Flipboard

MARCH 13, 2023

OpenAI has pioneered a technique to shape its models’ behaviors using something called reinforcement learning with human feedback (RLHF). Having a human periodically check on the reinforcement learning system’s output and give feedback allows reinforcement learning systems to learn even when the reward function is hidden. “I’m

ChatGPT 172

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Unite.AI

JUNE 22, 2023

Two years later, in 2011, I co-founded Crashlytics, a mobile crash reporting tool which was acquired by Twitter in 2013 and then again by Google in 2017. We're so excited about Digits AI because it seamlessly combines the strengths of both major fields in machine learning: generative large language models and predictive similarity models.

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Large Language Models Machine Learning ChatGPT Deep Learning 325

Unite.AI

MAY 8, 2024

He began his career at Yandex in 2017, concurrently studying at the Yandex School of Data Analysis. There, I learned a lot about more advanced machine learning algorithms and built my intuition. The most crucial point during this process was when I learned about neural networks and deep learning.

Machine Learning 298

Machine Learning Software Engineer Large Language Models Data Analysis 298

Explosion

FEBRUARY 12, 2017

In this post, I'll explain how to solve text-pair tasks with deep learning, using both new and established tips and technologies. Quora recently released the first dataset from their platform: a set of 400,000 question pairs, with annotations indicating whether the questions request the same information.

Deep Learning 40

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Viso.ai

JULY 17, 2024

Output from Neural Style Transfer – source Neural Style Transfer Explained Neural Style Transfer follows a simple process that involves: Three images, the image from which the style is copied, the content image, and a starting image that is just random noise. With deep learning, the results were impressively good.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

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.

Computer Vision 97

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Heartbeat

JUNE 14, 2023

1] explain that the reliability diagram is plotted as follows. Finally, we explained about a few calibration techniques that can enable neural networks to output reliable and interpretable confidence estimates. International conference on machine learning. PMLR, 2017. [2] arXiv preprint arXiv:1710.09412 (2017). [7]

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Heartbeat

OCTOBER 24, 2023

Open Neural Network Exchange, or ONNX, is a free and open-source ecosystem for deep learning model representation. Facebook and Microsoft created this tool in 2017 to make it simpler for academics and engineers to migrate models between various deep-learning frameworks and hardware platforms.

Neural Network 52

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Viso.ai

OCTOBER 7, 2024

This means more accurate results and automated machine-learning processes mainly because human domain knowledge exceeds that of machine learning. In this comprehensive overview, we will explore human-in-the-loop machine learning for computer vision tasks. This streamlines training and saves time.

Computer Vision 52

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Flipboard

MARCH 27, 2023

torch.compile Over the last few years, PyTorch has evolved as a popular and widely used framework for training deep neural networks (DNNs). Since the launch of PyTorch in 2017, it has strived for high performance and eager execution. In this series, you will learn about Accelerating Deep Learning Models with PyTorch 2.0.

Neural Network 158

Neural Network Convolutional Neural Networks BERT Deep Learning 158

Explosion

FEBRUARY 12, 2017

In this post, I’ll explain how to solve text-pair tasks with deep learning, using both new and established tips and technologies. The SNLI dataset is over 100x larger than previous similar resources, allowing current deep-learning models to be applied to the problem.

Neural Network 40

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AI Impacts

FEBRUARY 1, 2023

But this model, on its own, is inadequate for AI, for reasons I will explain in the next section. 5 I will not explain this problem in detail, but I will list some aspects of it here, along with real-world examples, and you can read more about it elsewhere. The alignment problem from a deep learning perspective.

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Pickl AI

APRIL 8, 2024

Example A 2017 study by McKinsey Global Institute estimated that automation could displace up to 800 million jobs globally by 2030. Example In 2017, re searchers demonstrated how a self-driving car could be tricked into making a dangerous turn by placing a strategically placed sticker on a stop sign.

Artificial Intelligence 52

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Mlearning.ai

MARCH 31, 2023

In this story, we talk about how to build a Deep Learning Object Detector from scratch using TensorFlow. The output layer is set to use Softmax Activation Function as usual in Deep Learning classifiers. That time, tensorflow/pytorch and the Deep Learning technology were not ready yet.

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The Stanford AI Lab Blog

FEBRUARY 24, 2022

In the rest of this blogpost, we’ll share our theoretical analysis explaining why this is the case, showing that retraining in self-training provably improves accuracy compared to the original pseudo-labeler. 2017 , Shu et al., Our theoretical analysis focuses on pseudo-label-based self-training, but there are also other variants.

Deep Learning 40

Deep Learning Algorithm Explainability 40

PyImageSearch

JULY 3, 2023

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. Artwork Personalization at Netflix,” Netflix Technology Blog , 2017 ). Figure 9: Regret in batch-based machine learning. user profile, location, query, language, etc.).

Computer Vision 52

Computer Vision Deep Learning Algorithm Machine Learning 52

Heartbeat

NOVEMBER 8, 2023

This is where Comet comes in as a versatile machine learning experiment management platform that can help manage machine learning projects. In this article, you will learn about the benefits of using Comet. It provides a single platform for managing machine learning experiments. What is Comet?

Machine Learning 52

Machine Learning Data Scientist Deep Learning ML 52

ML Review

MARCH 31, 2019

Up to this point, machine learning algorithms simply didn’t work well enough for anyone to be surprised when it failed to do the right thing. Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. Kurakin et al, ICLR 2017. Kurakin et al, ICLR 2017. Let’s look at an example. confidence.

Neural Network 52

Neural Network Machine Learning Deep Learning Explainability 52

Mlearning.ai

MARCH 9, 2023

Recent studies have demonstrated that deep learning-based image segmentation algorithms are vulnerable to adversarial attacks, where carefully crafted perturbations to the input image can cause significant misclassifications (Xie et al., Explaining and harnessing adversarial examples. 2018; Sitawarin et al., 2015; Huang et al.,

Computer Vision 52

Computer Vision Deep Learning Machine Learning Algorithm 52

deepsense.ai

APRIL 16, 2023

This allows for the efficient processing of large amounts of data and can significantly reduce the time required for training deep learning models. First, we will explain the MLP block. 2017) is ensemble learning. Figure 1: Illustration of data parallelism. billion parameters.

Large Language Models 52

Large Language Models Neural Network Deep Learning Explainability 52

Heartbeat

FEBRUARY 6, 2023

arXiv preprint arXiv:1701.06659 (2017). İrem KÖMÜRCÜ Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. Top: SSD, Bottom: DSSD, Source: Fu, Cheng-Yang, et al.

Algorithm 90

Algorithm Convolutional Neural Networks Neural Network Computer Vision 90

Heartbeat

DECEMBER 8, 2023

He focused on generative AI trained on large language models, The strength of the deep learning era of artificial intelligence has lead to something of a renaissance in corporate R&D in information technology, according to Yann LeCun, chief AI. Hinton is viewed as a leading figure in the deep learning community.

Convolutional Neural Networks 52

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Mlearning.ai

MARCH 2, 2023

Moreover, the most important theoretical foundations for BERT are explained and additional graphics are provided for illustration purposes. Conclusion: BERT as Trend-Setter in NLP and Deep Learning References I. Finally, the impact of the paper and applications of BERT are evaluated from today’s perspective.

BERT 52

BERT NLP Deep Learning Neural Network 52

Heartbeat

AUGUST 2, 2023

Introduction to LLMs LLM in the sphere of AI Large language models (often abbreviated as LLMs) refer to a type of artificial intelligence (AI) model typically based on deep learning architectures known as transformers. They are usually trained on a massive amount of text data. We pay our contributors, and we don’t sell ads.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering OpenAI 52

Applied Data Science

JANUARY 4, 2022

At the very least, we hope that by reading this list you can cross-out “Learning about the state of AI in 2021” from your resolution list ?. ? Transformers taking the AI world by storm The family of artificial neural networks (ANNs) saw a new member being born in 2017, the Transformer. What happens when you combine the two?

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 52

Heartbeat

JANUARY 23, 2023

Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. The following code provide homepage, citation, info, license and description. print("1-",qqp["train"].homepage)

Natural Language Processing 52

Natural Language Processing NLP Deep Learning Machine Learning 52

Mlearning.ai

OCTOBER 19, 2023

In this article, I’ll explain how to use deep learning techniques to achieve images super-resolution, and provide a brief overview of the diffusion model ☁️ What is Super Resolution? Image Super-Resolution refers to the process of enhancing the resolution of an image from low resolution (LR) to high (HR). Caballero, J.,

Computer Vision 52

Computer Vision Deep Learning Explainability LLM 52

Mlearning.ai

MAY 12, 2023

Deployment of deep neural network on mobile phone. (a) Introduction Many deep learning models, like CNNs, Transformers, etc., NCNN is lightweight and efficient, making it an ideal choice for deploying deep learning models on devices with limited computational resources. 1], (d) image by Shiwa ID on Unsplash.

Neural Network 52

Neural Network Deep Learning Computer Vision Large Language Models 52

Heartbeat

JULY 20, 2023

Limited availability of labeled datasets: In some domains, there is a scarcity of datasets with fine-grained annotations, making it difficult to train segmentation networks using supervised learning algorithms. When evaluated on the MS COCO dataset test-dev 2017, YOLOv8x attained an impressive average precision (AP) of 53.9%

Computer Vision 52

Computer Vision Machine Learning Deep Learning Algorithm 52

AWS Machine Learning Blog

FEBRUARY 10, 2023

In this post, we deep dive into the technical details of this ML model. We describe how we designed an accurate, explainable ML model to make coverage classification from player tracking data, followed by our quantitative evaluation and model explanation results. Advances in neural information processing systems 30 (2017).

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Computer Vision Machine Learning ML Convolutional Neural Networks 80

John Snow Labs

JUNE 6, 2023

Text classification with transformers refers to the application of deep learning models based on the transformer architecture to classify sequences of text into predefined categories or labels. This allows it to learn general patterns and relationships in the language that can be useful for a wide range of downstream NLP tasks.

BERT 52

BERT Python NLP Neural Network 52

ML Review

JUNE 4, 2018

2017)[ 51 ] Introduction to Image Captioning Suppose that we asked you to caption an image; that is to describe the image using a sentence. These ideas also move in step with the explainability of results. However, in 2014 a number of high-profile AI labs began to release new approaches leveraging deep learning to improve performance.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

Heartbeat

OCTOBER 9, 2023

Artificial Intelligence (AI) Integration: AI techniques, including machine learning and deep learning, will be combined with computer vision to improve the protection and understanding of cultural assets. Gonzalez and Richard E. We're committed to supporting and inspiring developers and engineers from all walks of life.

Computer Vision 52

Computer Vision Algorithm Deep Learning Machine Learning 52

TheSequence

NOVEMBER 22, 2023

In 2013, my team started exploring building neural network accelerators on FPGAs, and in 2015 we ramped that effort to take it to production as Project Brainwave , which shipped into production at large scale in 2017, accelerating neural network inference for Bing and Office. One is a more formal view of explainability.

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Neural Network Generative AI AI AI 40

Sebastian Ruder

JANUARY 6, 2020

While it has its limitations, i.e. practical neural networks still perform better than their NTK counterparts, and insights so far have not translated into empirical gains, it may help us open the black box of deep learning. 2017 ) architecture. However, our current methods to explain models' predictions are still limited.

NLP 52

NLP ML Neural Network BERT 52

Sebastian Ruder

JANUARY 24, 2022

  While pre-trained transformers will likely continue to be deployed as standard baselines for many tasks, we should expect to see alternative architectures particularly in settings where current models fail short, such as modeling long-range dependencies and high-dimensional inputs or where interpretability and explainability are required.

NLP 52

NLP ML BERT Computational Linguistics 52

ML Review

MAY 3, 2018

Much the same way we iterate, link and update concepts through whatever modality of input our brain takes — multi-modal approaches in deep learning are coming to the fore. While an oversimplification, the generalisability of current deep learning approaches is impressive.

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