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. In ML, there are a variety of algorithms that can help solve problems. Any competent software engineer can implement any algorithm. 12, 2021. [6]

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Heartbeat

NOVEMBER 14, 2023

For many years, gradient-boosting models and deep-learning solutions have won the lion's share of Kaggle competitions. XGBoost is not limited to machine learning tasks, as its incredible power can be harnessed when harmonized with deep learning algorithms. 2 (2021): 522–531. . "ConvXGB:

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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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Smart Data Collective

DECEMBER 1, 2022

The world of big data is constantly changing and evolving, and 2021 is no different. Natural language processing uses various algorithms to read, decode, and comprehend human speech. The two most common types of algorithms are deep learning and machine translation.

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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. Founded in 2021, ThirdAI Corp. 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.

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Heartbeat

MAY 23, 2023

As technology continues to improve exponentially, deep learning has emerged as a critical tool for enabling machines to make decisions and predictions based on large volumes of data. Edge computing may change how we think about deep learning. Standardizing model management can be tricky but there is a solution.

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PyImageSearch

OCTOBER 28, 2024

One day, I was looking for an email idea while writing my daily self-driving car newsletter , when I was suddenly caught by the news: Tesla had released a new FSD12 model based on End-to-End Learning. And it was because not only was the new model fully based on Deep Learning, but it also effectively removed 300,000 lines of code.

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Flipboard

JUNE 20, 2023

Rather than humans programming computers with specific step-by-step instructions on how to complete a task, in machine learning a human provides the AI with data and asks it to achieve a certain outcome via an algorithm. Deep learning Deep learning is a specific type of machine learning used in the most powerful AI systems.

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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. AI began back in the 1950s as a simple series of “if, then rules” and made its way into healthcare two decades later after more complex algorithms were developed. AI drug discovery is exploding.

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AssemblyAI

MARCH 21, 2023

Next-generation traffic prediction algorithm (Google Maps) Another highly impactful application of Graph Neural Networks came from a team of researchers from DeepMind who showed how GNNs can be applied to transportation maps to improve the accuracy of estimated time of arrival (ETA).

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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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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. an AI start-up, and worked as the CEO and Chief Scientist in 2019–2021. Yida Wang is a principal scientist in the AWS AI team of Amazon.

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Flipboard

JULY 10, 2023

Figure 4: Architecture of fully connected autoencoders (source: Amor, “Comprehensive introduction to Autoencoders,” ML Cheat Sheet , 2021 ). Figure 5: Architecture of Convolutional Autoencoder for Image Segmentation (source: Bandyopadhyay, “Autoencoders in Deep Learning: Tutorial & Use Cases [2023],” V7Labs , 2023 ).

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ML @ CMU

SEPTEMBER 15, 2023

At test time, we optimize only the reconstruction loss Our contributions are as follows: (i) We present an algorithm that significantly improves scene decomposition accuracy for out-of-distribution examples by performing test-time adaptation on each example in the test set independently. iv) Semantic-NeRF (Zhi et al.,

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Shreyansh Singh

DECEMBER 17, 2021

In my last post, I covered a high-level overview of Federated Learning, its applications, advantages & challenges. We also went through a high-level overview of how Federated Optimization algorithms work. But from a mathematical sense, how is Federated Learning training actually performed? But why make this change?

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AWS Machine Learning Blog

MAY 30, 2023

In 2021, we launched AWS Support Proactive Services as part of the AWS Enterprise Support plan. Since its introduction, we’ve helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their machine learning (ML) workloads’ cost and usage.

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AWS Machine Learning Blog

APRIL 7, 2025

yml file from the AWS Deep Learning Containers GitHub repository, illustrating how the model synthesizes information across an entire repository. His role focuses on enabling customers to take advantage of state-of-the-art open source and proprietary foundation models and traditional machine learning algorithms.

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Hacker News

MARCH 25, 2022

They’re driving a wave of advances in machine learning some have dubbed transformer AI. Stanford researchers called transformers “foundation models” in an August 2021 paper because they see them driving a paradigm shift in AI. Transformers Replace CNNs, RNNs.

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AWS Machine Learning Blog

FEBRUARY 25, 2025

In 2021, Applus+ IDIADA , a global partner to the automotive industry with over 30 years of experience supporting customers in product development activities through design, engineering, testing, and homologation services, established the Digital Solutions department. This method takes a parameter, which we set to 3.

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Applied Data Science

DECEMBER 23, 2022

In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. This trend started in 2021, with OpenAI Codex , a GPT-3 based tool. How is this even possible?

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BAIR

APRIL 6, 2023

These robots use recent advances in deep learning to operate autonomously in unstructured environments. By pooling data from all robots in the fleet, the entire fleet can efficiently learn from the experience of each individual robot. Using this formalism, we can now instantiate and compare IFL algorithms (i.e.,

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ODSC - Open Data Science

FEBRUARY 28, 2023

Tangent Works Recognized by Gartner as a Cool Vendor in 2021, Tangent Works’ goal is to help organizations effectively utilize their time-series data for predictive models. Cloudera For Cloudera, it’s all about machine learning optimization.

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ODSC - Open Data Science

MARCH 12, 2025

By 2021, GPT-3 had demonstrated unprecedented capabilities in text generation, leading to widespread adoption. The Decline of Traditional MachineLearning 20182020: Algorithms like random forests, SVMs, and gradient boosting were frequent discussion points.

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Dataconomy

APRIL 25, 2023

Since its release in November 2021, ChatGPT has amassed millions of users and widespread media coverage, greatly increasing OpenAI’s valuation. This is more than double the valuation of $14 billion that OpenAI had in 2021 when it completed a tender offer for existing shares.

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IBM Journey to AI blog

JULY 11, 2023

According to Statista , the artificial intelligence (AI) healthcare market, valued at $11 billion in 2021, is projected to be worth $187 billion in 2030. One use case example is out of the University of Hawaii, where a research team found that deploying deep learning AI technology can improve breast cancer risk prediction.

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Towards AI

JULY 20, 2023

As the capabilities of high-powered computers and ML algorithms have grown, so have opportunities to improve the SLR process. New research has also begun looking at deep learning algorithms for automatic systematic reviews, According to van Dinter et al.

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DrivenData Labs

MAY 24, 2023

Her primary interests lie in theoretical machine learning. She currently does research involving interpretability methods for biological deep learning models. We chose to compete in this challenge primarily to gain experience in the implementation of machine learning algorithms for data science.

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Dataconomy

APRIL 4, 2024

Deep learning, TensorFlow and other technologies emerged, mostly to power search engines, recommendations and advertising. If you feed an algorithm enough English and French text, it can figure out how to translate from one to another by understanding the relationships between the words of each language. Costs dropped.

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DrivenData Labs

JUNE 14, 2023

Accurate and performant algorithms are critical in flagging and removing inappropriate content. His research interest is deep metric learning and computer vision. His research interests focus on deep representation learning, data problem (e.g., Yifan Sun is currently a Senior Expert at Baidu Inc.

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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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Mlearning.ai

APRIL 10, 2023

3 feature visual representation of a K-means Algorithm. Source: Marubon-DS Unsupervised Learning In the data science context, clustering is an unsupervised machine learning technique, this means that it does not require predefined labeled inputs or outcomes to learn from.

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ODSC - Open Data Science

MARCH 22, 2023

Many companies are now utilizing data science and machine learning , but there’s still a lot of room for improvement in terms of ROI. Nevertheless, we are still left with the question: How can we do machine learning better? billion in 2022, an increase of 21.3% billion in 2022, an increase of 21.3%

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Heartbeat

FEBRUARY 27, 2023

Photo by Markus Spiske on Unsplash Deep learning has grown in importance as a focus of artificial intelligence research and development in recent years. Deep Reinforcement Learning (DRL) and Generative Adversarial Networks (GANs) are two promising deep learning trends.

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AWS Machine Learning Blog

DECEMBER 20, 2023

SageMaker JumpStart is the machine learning (ML) hub of Amazon SageMaker that provides access to foundation models in addition to built-in algorithms and end-to-end solution templates to help you quickly get started with ML. About the authors Dr. Kyle Ulrich is an Applied Scientist with the Amazon SageMaker built-in algorithms team.

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AWS Machine Learning Blog

NOVEMBER 14, 2023

Inference example with and without fine-tuning The following table contains the results of the Mistral 7B model fine-tuned with SEC filing documents of Amazon from 2021–2022. About the Authors Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms.

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DrivenData Labs

DECEMBER 11, 2024

Deep learning - It is hard to overstate how deep learning has transformed data science. In tandem, software for deep learning has grown into a rich and mature ecosystem with PyTorch and TensorFlow as the keystones, along with an impressive host of tools, libraries, pre-trained models and datasets that accelerate progress.

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Towards AI

MAY 31, 2024

In deep learning, diffusion models have already replaced State-of-the-art generative frameworks like GANs or VAEs. Similar to non-equilibrium statistical physics, the idea behind diffusion models in deep learning is to slowly and iteratively destroy the structure of a data distribution through a forward diffusion process.

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