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.

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Data Science Blog

MARCH 14, 2023

Die vollautomatisierte Analyse von textlicher Sprache, von Fotos oder Videomaterial war 2015 noch Nische, gehört heute jedoch zum Alltag hinzu. Während 2015 noch von neuen Geschäftsmodellen mit Big Data geträumt wurde, sind Data as a Service und AI as a Service heute längst Realität!

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DagsHub

JULY 25, 2024

Source: Author Introduction Deep learning, a branch of machine learning inspired by biological neural networks, has become a key technique in artificial intelligence (AI) applications. Deep learning methods use multi-layer artificial neural networks to extract intricate patterns from large data sets.

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Hacker News

OCTOBER 11, 2024

Deep learning — a software model that relies on billions of neurons and trillions of connections — requires immense computational power. What once seemed like science fiction — computers learning and adapting from vast amounts of data — was now a reality, driven by the raw power of GPUs.

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Towards AI

APRIL 23, 2024

Neural Style Transfer (NST) was born in 2015 [2], slightly later than GAN. It is one of the first algorithms to combine images based on deep learning. However, generative models is not a new term and it has come a long way since Generative Adversarial Network (GAN) was published in 2014 [1].

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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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Flipboard

NOVEMBER 16, 2024

Raw images are processed and utilized as input data for a 2-D convolutional neural network (CNN) deep learning classifier, demonstrating an impressive 95% overall accuracy against new images. The glucose predictions done by CNN are compared with ISO 15197:2013/2015 gold standard norms.

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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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Flipboard

JUNE 20, 2023

Deep learning Deep learning is a specific type of machine learning used in the most powerful AI systems. It imitates how the human brain works using artificial neural networks (explained below), allowing the AI to learn highly complex patterns in data.

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Towards AI

JULY 8, 2024

Introduction to AI Accelerators AI accelerators are specialized hardware designed to enhance the performance of artificial intelligence (AI) tasks, particularly in machine learning and deep learning. Sometime later, around 2015, the focus of CUDA transitioned towards supporting neural networks.

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Explosion

NOVEMBER 9, 2016

now features deep learning models for named entity recognition, dependency parsing, text classification and similarity prediction based on the architectures described in this post. You can now also create training and evaluation data for these models with Prodigy , our new active learning-powered annotation tool. Bowman et al.

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Flipboard

SEPTEMBER 4, 2023

Container runtimes are consistent, meaning they would work precisely the same whether you’re on a Dell laptop with an AMD CPU, a top-notch MacBook Pro , or an old Intel Lenovo ThinkPad from 2015. These images also support interfacing with the GPU, meaning you can leverage it for training your Deep Learning networks written in TensorFlow.

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AWS Machine Learning Blog

OCTOBER 5, 2023

He focuses on developing scalable machine learning algorithms. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering. Yida Wang is a principal scientist in the AWS AI team of Amazon. He founded StylingAI Inc.,

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Flipboard

JULY 31, 2023

Object detection works by using machine learning or deep learning models that learn from many examples of images with objects and their labels. In the early days of machine learning, this was often done manually, with researchers defining features (e.g., Object detection is useful for many applications (e.g.,

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Towards AI

JULY 19, 2023

Deep learning algorithms can be applied to solving many challenging problems in image classification. Therefore, Now we conquer this problem of detecting the cracks using image processing methods, deep learning algorithms, and Computer Vision. 196–210, 2015. irregular illuminated conditions, shading, and blemishes.

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Towards AI

FEBRUARY 3, 2024

PLoS ONE 10(7), e0130140 (2015) [2] Montavon, G., eds) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. .: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. Lapuschkin, S., Müller, KR. Layer-Wise Relevance Propagation: An Overview. In: Samek, W.,

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Towards AI

FEBRUARY 27, 2024

The whole machine learning industry since the early days was growing on open source solutions like scikit learn (2007) and then deep learning frameworks — TensorFlow (2015) and PyTorch (2016). More than 99% of Fortune 500 companies use open-source code [2].

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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., 2015; Huang et al., an image) with the intention of causing a machine learning model to misclassify it (Goodfellow et al., 2012; Otsu, 1979; Long et al.,

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Towards AI

APRIL 19, 2024

Intelligent Medical Objects 👉Industry domain: AI, Health Tech, IT, NLP, Software, Analytics, Generative AI 👉Location: 3 offices 👉Year founded: 1994 👉Programming languages deployed: Angular, C#, SQL, Scikit, TensorFlow, Spark, GitHub, R, Python 👉Benefits: Flexible time off, family medical leave, pet insurance, (..)

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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

FEBRUARY 14, 2023

OpenAI, on the other hand, is an AI research laboratory that was founded in 2015. The first step is to learn the basics of machine learning and deep learning, which are the technologies that underpin generative AI. How to Get Started With Generative AI?

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Towards AI

AUGUST 11, 2023

Development in Deep Learning was at its golden state. A small startup named OpenAI got formed then after a year, in Dec 2015. Facebook then, on the other hand, was creating a system that could predict if two picture showed the same person.

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Towards AI

JULY 23, 2023

Semi-Supervised Sequence Learning As we all know, supervised learning has a drawback, as it requires a huge labeled dataset to train. In 2015, Andrew M. In the NLP domain, it has been a challenge to procure large amounts of data, to train a model, in order for the model to get proper context and embeddings of words.

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Ines Montani

AUGUST 6, 2024

Around 2015 when deep learning was widely adopted and conversational AI became more viable, the industry got very excited about chat bots. So whenever you’re tasked with developing a system to replace and automate a human task, ask yourself: Am I building a window-knocking machine or an alarm clock?

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Pickl AI

AUGUST 22, 2024

Introduction Deep Learning frameworks are crucial in developing sophisticated AI models, and driving industry innovations. By understanding their unique features and capabilities, you’ll make informed decisions for your Deep Learning applications.

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Mlearning.ai

JUNE 16, 2023

The common practice for developing deep learning models for image-related tasks leveraged the “transfer learning” approach with ImageNet. December 14, 2015. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.” [link] [4] Huh, Minyoung, Pulkit Agrawal, and Alexei A. April 14, 2015.

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ML Review

FEBRUARY 26, 2018

Launched in July 2015, AliMe is an IHCI-based shopping guide and assistant for e-commerce that overhauls traditional services, and improves the online user experience. Chitchatting, such as “I’m in a bad mood”, pulls up a method that marries the retrieval model with deep learning (DL). 5] Mnih V, Badia A P, Mirza M, et al.

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AWS Machine Learning Blog

OCTOBER 9, 2023

He has 8 years of experience building out a variety of deep learning and other AI use cases and focuses on Personalization and Recommendation use cases with AWS. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. References [1] Maxwell Harper and Joseph A. DOI= [link]

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Pickl AI

JANUARY 23, 2023

It is mainly used for deep learning applications. PyTorch PyTorch is a popular, open-source, and lightweight machine learning and deep learning framework built on the Lua-based scientific computing framework for machine learning and deep learning algorithms. It also allows distributed training.

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ML Review

MARCH 31, 2019

Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. Source: Robust Physical-World Attacks on Deep Learning Visual Classification. Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. Robust Physical-World Attacks on Deep Learning Visual Classification.

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ML Review

JUNE 4, 2018

However, in 2014 a number of high-profile AI labs began to release new approaches leveraging deep learning to improve performance. An example of such an approach is seen in the work of Karpathy and Fei-Fei (2015)[ 78 ]. Largely due to the limits of heuristics or approximations for word-object relationships[ 52 ][ 53 ][ 54 ].

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Heartbeat

DECEMBER 20, 2023

Understanding why ResNet is essential, its innovative aspects, and what it enables in deep learning forms a crucial part of our exploration. This comparison is academic and practical, offering deep learning practitioners insights into choosing the right architecture based on specific project needs.

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Explosion

NOVEMBER 21, 2019

In 2016 we trained a sense2vec model on the 2015 portion of the Reddit comments corpus, leading to a useful library and one of our most popular demos. Try the new interactive demo to explore similarities and compare them between 2015 and 2019 sense2vec (Trask et. Interestingly, “to ghost” wasn’t very common in 2015.

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NYU Center for Data Science

SEPTEMBER 28, 2023

Cho’s work on building attention mechanisms within deep learning models has been seminal in the field. Research Highlights He, Linzen, and Sedoc provided examples of the diversity of research focuses at ML².

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AWS Machine Learning Blog

APRIL 18, 2023

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. per diluted share, for the year ended December 31, 2015. per diluted share, for the year ended December 31, 2015.

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PyImageSearch

JULY 3, 2023

Figure 4: The Netflix personalized home page generation problem (source: Alvino and Basilico, “Learning a Personalized Homepage,” Netflix Technology Blog , 2015 ). These features can be simple metadata or model-based features (extracted from a deep learning model), representing how good that video is for a member.

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Pickl AI

FEBRUARY 14, 2023

Significantly, by leveraging technologies like deep learning and proprietary algorithms for analytics, Artivatic.ai Arya.ai One of the growing AI companies in India, Arya.ai, deploys Deep Learning solutions for the BFSI sector. launched its meta framework on TensorFlow in 2015. Artivatic.ai Artivatic.ai

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