AI Weekly

DECEMBER 7, 2023

techxplore.com Millions of new materials discovered with deep learning AI tool GNoME finds 2.2 deepmind.google Seeing 3D images through the eyes of AI This issue is resolved by Professor Zhang's paper, "RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds." Petrobras) has invested in six robots from ANYbotics.

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AI Weekly

JUNE 29, 2023

AI is rightly touted as a massively powerful tool biztoc.com Robotics Meet the robots attending the UN’s ‘AI for Good Global’ summit At a UN summit in Geneva next week, tech luminaries ranging from futurist Ray Kurzweil to DeepMind COO Lila Ibrahim will discuss AI for good. phys.org Quantum Computing Inc.

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Analytics Vidhya

MARCH 30, 2022

Introduction AI and machine vision, which were formerly considered futuristic technology, has now become mainstream, with a wide range of applications ranging from automated robot assembly to automatic vehicle guiding, analysis of remotely sensed images, and automated visual inspection. Computer vision and deep learning […].

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AI Weekly

JULY 27, 2023

techcrunch.com The Essential Artificial Intelligence Glossary for Marketers (90+ Terms) BERT - Bidirectional Encoder Representations from Transformers (BERT) is Google’s deep learning model designed explicitly for natural language processing tasks like answering questions, analyzing sentiment, and translation. Get it today!]

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NVIDIA

OCTOBER 26, 2023

Researchers are taking deep learning for a deep dive, literally. The Woods Hole Oceanographic Institution (WHOI) Autonomous Robotics and Perception Laboratory ( WARPLab ) and MIT are developing a robot for studying coral reefs and their ecosystems. It also runs models to navigate and collect data autonomously.

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Marktechpost

MARCH 16, 2025

This capability is vital for autonomous driving, robotics, and augmented reality applications. Despite advancements in deep learning , many existing stereo-matching models require domain-specific fine-tuning to achieve high accuracy.

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Marktechpost

JUNE 3, 2024

Its AI courses offer hands-on training for real-world applications, enabling learners to effectively use Intel’s portfolio in deep learning, computer vision, and more. It covers AI fundamentals, including supervised learning and deep learning basics, without complex math.

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Heartbeat

MARCH 14, 2023

As robots need to be able to pick up on their surroundings and adapt accordingly, this is a crucial skill for the field. In this article, I would like to take a look at the current challenges in the field of robotics and discuss the relevance and applications of computer vision in this area.

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Unite.AI

AUGUST 8, 2023

Instead of complex and sequential architectures like Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs), the Transformer model introduced the concept of attention, which essentially meant focusing on different parts of the input text depending on the context.

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Marktechpost

MARCH 22, 2024

The advent of deep learning propelled the field forward, offering improved accuracy but at the expense of computational efficiency. This dichotomy is particularly pronounced in scenarios requiring instantaneous visual data processing, such as autonomous vehicles, robotic navigation, and interactive augmented reality systems.

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Marktechpost

NOVEMBER 20, 2024

Some researchers have introduced multi-architectural modular deep neural networks to reduce false positives in anomaly detection. Others have proposed a hybrid network intrusion detection system integrating convolutional neural networks (CNN), fuzzy C-means clustering, genetic algorithm, and a bagging classifier.

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Viso.ai

DECEMBER 3, 2023

How pose estimation works: Deep learning methods Use Cases and pose estimation applications How to get started with AI motion analysis Real-time full body pose estimation in construction – built with Viso Suite About us: Viso.ai Today, the most powerful image processing models are based on convolutional neural networks (CNNs).

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Marktechpost

MARCH 20, 2025

Let’s create a small dataset of abstracts from various fields: Copy Code Copied Use a different Browser abstracts = [ { "id": 1, "title": "Deep Learning for Natural Language Processing", "abstract": "This paper explores recent advances in deep learning models for natural language processing tasks.

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Marktechpost

DECEMBER 24, 2023

By addressing the limitations observed in prior surveys, this comprehensive study aims to bridge the gap by encompassing a broader spectrum of ML techniques, from traditional to deep learning and augmented learning. Deep learning methods have shown robust performance in classifying leaf diseases.

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Viso.ai

DECEMBER 3, 2023

The recent deep learning algorithms provide robust person detection results. However, deep learning models such as YOLO that are trained for person detection on a frontal view data set still provide good results when applied for overhead view person counting ( TPR of 95%, FPR up to 0.2% ).

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Marktechpost

SEPTEMBER 9, 2024

Learning computer vision is essential as it equips you with the skills to develop innovative solutions in areas like automation, robotics, and AI-driven analytics, driving the future of technology. Computer Vision The Computer Vision Nanodegree Program offers advanced training in computer vision, deep learning, and robotics.

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Pickl AI

APRIL 9, 2025

Summary: Artificial Intelligence (AI) and Deep Learning (DL) are often confused. AI vs Deep Learning is a common topic of discussion, as AI encompasses broader intelligent systems, while DL is a subset focused on neural networks. Is Deep Learning just another name for AI? What sets them apart?

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Marktechpost

DECEMBER 10, 2024

1943: McCulloch and Pitts created a mathematical model for neural networks, marking the theoretical inception of ANNs. 1958: Frank Rosenblatt introduced the Perceptron , the first machine capable of learning, laying the groundwork for neural network applications. How Do Artificial Neural Networks Work?

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Heartbeat

NOVEMBER 21, 2023

Computer vision, the field dedicated to enabling machines to perceive and understand visual data, has witnessed a monumental shift in recent years with the advent of deep learning. Photo by charlesdeluvio on Unsplash Welcome to a journey through the advancements and applications of deep learning in computer vision.

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Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms.

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Viso.ai

FEBRUARY 25, 2023

The scene is parted into different classes such as “building”, “road”, “tree” In the last 40 years, various segmentation methods have been proposed, ranging from MATLAB image segmentation and traditional computer vision methods to the state of the art deep learning methods.

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Pickl AI

SEPTEMBER 25, 2024

Summary: This blog delves into 20 Deep Learning applications that are revolutionising various industries in 2024. From healthcare to finance, retail to autonomous vehicles, Deep Learning is driving efficiency, personalization, and innovation across sectors.

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Marktechpost

DECEMBER 1, 2023

Applications include better human-computer interaction and improved emotional response in robots, making FER crucial in human-machine interface technology. Early approaches heavily relied on manually crafted features and machine learning algorithms such as support vector machines and random forests.

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Heartbeat

JANUARY 12, 2023

How to Log Your Keras Deep Learning Experiments With Comet Image by rawpixel.com on Freepik Overview Let us start by asking ourselves some questions: Have you ever wondered how Google’s translation app can instantly convert entire paragraphs between two languages? What is Deep Learning? Experience is the best teacher.

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Pickl AI

FEBRUARY 7, 2025

Summary: Machine Learning and Deep Learning are AI subsets with distinct applications. Introduction In todays world of AI, both Machine Learning (ML) and Deep Learning (DL) are transforming industries, yet many confuse the two. What is Deep Learning? billion by 2034.

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Pickl AI

FEBRUARY 1, 2023

Transfer Learning in Deep Learning: A Brief Overview Collecting large volumes of data, filtering it and then interpreting is a challenging task. Yes, Transfer Learning is the answer to it. What is Transfer Learning? Eventually, making it a powerful and efficient tool in Machine Learning.

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Viso.ai

DECEMBER 1, 2024

The field of computer vision is a sector of Artificial Intelligence (AI) that uses Machine Learning and Deep Learning to enable computers to see , perform AI pattern recognition , and analyze objects in photos and videos like people do. Multiple deep-learning computer vision models exist for x-ray-based COVID-19 diagnosis.

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Viso.ai

JUNE 10, 2024

This system has applications in various fields such as filmmaking, video production, animation, sports analysis, robotics, and augmented reality. Marker-less Systems: Currently, modern systems use computer vision and deep learning to track motion without markers. The concept of motion tracking has been in existence for decades.

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Deep Learning Computer Vision Neural Network Convolutional Neural Networks 52

Heartbeat

APRIL 23, 2023

Photo by RetroSupply on Unsplash Introduction Deep learning has been widely used in various fields, such as computer vision, NLP, and robotics. The success of deep learning is largely due to its ability to learn complex representations from data using deep neural networks.

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Machine Learning (Theory)

MARCH 5, 2025

As an example, the speech recognition community spent decades focusing on Hidden Markov Models at the expense of other architectures, before eventually being disrupted by advancements in deep learning. Support Vector Machines were disrupted by deep learning, and convolutional neural networks were displaced by transformers.

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Marktechpost

JULY 24, 2023

Many studies have been motivated to explore hidden hierarchical patterns in the large volume of weather datasets for weather forecasting due to the recent development of deep learning techniques, the widespread availability of massive weather observation data, and the advent of information and computer technology.

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Viso.ai

MAY 1, 2024

Deep learning and Convolutional Neural Networks (CNNs) have enabled speech understanding and computer vision on our phones, cars, and homes. Home Robots: over the next 15 years, mechanical and AI technologies will increase home robots’ reliable usage in a typical household. Brooks et al.

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NVIDIA

DECEMBER 21, 2023

DigitalPath, based in Chico, California, has refined a convolutional neural network to spot wildfires. The NVIDIA Jetson Orin -based robots-as-a-service business provides farmers with metrics on yield gains and chemical reduction. “We Burning for solutions, developers are embracing AI for early detection.

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Viso.ai

MARCH 26, 2024

The introduction of the Transformer model was a significant leap forward for the concept of attention in deep learning. Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. Vaswani et al.

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AWS Machine Learning Blog

MARCH 30, 2023

The model extracts features from the image using a convolutional neural network. He has been working on human-in-the-loop optimization since 1995, when he applied the LeNet Convolutional Neural Network to check recognition. As input, the model takes an image and a corresponding bounding box annotation.

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Heartbeat

DECEMBER 14, 2023

is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. in 1998, In general, LeNet refers to LeNet-5 and is a simple convolutional neural network. > Finished chain.

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Heartbeat

MAY 30, 2023

It combines reinforcement learning (RL), a type of learning in which an agent learns through examinations and experimentations by receiving rewards or punishments based on its actions, with deep learning. This machine learning subset uses artificially generated neural networks to model complex data relationships.

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