Mlearning.ai

JANUARY 17, 2024

Major milestones in the last few years comprised BERT (Google, 2018), GPT-3 (OpenAI, 2020), Dall-E (OpenAI, 2021), Stable Diffusion (Stability AI, LMU Munich, 2022), ChatGPT (OpenAI, 2022). Deep learning neural network. In the code, the complete deep learning network is represented as a matrix of weights.

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Sebastian Ruder

JANUARY 6, 2020

 Unsupervised pretraining was prevalent in NLP this year, mainly driven by BERT ( Devlin et al., A whole range of BERT variants have been applied to multimodal settings, mostly involving images and videos together with text (for an example see the figure below). 2019 ; Tran, 2020 ), which can be seen below. 2019 ; Wu et al.,

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Sebastian Ruder

NOVEMBER 14, 2022

Research models such as BERT and T5 have become much more accessible while the latest generation of language and multi-modal models are demonstrating increasingly powerful capabilities. This post is partially based on a keynote I gave at the Deep Learning Indaba 2022. The Deep Learning Indaba 2022 in Tunesia.

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Heartbeat

JUNE 6, 2023

Prerequisites To follow along with this tutorial, you will need the following: Basic knowledge of Python and deep learning. We will construct a graph based on the citation links between the papers and use GCNs to classify the papers. Some familiarity with PyTorch and Comet, as these are the tools we will use to implement the GCN.

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Viso.ai

JUNE 12, 2024

Our software helps several leading organizations start with computer vision and implement deep learning models efficiently with minimal overhead for various downstream tasks. GPT models are based on transformer-based deep learning neural network architecture. About us : Viso.ai Get a demo here.

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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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Lexalytics

APRIL 5, 2021

They were not wrong: the results they found about the limitations of perceptrons still apply even to the more sophisticated deep-learning networks of today. And indeed we can see other machine learning topics arising to take their place, like “optimization” in the mid-’00s, with “deep learning” springing out of nowhere in 2012.

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Viso.ai

FEBRUARY 25, 2023

No 2018 Oct BERT Pre-trained transformer models started dominating the NLP field. No 2020 May DETR DETR is a simple yet effective framework for high-level vision that views object detection as a direct set prediction problem. No 2020 Jul iGPT The transformer model, originally developed for NLP, can also be used for image pre-training.

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Sebastian Ruder

JANUARY 24, 2022

6] such as W2v-BERT [7] as well as more powerful multilingual models such as XLS-R [8]. For each input chunk, nearest neighbor chunks are retrieved using approximate nearest neighbor search based on BERT embedding similarity. A framework for self-supervised learning of speech representations. What happened?   wav2vec 2.0:

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Probably Approximately a Scientific Blog

JANUARY 12, 2021

This long-overdue blog post is based on the Commonsense Tutorial taught by Maarten Sap, Antoine Bosselut, Yejin Choi, Dan Roth, and myself at ACL 2020. Here, BERT has seen in its training corpus enough sentences of the type "The color of something is [color]" to know to suggest different colors as substitutes for the masked word.

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The Stanford AI Lab Blog

JANUARY 21, 2022

Deep learning has enabled improvements in the capabilities of robots on a range of problems such as grasping 1 and locomotion 2 in recent years. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. RoBERTa: A Robustly Optimized BERT Pretraining Approach. Toutanova, K. Goyal, N.,

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Snorkel AI

APRIL 11, 2023

The unprecedented amount of available data has been critical to many of deep learning’s recent successes, but this big data brings its own problems. Active learning is a really powerful data selection technique for reducing labeling costs. First, “Selection via Proxy,” which appeared in ICLR 2020.

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Snorkel AI

APRIL 11, 2023

The unprecedented amount of available data has been critical to many of deep learning’s recent successes, but this big data brings its own problems. Active learning is a really powerful data selection technique for reducing labeling costs. First, “Selection via Proxy,” which appeared in ICLR 2020.

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Snorkel AI

APRIL 11, 2023

The unprecedented amount of available data has been critical to many of deep learning’s recent successes, but this big data brings its own problems. Active learning is a really powerful data selection technique for reducing labeling costs. First, “Selection via Proxy,” which appeared in ICLR 2020.

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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. It is not surprising that it has become a major application area for deep learning.

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Pickl AI

NOVEMBER 26, 2024

billion in 2020 to an expected $152.61 TensorFlow and PyTorch are the most commonly use libraries for deep learning, offering robust support for RNNs and other neural network architectures. GPT , BERT) and other complex tasks. As the global neural network market expands—from $14.35

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Topbots

JUNE 15, 2023

Are All Languages Created Equal in Multilingual BERT? In Proceedings of the 5th Workshop on Representation Learning for NLP , pages 120–130, Online. Advances in neural information processing systems 33 (2020): 1877–1901. In Findings of the Association for Computational Linguistics: ACL 2022 , pages 2340–2354, Dublin, Ireland.

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Marek Rei

NOVEMBER 4, 2024

They annotate a new test set of news data from 2020 and find that performance of certain models holds up very well and the field luckily hasn’t overfitted to the CoNLL 2003 test set. Mind the gap: Challenges of deep learning approaches to Theory of Mind Jaan Aru, Aqeel Labash, Oriol Corcoll, Raul Vicente. ArXiv 2022.

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Jay Alammar

DECEMBER 16, 2020

We see plenty of room to explore further methods and interfaces that improve the transparency of deep learning models including Transformer-based models. Retrieved from [link] BibTex: @misc{alammar2020explaining, title={Interfaces for Explaining Transformer Language Models}, author={Alammar, J}, year={2020}, url={[link] }

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deepsense.ai

NOVEMBER 14, 2023

2023 [link] [link] [link] BERTScore: Evaluating text generation with BERT , Zhang T., 2020 Better automatic evaluation of open-domain dialogue systems with contextualized embeddings , Ghazarian S., 2004 BLEURT: Learning robust metrics for text generation , Sellam T., Garrido-Merchán E.C., Hertzmann A., Kishore V., Eskanazi M.,

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Unite.AI

AUGUST 8, 2023

These advanced AI deep learning models have seamlessly integrated into various applications, from Google's search engine enhancements with BERT to GitHub’s Copilot, which harnesses the capability of Large Language Models (LLMs) to convert simple code snippets into fully functional source codes.

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Unite.AI

SEPTEMBER 26, 2024

It all started in 2012 with AlexNet, a deep learning model that showed the true potential of neural networks. Then, in 2015, Google released TensorFlow, a powerful tool that made advanced machine learning libraries available to the public. The necessary hardware, software, and data storage costs were very high.

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