Dataconomy

MARCH 19, 2025

Tensor Processing Units (TPUs) represent a significant leap in hardware specifically designed for machine learning tasks. They are essential for processing large amounts of data efficiently, particularly in deep learning applications.

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Analytics Vidhya

APRIL 29, 2024

Introduction In 2018, when we were contemplating whether AI would take over our jobs or not, OpenAI put us on the edge of believing that. Our way of working has completely changed after the inception of OpenAI’s ChatGPT in 2022. But is it a threat or a boon?

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Dataconomy

MARCH 20, 2025

TensorFlow has revolutionized the field of machine learning and deep learning since its inception. TensorFlow is an open-source framework designed for machine learning and deep learning applications. These APIs simplify user interactions and expedite the development of data pipelines.

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Smart Data Collective

FEBRUARY 24, 2021

However, this ever-evolving machine learning technology might surprise you in this regard. The truth is that machine learning is now capable of writing amazing content. Machine Learning to Write your College Essays. Machine Learning to Write your College Essays.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

Since 2018, using state-of-the-art proprietary and open source large language models (LLMs), our flagship product— Rad AI Impressions — has significantly reduced the time radiologists spend dictating reports, by generating Impression sections. This post is co-written with Ken Kao and Hasan Ali Demirci from Rad AI. Ken holds M.S.

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Dataconomy

MARCH 5, 2025

is dedicated to creating systems that can learn and adapt, a fundamental step toward achieving General-Purpose Artificial Intelligence (AGI). Technology and methodology DeepMind’s approach revolves around sophisticated machine learning methods that enable AI to interact with its environment and learn from experience.

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ODSC - Open Data Science

SEPTEMBER 21, 2023

The majority of us who work in machine learning, analytics, and related disciplines do so for organizations with a variety of different structures and motives. The following is an extract from Andrew McMahon’s book , Machine Learning Engineering with Python, Second Edition.

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Hacker News

SEPTEMBER 24, 2021

Deep learning is now being used to translate between languages, predict how proteins fold , analyze medical scans , and play games as complex as Go , to name just a few applications of a technique that is now becoming pervasive. Although deep learning's rise to fame is relatively recent, its origins are not.

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ML @ CMU

JANUARY 2, 2025

2018 ) to enhance training (see Materials and Methods in Zhang et al., It then outputs the estimated (Q_t) for this action, trained through the temporal-difference error (TD error) after receiving the reward (r_t) ((|r_t+gamma Q_{t+1}-Q_{t}|), where (gamma) denotes the temporal discount factor).

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ODSC - Open Data Science

MARCH 12, 2025

By analyzing conference session titles and abstracts from 2018 to 2024, we can trace the rise and fall of key trends that shaped the industry. The Rise of AI Engineering andMLOps 20182019: Early discussions around MLOps and AI engineering were sparse, primarily focused on general machine learning best practices.

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Data Science 101

MARCH 6, 2019

Luckily, a few of them are willing to share data science, machine learning and deep learning materials online for everyone. Here is just I small list I have come across lately. Do you have any favorite university resources? If so, please leave a comment.

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NYU Center for Data Science

OCTOBER 16, 2024

The tweet linked to a paper from 2018, hinting at the foundational research behind these now-commercialized ideas. Back in 2018, recent CDS PhD grad Katrina Drozdov (née Evtimova), Cho, and their colleagues published a paper at ICLR called “ Emergent Communication in a Multi-Modal, Multi-Step Referential Game.”

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Data Science Dojo

SEPTEMBER 13, 2023

Deep learning And NLP Deep Learning and Natural Language Processing (NLP) are like best friends in the world of computers and language. Deep Learning is when computers use their brains, called neural networks, to learn lots of things from a ton of information. I was developed by OpenAI in 2018.

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Dataconomy

OCTOBER 2, 2024

Kingma, is a prominent figure in the field of artificial intelligence and machine learning. 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. He earned his Ph.D.

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AWS Machine Learning Blog

APRIL 7, 2025

This approach allows for greater flexibility and integration with existing AI and machine learning (AI/ML) workflows and pipelines. yml file from the AWS Deep Learning Containers GitHub repository, illustrating how the model synthesizes information across an entire repository. billion to a projected $574.78

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AWS Machine Learning Blog

OCTOBER 5, 2023

Before that, he worked on developing machine learning methods for fraud detection for Amazon Fraud Detector. He is passionate about applying machine learning, optimization, and generative AI techniques to various real-world problems. He focuses on developing scalable machine learning algorithms.

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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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AWS Machine Learning Blog

NOVEMBER 26, 2024

By harnessing the power of threat intelligence, machine learning (ML), and artificial intelligence (AI), Sophos delivers a comprehensive range of advanced products and services. Amazon SageMaker Amazon SageMaker is a fully managed machine learning (ML) service. His latest research can be found on Google Scholar.

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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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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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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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Flipboard

JANUARY 25, 2025

Cancer 18, 500510 (2018). - Quackenbush, J., Schwartz, L. Artificial intelligence in radiology.

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Towards AI

DECEMBER 28, 2023

Later, Python gained momentum and surpassed all programming languages, including Java, in popularity around 2018–19. The advent of more powerful personal computers paved the way for the gradual acceptance of deep learning-based methods. CS6910/CS7015: Deep Learning Mitesh M. Khapra Homepage www.cse.iitm.ac.in

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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. A total of 300 million words and 175 billion parameters have been analyzed by BERT’s machine learning algorithms, which far exceed the training model used by the model.

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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 ). How Are Autoencoders Different from GANs?

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NYU Center for Data Science

OCTOBER 10, 2023

CDS PhD Student Angelica Chen Most machine learning interpretability research analyzes the behavior of models after their training is complete, and is often correlational, or even anecdotal. The paper is a case study of syntax acquisition in BERT (Bidirectional Encoder Representations from Transformers). By Stephen Thomas

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Heartbeat

SEPTEMBER 13, 2023

This guide will buttress explainability in machine learning and AI systems. The explainability concept involves providing insights into the decisions and predictions made by artificial intelligence (AI) systems and machine learning models. What is Explainability?

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Towards AI

APRIL 5, 2023

Picture created with Dall-E-2 Yoshua Bengio, Geoffrey Hinton, and Yann LeCun, three computer scientists and artificial intelligence (AI) researchers, were jointly awarded the 2018 Turing Prize for their contributions to deep learning, a subfield of AI.

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How to Learn Machine Learning

JANUARY 10, 2023

Still, you’ll probably be familiar with Machine Learning or Blockchain apps and their potential to reform the tech world. Fascinated by the world of Artificial Intelligence and Machine Learning and wondering how to start? Python: The Best Programming Language To Choose For Blockchain Programming and Machine Learning.

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Towards AI

NOVEMBER 16, 2024

This blog explores how Keswani’s method addresses common challenges in min-max scenarios, with applications in areas of modern Machine Learning such as GANs, adversarial training, and distributed computing, providing a robust alternative to traditional algorithms like Gradient Descent Ascent (GDA). Arjovsky, S. Chintala, and L.

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Mlearning.ai

JUNE 8, 2023

Netflix-style if-you-like-these-movies-you’ll-like-this-one-too) All kinds of search Text search (like Google Search) Image search (like Google Reverse Image Search) Chatbots and question-answering systems Data preprocessing (preparing data to be fed into a machine learning model) One-shot/zero-shot learning (i.e.

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AWS Machine Learning Blog

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. We recently developed four more new models.

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Heartbeat

MARCH 14, 2023

Some of the methods used for scene interpretation include Convolutional Neural Networks (CNNs) , a deep learning-based methodology, and more conventional computer vision-based techniques like SIFT and SURF. A combination of simulated and real-world data was used to train the system, enabling it to generalize to new objects and tasks.

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Mlearning.ai

APRIL 8, 2023

SOTA (state-of-the-art) in machine learning refers to the best performance achieved by a model or system on a given benchmark dataset or task at a specific point in time. The earlier models that were SOTA for NLP mainly fell under the traditional machine learning algorithms. Citation: Article from IBM archives 2.

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Heartbeat

JANUARY 29, 2024

Photo by Brett Jordan on Unsplash In the ever-evolving landscape of artificial intelligence and machine learning, researchers and practitioners continuously seek to elevate the capabilities of intelligent systems. Among the myriad breakthroughs in this field, Meta-Learning is pushing the boundaries of machine learning.

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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. Moreover, random forest models as well as support vector machines (SVMs) are also frequently applied.

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ODSC - Open Data Science

MARCH 10, 2025

Over the past decade, data science has undergone a remarkable evolution, driven by rapid advancements in machine learning, artificial intelligence, and big data technologies. By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow.

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