Viso.ai

MAY 7, 2024

GoogLeNet’s deep learning model was deeper than all the previous models released, with 22 layers in total. Increasing the depth of the Machine Learning model is intuitive, as deeper models tend to have more learning capacity and as a result, this increases the performance of a model.

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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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Viso.ai

NOVEMBER 27, 2024

When the first YOLO was developed by Joseph Redmon and Ali Farhadi back in 2016, it overcame most problems with traditional object detection algorithms, with a new and enhanced architecture. Improved Explainability : Making the model’s decision-making process more transparent. Architecture The Architecture of YOLOv1.

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John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. For visual question answering in Deep Learning using NLP, public datasets play a crucial role. Is aqua the maximum?

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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. Preprint posted online June 21, 2016. New York, NY: W.W. link] 7 Ngo R.

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NVIDIA

JANUARY 16, 2024

So began the organization’s trustworthy AI efforts, in 2016, to accelerate its work using ASR. “No To collect speech data in a transparent, ethically compliant, community-oriented way, Te Hiku Media began by explaining its cause to elders, garnering their support and asking them to come to the station to read phrases aloud. “It

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NVIDIA

JUNE 5, 2023

Opening the Lab Doors In 2016, Girone was named CTO of CERN openlab, a group that gathers academic and industry researchers to accelerate innovation and tackle future computing challenges. In their presentations, physicists explained the challenges ahead. “By Industry participation was strong and enthusiastic about the technology.

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Pickl AI

APRIL 8, 2024

Example In 2016, an investigation by ProPublica revealed that a risk assessment algorithm used in US courts to predict recidivism rates was biased against Black defendants. Example In 2016, a chatbot developed by Microsoft called Tay was launched on Twitter. How Can We Ensure the Transparency of AI Systems?

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Marktechpost

FEBRUARY 17, 2025

The study’s bibliometric analysis revealed a steady increase in AI safety research since 2016, driven by advancements in deep learning. A word cloud analysis highlighted key themes such as safe reinforcement learning, adversarial robustness, and domain adaptation.

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Explosion

JANUARY 12, 2018

We founded Explosion in October 2016, so this was our first full calendar year in operation. Highlights included: Developed new deep learning models for text classification, parsing, tagging, and NER with near state-of-the-art accuracy. spaCy’s Machine Learning library for NLP in Python. Here’s what we got done.

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Heartbeat

JUNE 14, 2023

1] explain that the reliability diagram is plotted as follows. Conclusion In this article, we introduced the concept of calibration in deep neural networks. Finally, we explained about a few calibration techniques that can enable neural networks to output reliable and interpretable confidence estimates. 6] Zhang, Hongyi, et al.

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ML Review

JUNE 4, 2018

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. Source : Britz (2016)[ 62 ] CNNs can encode abstract features from images. 2014)[ 73 ] and Donahue et al.

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Heartbeat

MAY 11, 2023

The first version of YOLO was introduced in 2016 and changed how object detection was performed by treating object detection as a single regression problem. But just because we have all these YOLOs doesn’t mean that deep learning for object detection is a dormant area of research. We pay our contributors, and we don’t sell ads.

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TheSequence

DECEMBER 13, 2023

My path to working in AI is somewhat unconventional and began when I was wrapping up a postdoc in theoretical particle physics around 2016. I was surprised to learn that a few lines of code could outperform features that had been carefully designed by physicists over many years. TheSequence is a reader-supported publication.

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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., Goodfellow, I.

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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

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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. 2016 ), only the activations at the boundaries of each partition are saved and shared between workers during training.

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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. The Companys net income attributable to the Company for the year ended December 31, 2016 was $4,816,000, or $0.28

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Pickl AI

JULY 24, 2024

The advent of big data, coupled with advancements in Machine Learning and deep learning, has transformed the landscape of AI. Techniques such as neural networks, particularly deep learning, have enabled significant breakthroughs in image and speech recognition, natural language processing, and autonomous systems.

Artificial Intelligence 52

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AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). There are a number of theories that try to explain this effect: When tensor updates are big in size, traffic between workers and the parameter server can get congested. International Conference on Machine Learning. PMLR, 2018. [2]

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deepsense.ai

MAY 7, 2023

We talked about diffusion in deep learning, models that utilize it to generate images, and several ways of fine-tuning it to customize your generative model. We also explained the building blocks of Stable Diffusion and highlighted why its release last year was such a groundbreaking achievement. But don’t worry!

Neural Network 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. Benchmark Results V.

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Heartbeat

AUGUST 23, 2023

Natural language processing and machine learning for law and policy texts. Artificial intelligence in law: The state of play 2016. 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.

NLP 52

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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. The Companys net income attributable to the Company for the year ended December 31, 2016 was $4,816,000, or $0.28

NLP 52

NLP LLM Deep Learning ML 52

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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Heartbeat

FEBRUARY 2, 2024

In xxAI — Beyond Explainable AI Chapter. Deep residual learning for image recognition. 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. Salewski, L.,

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Sebastian Ruder

MAY 22, 2020

While a PhD encourages you to delve deep into a specific topic, you can add value by making connections between different topics or entirely different fields. Many ideas in deep learning take inspiration from other fields such as biology ( Hinton et al., 2016 ), physics ( Cohen et al., 2014 ), neuroscience ( Wang et al.,

Natural Language Processing 52

Natural Language Processing NLP ML Deep Learning 52

Snorkel AI

MAY 3, 2023

This is a model trained on the Berkeley Deep Drive-100k dataset , performing bounding box tracking on the road. Whenever we see videos like this, we may get this overly positive impression of how remarkable deep learning models are, which is true in some cases. To explain what I mean, let’s revisit this example.

Machine Learning 52

Machine Learning Neural Network Explainability Data Scientist 52

Snorkel AI

MAY 3, 2023

This is a model trained on the Berkeley Deep Drive-100k dataset , performing bounding box tracking on the road. Whenever we see videos like this, we may get this overly positive impression of how remarkable deep learning models are, which is true in some cases. To explain what I mean, let’s revisit this example.

Machine Learning 52

Machine Learning Neural Network Explainability Data Scientist 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

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Probably Approximately a Scientific Blog

AUGUST 14, 2018

Since I’ve started this blog 3 years ago, I’ve been refraining from writing about deep learning (DL), with the exception of occasionally discussing a method that uses it, without going into details. It’s a challenge to explain deep learning using simple concepts and without the caveat of remaining at a very high level.

Deep Learning 52

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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.

Neural Network 52

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Unite.AI

NOVEMBER 28, 2024

GPUs, originally developed for rendering graphics, became essential for accelerating data processing and advancing deep learning. 2010s – Cloud Computing, Deep Learning, and Winning Go With the advent of cloud computing and breakthroughs in deep learning , AI reached unprecedented heights.

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deepsense.ai

MARCH 28, 2023

Within the context of deep learning, diffusion refers to one of the processes used by these methods to generate images based on the training data. Below we will explain each of these in a bit more detail. 2020 [link] Learning Transferable Visual Models From Natural Language Supervision , Radford et al.

Neural Network 52

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AWS Machine Learning Blog

JANUARY 17, 2024

The underlying Deep Learning Container (DLC) of the deployment is the Large Model Inference (LMI) NeuronX DLC. He retired from EPFL in December 2016.nnIn He focuses on developing scalable machine learning algorithms. Qing has in-depth knowledge on the infrastructure optimization and Deep Learning acceleration.

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Unite.AI

NOVEMBER 29, 2023

Looking back at the recent past, the 2016 US presidential election result makes us explore what influenced voters' decisions. AI watchdogs employ state-of-the-art technologies, particularly machine learning and deep learning algorithms, to combat the ever-increasing amount of election-related false information.

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AWS Machine Learning Blog

MAY 10, 2023

For example, using a large language model, Jupyter AI can help a programmer generate, debug, and explain their source code. This distribution includes deep learning frameworks like PyTorch, TensorFlow, and Keras; popular Python packages like NumPy, scikit-learn, and pandas; and IDEs like JupyterLab and the Jupyter Notebook.

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