2 billion in 2018 ( Figure 1 ). This blog will cover the benefits, applications, challenges, and tradeoffs of using deep learning in healthcare.

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Heartbeat

MARCH 14, 2023

By incorporating computer vision methods and algorithms into robots, they are able to view and understand their environment. Object recognition and tracking algorithms include the CamShift algorithm , Kalman filter , and Particle filter , among others.

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PyImageSearch

NOVEMBER 13, 2023

Home Table of Contents Faster R-CNNs Object Detection and Deep Learning Measuring Object Detector Performance From Where Do the Ground-Truth Examples Come? One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al.

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Mlearning.ai

MAY 5, 2023

These models, which are based on artificial intelligence and machine learning algorithms, are designed to process vast amounts of natural language data and generate new content based on that data. It wasn’t until the development of deep learning algorithms in the 2000s and 2010s that LLMs truly began to take shape.

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Dataconomy

NOVEMBER 20, 2023

Co-inventing AlexNet with Krizhevsky and Hinton, he laid the groundwork for modern deep learning. His work on the sequence-to-sequence learning algorithm and contributions to TensorFlow underscore his commitment to pushing AI’s boundaries. in computer science in 2013 under the guidance of Geoffrey Hinton.

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Flipboard

JULY 10, 2023

By using our mathematical notation, the entire training process of the autoencoder can be written as follows: Figure 2 demonstrates the basic architecture of an autoencoder: Figure 2: Architecture of Autoencoder (inspired by Hubens, “Deep Inside: Autoencoders,” Towards Data Science , 2018 ). Or requires a degree in computer science?

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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. NLP algorithms help computers understand, interpret, and generate natural language.

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Dataconomy

OCTOBER 5, 2023

AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand. Common choices include neural networks (used in deep learning), decision trees, support vector machines, and more. Another form of machine learning algorithm is known as unsupervised learning.

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AWS Machine Learning Blog

OCTOBER 5, 2023

Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms. He focuses on developing scalable machine learning algorithms. From 2015–2018, he worked as a program director at the US NSF in charge of its big data program. He founded StylingAI Inc.,

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ODSC - Open Data Science

MARCH 22, 2023

Cleanlab is an open-source software library that helps make this process more efficient (via novel algorithms that automatically detect certain issues in data) and systematic (with better coverage to detect different types of issues). Data-centric AI instead asks how we can systematically engineer better data through algorithms/automation.

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Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., Understanding the robustness of image segmentation algorithms to adversarial attacks is critical for ensuring their reliability and security in practical applications.

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Towards AI

FEBRUARY 10, 2023

The term “artificial intelligence” may evoke the ideas of algorithms and data, but it is powered by the rare earth’s minerals and resources that make up the computing components [1]. China’s data center industry gets 73% of its power from coal, emitting roughly 99 million tons of CO2 in 2018 [4].

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Towards AI

JULY 23, 2023

Year and work published Generative Pre-trained Transformer (GPT) In 2018, OpenAI introduced GPT, which has shown, with the implementation of pre-training, transfer learning, and proper fine-tuning, transformers can achieve state-of-the-art performance.

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Dataconomy

MAY 8, 2023

AI drawing generators use machine learning algorithms to produce artwork What is AI drawing? You might think of AI drawing as a generative art where the artist combines data and algorithms to create something completely new. They use deep learning models to learn from large sets of images and make new ones that meet the prompts.

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Data Science Blog

OCTOBER 20, 2024

Algorithmic Attribution using binary Classifier and (causal) Machine Learning While customer journey data often suffices for evaluating channel contributions and strategy formulation, it may not always be comprehensive enough. All those models are part of the Machine Learning & AI Toolkit for assessing MTA. Mahboobi, S.

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Smart Data Collective

FEBRUARY 24, 2021

You might have received a lengthy email from your coworker, and you could simply press on the ‘Got it’ response suggested by Google’s AI algorithm to compose your reply. Machine Learning to Write your College Essays. Let us take a look at a few cases that will offer us more insight. The numbers were clearly average.

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Flipboard

AUGUST 25, 2023

Zhavoronkov has a narrower definition of AI drug discovery, saying it refers specifically to the application of deep learning and generative learning in the drug discovery space. The “deep learning revolution” — a time when development and use of the technology exploded — took off around 2014, Zhavoronkov said.

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Towards AI

JULY 20, 2023

An additional 2018 study found that each SLR takes nearly 1,200 total hours per project. As the capabilities of high-powered computers and ML algorithms have grown, so have opportunities to improve the SLR process. dollars apiece.

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Pickl AI

APRIL 8, 2024

This blog explores 13 major AI blunders, highlighting issues like algorithmic bias, lack of transparency, and job displacement. From the moment we wake up to the personalized recommendations on our phones to the algorithms powering facial recognition software, AI is constantly shaping our world.

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DrivenData Labs

JULY 19, 2023

Over the past few years, our team has run over 20 different machine learning competitions in the domain of climate change and AI, with over a million dollars awarded to developers of the top-performing approaches. From these, we've identified five distinct areas where AI is having an impact on climate action. ?

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ODSC - Open Data Science

SEPTEMBER 21, 2023

After all, this is what machine learning really is; a series of algorithms rooted in mathematics that can iterate some internal parameters based on data. This is understandable: a report by PwC in 2018 suggested that 30% of UK jobs will be impacted by automation by the 2030s Will Robots Really Steal Our Jobs?

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Mlearning.ai

JANUARY 17, 2024

The foundations for today’s generative language applications were elaborated in the 1990s ( Hochreiter , Schmidhuber ), and the whole field took off around 2018 ( Radford , Devlin , et al.). Deep learning neural network. In the code, the complete deep learning network is represented as a matrix of weights.

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Heartbeat

DECEMBER 14, 2023

LeCun received the 2018 Turing Award (often referred to as the "Nobel Prize of Computing"), together with Yoshua Bengio and Geoffrey Hinton, for their work on deep learning. Hinton is viewed as a leading figure in the deep learning community. He co-developed the Lush programming language with Léon Bottou.

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AWS Machine Learning Blog

APRIL 3, 2023

The accomplishments of deep learning are essentially just a type of curve fitting, whereas causality could be used to uncover interactions between the systems of the world under various constraints without testing hypotheses directly. SageMaker Asynchronous Inference allows queuing incoming requests and processes them asynchronously.

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AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). Another way can be to use an AllReduce algorithm. For example, in the ring-allreduce algorithm, each node communicates with only two of its neighboring nodes, thereby reducing the overall data transfers.

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Mlearning.ai

FEBRUARY 10, 2023

For example, if you are using regularization such as L2 regularization or dropout with your deep learning model that performs well on your hold-out-cross-validation set, then increasing the model size won’t hurt performance, it will stay the same or improve. In the big data and deep learning era, now you have much more flexibility.

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Since its launch in 2018, Just Walk Out technology by Amazon has transformed the shopping experience by allowing customers to enter a store, pick up items, and leave without standing in line to pay. AI model training—in which curated data is fed to selected algorithms—helps the system refine itself to produce accurate results.

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AWS Machine Learning Blog

MAY 2, 2023

There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19). If you have a large dataset, the SageMaker KNN algorithm may provide you with an effective semantic search.

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Pickl AI

JULY 24, 2024

Turing proposed the concept of a “universal machine,” capable of simulating any algorithmic process. The development of LISP by John McCarthy became the programming language of choice for AI research, enabling the creation of more sophisticated algorithms. Simon, demonstrated the ability to prove mathematical theorems.

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PyImageSearch

JULY 3, 2023

Figure 1: Netflix Recommendation System (source: “Netflix Film Recommendation Algorithm,” Pinterest ). Netflix recommendations are not just one algorithm but a collection of various state-of-the-art algorithms that serve different purposes to create the complete Netflix experience. Each row has a title (e.g.,

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DrivenData Labs

FEBRUARY 3, 2025

I love participating in various competitions involving deep learning, especially tasks involving natural language processing or LLMs. Dueweke and Bridges, 2018 ) To better guide suicide prevention, we must first understand the series of events that victims go through in the days, weeks, or even months prior to death.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

In 2018, other forms of PBAs became available, and by 2020, PBAs were being widely used for parallel problems, such as training of NN. Together, these elements lead to the start of a period of dramatic progress in ML, with NN being redubbed deep learning. Thirdly, the presence of GPUs enabled the labeled data to be processed.

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Heartbeat

MARCH 15, 2023

An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019. The library can be installed via pip.

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