Marktechpost

MAY 23, 2024

Generative Adversarial Networks: Creating Realistic Synthetic Data Generative Adversarial Networks, introduced by Ian Goodfellow in 2014, are a class of machine-learning frameworks designed for generative tasks. Finance: RL models optimize strategies for buying and selling assets to maximize returns in trading.

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Mlearning.ai

AUGUST 4, 2023

In this blog, we will try to deep dive into the concept of 1x1 convolution operation which appeared in the paper ‘Network in Network’ by Lin et al in (2013) and ‘Going Deeper with Convolutions’ by Szegedy et al (2014) that proposed the GoogLeNet architecture. References: [link] [link] [link] WRITER at MLearning.ai // Control AI Video ?

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Unite.AI

FEBRUARY 16, 2024

In 2014, you launched Cubic.ai, one of the first smart speakers and voice-assistant apps for smart homes. in 2014 and brought my family with me. Children download the App and convince parents to pay for a subscription, explaining that Buddy is a teacher. What were some of your key takeaways from this experience?

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Natural Language Processing AI AI UX Design 170

Viso.ai

MAY 7, 2024

GoogLeNet, released in 2014, set a new benchmark in object classification and detection through its innovative approach (achieving a top-5 error rate of 6.7%, nearly half the error rate of the previous year’s winner ZFNet with 11.7%) in ImageNet Large Scale Visual Recognition Challenge (ILSVRC).

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Viso.ai

JULY 9, 2024

StyleGAN is GAN (Generative Adversarial Network), a Deep Learning (DL) model, that has been around for some time, developed by a team of researchers including Ian Goodfellow in 2014. Since the development of GANs, the world saw several models introduced every year that got nearer to generating real images.

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Explainability Computer Vision Convolutional Neural Networks Neural Network 52

NVIDIA

SEPTEMBER 6, 2023

Pioneering AI in Physics In 2014, her life’s work took her more than 7,000 miles from her Shanghai home to Princeton University’s prestigious plasma physics lab, where she earned a Ph.D. Then he explained why he wanted to take an approach, popular among researchers, of using high-temperature superconducting magnets to control the plasma.

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AssemblyAI

OCTOBER 4, 2023

Answer: Taylor Swift released the song "Blank Space" on November 10, 2014. This means the chat completion service works, but as explained in the output, the model was trained with data up to October 2021, so the song "Anti-Hero" by Taylor Swift did not exist at the time. Don't explain your reasoning.

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LLM OpenAI Explainability Large Language Models 162

Becoming Human

MARCH 16, 2023

Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. Referred to as black boxes, such AI models might produce the most accurate predictions possible, but even engineers can’t explain the reasoning behind them. AI drug discovery is exploding.

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ODSC - Open Data Science

APRIL 19, 2023

In this ODSC talk , I’ll explain the core business skills covered in my book, illustrate why each is so critical for analytics professionals, and point out ways in which leaders can foster these skills within their analytics teams.

Data Scientist 84

Data Scientist Data Science Machine Learning Explainability 84

Towards AI

AUGUST 16, 2023

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps. MIT Press, ISBN: 978–0262028189, 2014. [2] References [1] E.

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Machine Learning Software Development Data Drift DevOps 74

AI Impacts

APRIL 11, 2023

Oldham, et al 2014. But this does not explain the lack of research, and one of the reasons given for opposition to experiments is that it has not been shown to be safe. 2031 (2014): 20140065. 4 Scientific papers published on solar radiation management by year. 5 Bolinger, Mark, Ryan Wiser, and Eric O'Shaughnessy.

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Towards AI

AUGUST 11, 2023

GANs are a part of the deep-learning world and were very introduced by Ian Goodfellow and his collaborators in 2014, After that GANs have rapidly captivated many researchers’ eyes which resulted in much research and also helped to redefine the boundaries of creativity and artificial intelligence in the world of AI 1.1

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ML Review

JUNE 4, 2018

These ideas also move in step with the explainability of results. Image captioning (circa 2014) Image captioning research has been around for a number of years, but the efficacy of techniques was limited, and they generally weren’t robust enough to handle the real world. 2014)[ 73 ] and Donahue et al.

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Topbots

JUNE 22, 2023

Also, since at least 2018, the American agency DARPA has delved into the significance of bringing explainability to AI decisions. Outstandingly, ChatPGT presents such a capacity: it can explain its decisions. Expert Systems with Applications (2014), 41(16):7653–7670. This capability helped me to give my ruling. Aghabozorgi, S.,

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Machine Learning ChatGPT Natural Language Processing Categorization 107

ML Review

MARCH 31, 2019

In 2014, a group of researchers at Google and NYU found that it was far too easy to fool ConvNets with an imperceivable, but carefully constructed nudge in the input. But by 2014, ConvNets had become powerful enough to start surpassing human accuracy on a number of visual recognition tasks. What are adversarial attacks? confidence.

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Mlearning.ai

MARCH 31, 2023

This article on roboflow does a great job in explaining this: [link] Getting Better Results Given the small size of training data (only 904 training instances!) In my professional life, in 2014–2017, I could work in a project to develop a real time object detector for production purposes. and model (only 3,235,014 parameters!)

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Deep Learning Machine Learning Python Categorization 98

Mlearning.ai

MARCH 9, 2023

Explaining and harnessing adversarial examples. Explaining and harnessing adversarial examples. However, these algorithms are vulnerable to adversarial attacks, where imperceptible perturbations to the input image can lead to significant misclassifications (Goodfellow et al., 2013; Goodfellow et al., Goodfellow, I. Goodfellow, I.

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Deep Learning Computer Vision Machine Learning Algorithm 52

Explosion

MAY 17, 2023

In 2014 I started working on spaCy , and here’s an excerpt of how I explained the motivation for the library: Computers don’t understand text. We all spend a big part of our working lives writing, reading, speaking and listening. This is unfortunate, because that’s what the web almost entirely consists of.

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LLM NLP Large Language Models OpenAI 135

AWS Machine Learning Blog

NOVEMBER 15, 2023

The following images show the output for “silver car.” The following image shows the output for “driving lane.” We can use this pipeline to build a visual chain. We compare the performance with respect to the object sizes (in proportion to image size)— small (area 1%).

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Prompt Engineer Prompt Engineering Computer Vision Machine Learning 110

Dlabs.ai

AUGUST 20, 2020

A Guide for Making Black Box Models Explainable Author: Christoph Molnar If you’re looking to learn how to make machine learning decisions interpretable, this is the eBook for you! It explains how to make machine learning algorithms work. Meaning you can download it for free, and if you find it useful, you can pay for this resource.

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Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 52

ML @ CMU

DECEMBER 29, 2023

Given a hyperparameter configuration ((lambda_s, lambda_c)), we train a model using the training clients (explained in section “FL Training”). In our work, we focus on an instantiation of FedOPT called FedAdam , which uses Adam (Kingma and Ba 2014) as ServerOPT and SGD as ClientOPT.

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Algorithm Machine Learning Explainability ML 86

AWS Machine Learning Blog

JUNE 3, 2024

To learn more about how SageMaker Canvas uses training and validation datasets, see Evaluating Your Model’s Performance in Amazon SageMaker Canvas and SHAP Baselines for Explainability. Your results may differ from those in this post. Machine learning introduces stochasticity in the model training process, which can lead to slight variations.

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Generative AI Categorization Auto-complete Auto-classification 108

The MLOps Blog

OCTOBER 20, 2023

In 2014, Project Jupyter evolved from IPython. There, you can use infographics, custom visualizations, and broader ways to explain your ideas. How to structure Jupyter notebook’s content In this section, I will explain the notebook layout I typically use. These tools gained significant adoption among researchers.

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Mlearning.ai

FEBRUARY 28, 2023

Year: More than half the cars in the data were manufactured in or after 2014. This is a clear indication that a good model is formed to explain variance in the price of used cars up to 87%. The log transformation was applied on this column to reduce skewness. Seats: 84% of the cars in the dataset are 5-seater cars. MAPE of 43.55

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AWS Machine Learning Blog

OCTOBER 17, 2023

According to a 2014 study, the proportion of severely lame cows in China can be as high as 31 percent. Summary In this article, we briefly explained how the AWS Customer Solutions team innovates quickly based on the customer’s business. Therefore, when lameness occurs, veterinarians should intervene as soon as possible.

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AWS Machine Learning Blog

JULY 13, 2023

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. Advances in neural information processing systems 27 (2014). So, why did distributing the training process affect model accuracy? 4] Dauphin, Yann N.,

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Algorithm Deep Learning Neural Network Machine Learning 71

deepsense.ai

MAY 7, 2023

We also explained the building blocks of Stable Diffusion and highlighted why its release last year was such a groundbreaking achievement. Source: [ 2 ] In the previous post, we explained the importance of Stable Diffusion [ 3 ]. 2014 Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks , Zhang et al.

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Neural Network Convolutional Neural Networks Explainability Deep Learning 52

Mlearning.ai

JUNE 8, 2023

Vector Embeddings for Developers: The Basics | Pinecone Used geometry concept to explain what is vector, and how raw data is transformed to embedding using embedding model. Pinecone Used a picture of phrase vector to explain vector embedding. What are Vector Embeddings?

Machine Learning 52

Machine Learning BERT Neural Network OpenAI 52

Mlearning.ai

APRIL 1, 2023

Doc2Vec was introduced in 2014 by a team of researchers led by Tomas Mikolov. We have explained the architectures of each model, as well as how to create and train them using the gensim library in Python. Doc2Vec learns vector representations of documents by combining the word vectors with a document-level vector.

Deep Learning 52

Deep Learning NLP Natural Language Processing Neural Network 52

Viso.ai

DECEMBER 6, 2023

The model secured first and second positions in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014. Future Directions Explainable AI: Explainable AI (XAI) is one research paradigm that can help you detect biases easily. VGGNet uses 3×3 filters to extract fundamental features from image data.

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Computer Vision Convolutional Neural Networks Neural Network Categorization 52

Heartbeat

AUGUST 21, 2023

GoogLeNet: is a highly optimized CNN architecture developed by researchers at Google in 2014. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments. We pay our contributors, and we don’t sell ads.

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Convolutional Neural Networks Neural Network Natural Language Processing Deep Learning 52

Heartbeat

FEBRUARY 27, 2023

Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments. We’re committed to supporting and inspiring developers and engineers from all walks of life. We pay our contributors, and we don’t sell ads.

Machine Learning 52

Machine Learning Deep Learning Robotics Neural Network 52

ODSC - Open Data Science

JANUARY 29, 2024

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. Shap: Currently LLMs are not directly explainable in the same way as simpler machine learning models due to their complexity, size, and the black box nature of closed source models.

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Viso.ai

JANUARY 2, 2024

VGGNet , introduced by Simonyan and Zisserman in 2014, emphasized the importance of depth in CNN architectures through its 16-19 layer CNN network. Making CNN models more interpretable and explainable. However, these advancements come with their own set of challenges: Overcoming the heavy reliance on large, labeled datasets.

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Explosion

FEBRUARY 18, 2015

So I wrote two blog posts, explaining how to write a part-of-speech tagger and parser. This is easy to do, as spaCy loads a vector-space representation for every word (by default, the vectors produced by Levy and Goldberg (2014) _). Nothing past the tokenizer is suitable for production use.

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NLP Natural Language Processing Software Engineer Machine Learning 52

Heartbeat

OCTOBER 13, 2023

Time series Analysis showing Tuberculosis morbidity from a timespan of January 2004 to June 2014 in Xinjiang. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

Machine Learning 52

Machine Learning Data Scientist Deep Learning Python 52

Pragnakalp

JANUARY 1, 2024

There are several theories and hypotheses that attempt to explain what might have come before the Big Bang, but none of them have been proven conclusively. Overall, while there are many theories and hypotheses that attempt to explain what came before the Big Bang, none of them have been proven conclusively. Mistral-7b-instruct-v0.1

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