Heartbeat

FEBRUARY 6, 2023

How do Object Detection Algorithms Work? There are two main categories of object detection algorithms. Two-Stage Algorithms: Two-stage object detection algorithms consist of two different stages. Single-stage object detection algorithms do the whole process through a single neural network model.

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John Snow Labs

JUNE 2, 2023

Spark NLP’s deep learning models have achieved state-of-the-art results on sentiment analysis tasks, thanks to their ability to automatically learn features and representations from raw text data. During training, the model learns to identify patterns and features that are indicative of a certain sentiment.

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Marktechpost

SEPTEMBER 17, 2024

Large-scale deep learning models, especially transformer-based architectures, have grown exponentially in size and complexity, reaching billions to trillions of parameters. Recent studies have reviewed language models, optimization techniques, and acceleration methods for large-scale deep-learning models and LLMs.

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Viso.ai

DECEMBER 3, 2023

This article will provide an introduction to object detection and provide an overview of the state-of-the-art computer vision object detection algorithms. The recent deep learning algorithms provide robust person detection results. Most modern person detector techniques are trained on frontal and asymmetric views.

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Towards AI

AUGUST 25, 2023

Machine learning (ML) is a subset of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed. In ML, there are a variety of algorithms that can help solve problems. Any competent software engineer can implement any algorithm. 12, 2014. [3]

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AssemblyAI

SEPTEMBER 4, 2023

” In fact, when our company was accepted into Y Combinator back in 2017, one of the first questions the YC partners asked us was “What’s your WER?” In the field of Automatic Speech Recognition, the Word Error Rate has become the de facto standard for measuring how accurate a speech recognition model is.

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PyImageSearch

JANUARY 2, 2023

This blog will cover the benefits, applications, challenges, and tradeoffs of using deep learning in healthcare. Computer Vision and Deep Learning for Healthcare Benefits Unlocking Data for Health Research The volume of healthcare-related data is increasing at an exponential rate.

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Heartbeat

MARCH 14, 2023

By incorporating computer vision methods and algorithms into robots, they are able to view and understand their environment. Object recognition and tracking algorithms include the CamShift algorithm , Kalman filter , and Particle filter , among others.

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Marktechpost

OCTOBER 25, 2024

It’s built on top of popular deep learning frameworks like PyTorch and TensorFlow, making it accessible to a broad audience of developers and researchers. NLP Tutorial is a comprehensive guide for deep learning researchers, providing implementations of various NLP models using PyTorch.

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Viso.ai

DECEMBER 19, 2023

Image Classification Using Machine Learning CNN Image Classification (Deep Learning) Example applications of Image Classification Let’s dive deep into it! It uses AI-based deep learning models to analyze images with results that for specific tasks already surpass human-level accuracy (for example, in face recognition ).

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Unite.AI

NOVEMBER 20, 2024

Founded in 2017, XCath is a startup focused on advancements in medical robotics, nanorobotics, and materials science. AI algorithms can serve as a constant teacher and assistant, decreasing the cognitive load and leveling up all providers to provide world-class care. What role does AI play in improving patient outcomes?

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Viso.ai

DECEMBER 10, 2023

Image recognition with deep learning is a key application of AI vision and is used to power a wide range of real-world use cases today. I n past years, machine learning, in particular deep learning technology , has achieved big successes in many computer vision and image understanding tasks.

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AssemblyAI

MAY 3, 2023

With these fairly complex algorithms often being described as “giant black boxes” in news and media, a demand for clear and accessible resources is surging. This process of adapting pre-trained models to new tasks or domains is an example of Transfer Learning , a fundamental concept in modern deep learning.

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. NLP algorithms help computers understand, interpret, and generate natural language.

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AWS Machine Learning Blog

MAY 16, 2024

Therefore, we decided to introduce a deep learning-based recommendation algorithm that can identify not only linear relationships in the data, but also more complex relationships. Recommendation model using NCF NCF is an algorithm based on a paper presented at the International World Wide Web Conference in 2017.

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Defined.ai blog

JANUARY 29, 2023

Generative Adversarial Networks (GANs) are a type of deep learning algorithm that’s been gaining popularity due to their ability to generate high-quality, realistic images and other types of data. As such, Generative Adversarial Networks are invaluable deep learning algorithms with almost endless beneficial potential.

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Marktechpost

JULY 18, 2023

One of the most significant advances in deep learning and image processing is object detection, locating and labeling objects inside a given image. Interest in object detection has been strong for a long time, well before the advent of deep learning techniques and cutting-edge image processing tools.

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The Stanford AI Lab Blog

FEBRUARY 24, 2022

In this first post, we’ll analyze self-training , which is a very impactful algorithmic paradigm for semi-supervised learning and domain adaptation. In Part 2, we will use related theoretical ideas to analyze self-supervised contrastive learning algorithms, which have been very effective for unsupervised representation learning.

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Towards AI

JULY 20, 2023

That’s great news for researchers who often work on SLRs because the traditional process is mind-numbingly slow: An analysis from 2017 found that SLRs take, on average, 67 weeks to produce. As the capabilities of high-powered computers and ML algorithms have grown, so have opportunities to improve the SLR process. dollars apiece.

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AI Impacts

FEBRUARY 1, 2023

A faulty brake line on a car is not much of a concern to the public until the car is on public roads, and the facebook feed algorithm cannot be a threat to society until it is used to control what large numbers of people see on their screens. The alignment problem from a deep learning perspective. Norton & Company; 2020.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Together, these elements lead to the start of a period of dramatic progress in ML, with NN being redubbed deep learning. In 2017, the landmark paper “ Attention is all you need ” was published, which laid out a new deep learning architecture based on the transformer.

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Viso.ai

DECEMBER 6, 2024

To accomplish this, they require two key components: machine learning and computer vision. Machine learning methods represent the brain of the car. After that, they utilize specialized algorithms to identify trends, predict outcomes, and absorb fresh data. It is achieved by computer vision algorithms. Garcia et al.

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Viso.ai

FEBRUARY 11, 2024

Image classification employs AI-based deep learning models to analyze images and perform object recognition, as well as a human operator. The Need for Image Training Datasets To train the image classification algorithms we need image datasets. The labels provide the Knowledge the algorithm can learn from.

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PyImageSearch

JULY 3, 2023

Figure 1: Netflix Recommendation System (source: “Netflix Film Recommendation Algorithm,” Pinterest ). Netflix recommendations are not just one algorithm but a collection of various state-of-the-art algorithms that serve different purposes to create the complete Netflix experience.

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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. Kurakin et al, ICLR 2017. Source: Robust Physical-World Attacks on Deep Learning Visual Classification. Kurakin et al, ICLR 2017. Sharif et al. Eykholt et al.

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Pickl AI

APRIL 8, 2024

This blog explores 13 major AI blunders, highlighting issues like algorithmic bias, lack of transparency, and job displacement. From the moment we wake up to the personalized recommendations on our phones to the algorithms powering facial recognition software, AI is constantly shaping our world.

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Viso.ai

JULY 17, 2024

Background and History of Neural Style Transfer NST is an example of an image styling problem that has been in development for decades, with image analogies and texture synthesis algorithms paving foundational work for NST. With deep learning, the results were impressively good. Here is the journey of NST. Gatys et al.

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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., Understanding the robustness of image segmentation algorithms to adversarial attacks is critical for ensuring their reliability and security in practical applications.

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Mlearning.ai

FEBRUARY 10, 2023

For example, if you are using regularization such as L2 regularization or dropout with your deep learning model that performs well on your hold-out-cross-validation set, then increasing the model size won’t hurt performance, it will stay the same or improve. In the big data and deep learning era, now you have much more flexibility.

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Viso.ai

OCTOBER 10, 2024

Data Preprocessing Although the satellite data is already prepared and ready for analysis, there are a few more processing steps – before using the data in a deep learning model. It is not feasible to manually label enough satellite photos and train algorithms to comprehend visual data. Model Training Miller et al.

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Viso.ai

JULY 9, 2024

Neural Networks For now, most attempts to develop ASI are still grounded in well-known models, such as neural networks , machine learning/deep learning , and computational neuroscience. This is because reinforcement learning is most similar to how humans develop by continuously interacting with and learning from our environment.

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Mlearning.ai

APRIL 10, 2023

3 feature visual representation of a K-means Algorithm. Source: Marubon-DS Unsupervised Learning In the data science context, clustering is an unsupervised machine learning technique, this means that it does not require predefined labeled inputs or outcomes to learn from.

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AWS Machine Learning Blog

AUGUST 16, 2023

Travelers collaborated with the Amazon Machine Learning Solutions Lab (now known as the Generative AI Innovation Center ) to develop this framework to support and enhance aerial imagery model use cases. The images document the land cover, or physical surface features, of ten European countries between June 2017 and May 2018.

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Mlearning.ai

MAY 16, 2023

. * @return the predicted class */ std::string Inference(cv::Mat& src, AAssetManager* mgr); #endif //IMAGECLASSIFICATION_INFERENCE_H /** * inference.cpp */ #include <algorithm> #include <vector> #include <string> #include <opencv2/imgproc.hpp> #include "net.h" " #include "inference.h"

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Mlearning.ai

JULY 31, 2023

Things become more complex when we apply this information to Deep Learning (DL) models, where each data type presents unique challenges for capturing its inherent characteristics. 2017) paper, vector embeddings have become a standard for training text-based DL models. Likewise, sound and text have no meaning to a computer.

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Marktechpost

JULY 17, 2023

Synthetic data is generated algorithmically and used to train machine learning models, validate mathematical models, and act as a stand-in for test production or operational data test datasets. That is the main line of business for Parallel Domain, a Silicon Valley startup that was established in 2017 and which we previously profiled.

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Machine Learning Data Scientist Computer Vision Deep Learning 52

Google Research AI blog

FEBRUARY 2, 2023

Great machine learning (ML) research requires great systems. With the increasing sophistication of the algorithms and hardware in use today and with the scale at which they run, the complexity of the software necessary to carry out day-to-day tasks only increases. You can find other posts in the series here.) Mechanization.

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