Data Science Dojo

FEBRUARY 1, 2024

In a world of large language models (LLMs), deep double descent has created a new shift in understanding data and its position in deep learning models. A traditional LLM uses large amounts of data to train a machine-learning model, believing that bigger datasets lead to greater accuracy of results.

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Flipboard

JULY 31, 2023

Importance and Role of Datasets in Machine Learning Data is king. Algorithms are important and require expert knowledge to develop and refine, but they would be useless without data. Datasets are to machine learning what fuel is to a car: they power the entire process. Object detection is useful for many applications (e.g.,

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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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ODSC - Open Data Science

FEBRUARY 17, 2023

Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others. Machine & Deep Learning Machine learning is the fundamental data science skillset, and deep learning is the foundation for NLP.

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Towards AI

FEBRUARY 3, 2024

(Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. A few weeks ago, I needed an explainability algorithm for a YoloV8 model. The truth is, I couldn’t find anything. Lapuschkin, S.,

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AssemblyAI

MARCH 21, 2023

Top 50 keywords in submitted research papers at ICLR 2022 ( source ) A recent bibliometric study systematically analysed this research trend, revealing an exponential growth of published research involving GNNs, with a striking +447% average annual increase in the period 2017-2019.

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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

RF Diffusion, a deep learning tool. Senior scientist Bobby Langan shows a video of one of his favorite deep-learning tools, used to create experimental cancer therapeutics. Senior scientist Bobby Langan shows a video of one of his favorite deep-learning tools, used to create experimental cancer therapeutics.

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How to Learn Machine Learning

JULY 20, 2024

In this article you will learn about 7 of the top Generative AI Trends to watch out for in this year, so please please sit back relax, enjoy, and learn! 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.

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Flipboard

JULY 10, 2023

Figure 5: Architecture of Convolutional Autoencoder for Image Segmentation (source: Bandyopadhyay, “Autoencoders in Deep Learning: Tutorial & Use Cases [2023],” V7Labs , 2023 ). This can be helpful for visualization, data compression, and speeding up other machine learning algorithms. That’s not the case.

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Towards AI

JULY 20, 2023

Simple Introduction to Web Navigation Problems Photo by Émile Perron on Unsplash Model reinforcement learning algorithms have achieved astonishing results in many real-world games, such as Alpha Go and OpenAI Five. Specifically, we discuss an algorithm, named QWeb proposed by Gur et al.

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Heartbeat

OCTOBER 10, 2023

Photo by Taylor Grote on Unsplash Introduction Have you realized how rapidly artificial intelligence and machine learning have developed over the past few years? Machine learning algorithms can process and analyze enormous volumes of data, which enables them to grow and learn over time. Why Did RoBERTa Get Developed?

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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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How to Learn Machine Learning

JULY 20, 2024

For example, a photo to text converter can handle different languages, fonts, & text styles, thanks to the algorithms that support it. How machine learning enhances OCR accuracy Machine learning is a subset of artificial intelligence. This high-end versatility is important for global applications. Didn’t you know that?

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Smart Data Collective

MARCH 26, 2021

Due to its constant learning and evolution, the algorithms are able to adapt based on success and failure. Machine learning mimics the human brain. It entails deep learning from its neural networks, natural language processing (NLP), and constant changes based on incoming information.

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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. Just wait until you hear what happened in 2022. Who should I follow?

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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]. The Cost of Larger Models: Diminishing Returns Diminishing return is a growing concern in the AI industry, as stated in the 2019 paper from the Allen Institute.

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Heartbeat

DECEMBER 8, 2023

This includes cleaning and transforming data, performing calculations, or applying machine learning algorithms. 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. Meta's chief A.I.

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Mlearning.ai

JUNE 14, 2023

YouTube Introduction to Sequence Learning and Attention Mechanisms Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 — Translation, Seq2Seq, Attention — YouTube Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 7 — Translation, Seq2Seq, Attention — YouTube 2.

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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. What is RoBERTa?

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KDnuggets

DECEMBER 10, 2019

Graph machine learning is a developing area of research that brings many complexities. One challenge that both fascinates and infuriates those working with graph algorithms is — scalability.

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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. When you scroll any social media feed or watch a streaming service, recommendation algorithms are working double time.

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Heartbeat

AUGUST 23, 2023

Consider a scenario where legal practitioners are armed with clever algorithms capable of analyzing, comprehending, and extracting key insights from massive collections of legal papers. Algorithms can automatically detect and extract key items. But what if there was a technique to quickly and accurately solve this language puzzle?

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Google Research AI blog

FEBRUARY 23, 2023

Commensurate with our mission to demonstrate these societal benefits , Google Research’s programs in applied machine learning (ML) have helped place Alphabet among the top five most impactful corporate research institutions in the health and life sciences publications on the Nature Impact Index in every year from 2019 through 2022.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Together, these elements lead to the start of a period of dramatic progress in ML, with NN being redubbed deep learning. In 2017, the landmark paper “ Attention is all you need ” was published, which laid out a new deep learning architecture based on the transformer.

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Heartbeat

SEPTEMBER 13, 2023

For example, explainability is crucial if a healthcare professional uses a deep learning model for medical diagnoses. Algorithmic Accountability: Explainability ensures accountability in machine learning and AI systems. It provides insights into model refinement, feature engineering, or algorithmic modifications.

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Pickl AI

JANUARY 3, 2024

Geographic Variations: The average salary of a Machine Learning professional in India is ₹12,95,145 per annum. Career Advancement: Professionals can enhance earning potential by acquiring in-demand skills like Natural Language Processing, Deep Learning, and relevant certifications aligned with industry needs.

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Towards AI

JULY 23, 2023

Generating Wikipedia By Summarizing Long Sequences This work was published by Peter J Liu at Google in 2019. To perform this, extractive summarization methods like tf-idf, and text-rank algorithms have been used. Paper link: [link] Important Conclusion: Pre-training boosts performance.

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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. Can AI replace humans in coming up with better creative content?

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Heartbeat

JANUARY 29, 2024

Meta-Learning presents a radical departure from conventional approaches by endowing machines with the extraordinary ability to learn how to learn. This article explores Meta-Learning by delving into the cutting-edge algorithms driving its advancements and the diverse applications that redefine machine learning paradigms.

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Defined.ai blog

MARCH 8, 2023

For readers wondering what a “parameter” refers to, here’s an illustrative, if overly simplified example: if a deep learning algorithm were a cake recipe—e.g. BioMedLM’s foundational model was 2019’s GPT-2 , which—while still powerful—is less capable than the current generation of OpenAI’s GPT-3 and the forthcoming GPT-4.

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AWS Machine Learning Blog

APRIL 6, 2023

We also demonstrate the performance of our state-of-the-art point cloud-based product lifecycle prediction algorithm. For example, in the 2019 WAPE value, we trained our model using sales data between 2011–2018 and predicted sales values for the next 12 months (2019 sale). In 2019 and 2020, our model achieved less than 0.1

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Mlearning.ai

APRIL 11, 2023

Conclusion This work gives a brief overview of the double descent phenomenon, a novel concept discovered in 2019 [3]. This phenomenon was observed through some algorithms such as linear regression and neural networks [4] and remains an active area of research in the field of Machine Learning/Deep Learning.

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Hacker News

MARCH 25, 2022

“Transformers made self-supervised learning possible, and AI jumped to warp speed,” said NVIDIA founder and CEO Jensen Huang in his keynote address this week at GTC. Transformers are in many cases replacing convolutional and recurrent neural networks (CNNs and RNNs), the most popular types of deep learning models just five years ago.

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ODSC - Open Data Science

JUNE 5, 2023

Social Media Links: YouTube: [link] GitHub: [link] Twitter: [link] Mastodon: [link] LinkedIn: [link] About the author/ODSC Europe speaker: Thomas is a Senior Machine Learning engineer, working in the automotive industry since 2019.

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Mlearning.ai

JUNE 4, 2023

This will yield a new dataset the we can feed to any other standard Machine or Deep Learning model. Conclusions In this articles we showcased a basic pipeline using Scikit-LLM and had a glimpse on the performance of ChatGPT in a Zero-Shot learning task. We can try to plot the distribution of our dataset using the t-SNE.

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The MLOps Blog

APRIL 5, 2023

See also MLOps Problems and Best Practices Addressing model environments Use ONNX ONNX ( Open Neural Network Exchange) | Source ONNX (Open Neural Network Exchange), an open-source format for representing deep learning models, was developed by Microsoft and is now managed by the Linux Foundation. Thanks for reading, and keep learning!

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