Google Research AI blog

FEBRUARY 7, 2023

The explosion in deep learning a decade ago was catapulted in part by the convergence of new algorithms and architectures, a marked increase in data, and access to greater compute. Below, we highlight a panoply of works that demonstrate Google Research’s efforts in developing new algorithms to address the above challenges.

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Pickl AI

AUGUST 3, 2023

Introduction to Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning techniques that are designed to automatically learn and represent data in multiple layers of abstraction. How Deep Learning Algorithms Work?

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Marktechpost

NOVEMBER 20, 2024

By utilizing a transformer, AlphaQubit learns to interpret noisy syndrome information, providing a mechanism that outperforms existing algorithms on Google’s Sycamore quantum processor for surface codes of distances 3 and 5, and demonstrates its capability on distances up to 11 in simulated environments.

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Marktechpost

MARCH 30, 2024

In 2024, the landscape of Python libraries for machine learning and deep learning continues to evolve, integrating more advanced features and offering more efficient and easier ways to build, train, and deploy models. PyTorch PyTorch is a widely used open-source machine learning library based on the Torch library.

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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

MARCH 5, 2024

Deep Learning models have revolutionized our ability to process and understand vast amounts of data. However, a vast portion of the digital world comprises binary data, the fundamental building block of all digital information, which still needs to be explored by current deep-learning models.

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Unite.AI

AUGUST 2, 2023

While this has certainly improved many areas of life itself, such as how we walk around with handheld devices that can deliver us information at any time, it also poses certain risks. More so what I’m referring to here is that there are so many parts of our lives today that are impacted by algorithms used by artificial intelligence (AI).

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Marktechpost

NOVEMBER 16, 2024

In robotics, tactile sensing is a critical technology that complements visual information, allowing the robots to interact with their environment in a way that is similar to human touch. Deep Learning Assistance: These sensing units collect complex data that needs to be interpreted.

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Marktechpost

SEPTEMBER 1, 2023

Smartphones now hold immense amounts of sensitive information, making security a pressing concern. Incorporating machine learning and deep learning algorithms has shown promise in bolstering security. Researchers have been exploring behavioral and physiological biometrics for enhancing mobile device security.

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Marktechpost

JANUARY 24, 2024

Artificial Intelligence has witnessed a revolution, largely due to advancements in deep learning. This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. Before the advent of deep learning, data representation often involved manually curated feature vectors.

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Analytics Vidhya

OCTOBER 3, 2022

Introduction In the last article, we learned about various blind search algorithms because no further information is given beyond the constraints laid out in the problem. Hence, The algorithms look for traversing through many different states before reaching the goal state. The disadvantage […].

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The MLOps Blog

NOVEMBER 14, 2023

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. The reasons for this range from wrongly connected model components to misconfigured optimizers.

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Marktechpost

APRIL 21, 2024

MIT researchers proposed working with deep learning to address the challenges of understanding and accurately modeling the planetary boundary layer (PBL) to improve weather forecasting and climate projections and deal with issues like droughts. These methods work to some extent, but they can not solve very complicated PBL structures.

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Marktechpost

NOVEMBER 13, 2024

Researchers from Majmaah University, King Saud University, and the University of Wollongong developed FinSafeNet, a deep-learning model for secure digital banking. Based on a Bi-LSTM, CNN, and a dual attention mechanism, this model addresses real-time transaction security. Don’t Forget to join our 55k+ ML SubReddit.

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Towards AI

NOVEMBER 16, 2024

Keswani’s Algorithm introduces a novel approach to solving two-player non-convex min-max optimization problems, particularly in differentiable sequential games where the sequence of player actions is crucial. Keswani’s Algorithm: The algorithm essentially makes response function : maxy∈{R^m} f (.,

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Marktechpost

FEBRUARY 24, 2024

Traditional segmentation methods, such as threshold-based and morphology-based algorithms, have been employed but need help accurately delineating objects in the complex underwater environment where image degradation is common. Researchers increasingly use deep learning techniques for underwater image segmentation to address these challenges.

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Pickl AI

SEPTEMBER 23, 2024

Summary: This guide covers the most important Deep Learning interview questions, including foundational concepts, advanced techniques, and scenario-based inquiries. Gain insights into neural networks, optimisation methods, and troubleshooting tips to excel in Deep Learning interviews and showcase your expertise.

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Towards AI

SEPTEMBER 10, 2023

What I’ve learned from the most popular DL course Photo by Sincerely Media on Unsplash I’ve recently finished the Practical Deep Learning Course from Fast.AI. So you definitely can trust his expertise in Machine Learning and Deep Learning. Luckily, there’s a handy tool to pick up Deep Learning Architecture.

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Marktechpost

JANUARY 14, 2024

There has been a meteoric rise in the use of deep learning in image processing in the past several years. The robust feature learning and mapping capabilities of deep learning-based approaches enable them to acquire intricate blur removal patterns from large datasets.

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Marktechpost

MARCH 23, 2024

AI researchers are taking the game to a new level with geometric deep learning. However, for the algorithms of TacticAI, it’s a complex physics problem that is just waiting to be solved through data and prediction. The future of football coaching has arrived – and it’s taking geometric deep learning to heart.

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Marktechpost

APRIL 12, 2024

Deep learning architectures have revolutionized the field of artificial intelligence, offering innovative solutions for complex problems across various domains, including computer vision, natural language processing, speech recognition, and generative models. This state is updated as the network processes each element of the sequence.

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Marktechpost

APRIL 18, 2024

Since the backpropagation learning algorithm is sequential, it isn’t easy to parallelize these models, even though the process works fine on a single computing unit. The ever-increasing size and complexity of neural networks have propelled distributed deep learning to new heights in recent years.

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PyImageSearch

JANUARY 30, 2023

This last blog of the series will cover the benefits, applications, challenges, and tradeoffs of using deep learning in the education sector. To learn about Computer Vision and Deep Learning for Education, just keep reading. Figure 2 ). Task Automation AI software can easily handle repetitive, manual tasks (e.g.,

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AI News

JUNE 3, 2024

delivers accurate and relevant information, making it an indispensable tool for professionals in these fields. Harnessing the Power of Machine Learning and Deep Learning At TickLab, our innovative approach is deeply rooted in the advanced capabilities of machine learning (ML) and deep learning (DL).

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Towards AI

NOVEMBER 19, 2023

Photo by Pietro Jeng on Unsplash Deep learning is a type of machine learning that utilizes layered neural networks to help computers learn from large amounts of data in an automated way, much like humans do. Loss functions guide learning by measuring errors. Activation functions introduce non-linear patterns.

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Heartbeat

FEBRUARY 21, 2024

Photo by Almos Bechtold on Unsplash Deep learning is a machine learning sub-branch that can automatically learn and understand complex tasks using artificial neural networks. Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns.

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Marktechpost

SEPTEMBER 10, 2024

These systems use algorithms that analyze users’ listening patterns, making personalized song recommendations. One key type of algorithm used in these services is sequential recommendation systems, which predict the next song a user will enjoy based on previous listening sessions.

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Marktechpost

AUGUST 8, 2023

This involves the use of Machine Learning and Deep Learning models in this domain. The team of research scientists used deep learning techniques to predict the molecules with a 10 times increase in size as the previously obtained molecules. The research team developed 2 software tools for protein design.

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Analytics Vidhya

FEBRUARY 4, 2023

NLP has proven to be […] The post Extracting Medical Information From Clinical Text With NLP appeared first on Analytics Vidhya.

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Viso.ai

DECEMBER 3, 2023

This article will explore the latest advances in pose analytics algorithms and AI vision techniques, their applications and use cases, and their limitations. However, modern deep learning based approaches have achieved major breakthroughs by improving the performance significantly for both single-person and multi-person pose estimation.

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Heartbeat

OCTOBER 11, 2023

A key component of artificial intelligence is training algorithms to make predictions or judgments based on data. This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. What is Machine Learning?

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Towards AI

AUGUST 21, 2023

Machine Learning vs. AI vs. Deep Learning vs. Neural Networks: What’s the Difference? The rapid evolution of technology is molding our everyday existence as businesses turn more and more to sophisticated algorithms for efficiency. Machine Learning (ML): Next, machine learning takes the spotlight.

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Pickl AI

SEPTEMBER 16, 2024

Summary : Deep Belief Networks (DBNs) are Deep Learning models that use Restricted Boltzmann Machines and feedforward networks to learn hierarchical features and model complex data distributions. What is a Deep Belief Network (DBN)? They are effective in image recognition, NLP, and speech recognition.

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Heartbeat

NOVEMBER 21, 2023

In a world where visual data surrounds us, the ability to extract meaningful information from images and videos is more crucial than ever. 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.

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Marktechpost

OCTOBER 2, 2023

It involves the information about the morphology and topography of the surface to identify the defects. The Korean Research Institute of Standards and Science researchers have developed a technology based on deep learning techniques that enables real-time 3D measurements using a single-sot pattern projection method.

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Viso.ai

JUNE 10, 2024

Before the deep learning era, mechanical systems (these devices used rotating disks to record motion sequences) and manual methods tracked motion (where each object in each frame was traced by hand). Marker-less Systems: Currently, modern systems use computer vision and deep learning to track motion without markers.

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Heartbeat

NOVEMBER 14, 2023

For many years, gradient-boosting models and deep-learning solutions have won the lion's share of Kaggle competitions. XGBoost is not limited to machine learning tasks, as its incredible power can be harnessed when harmonized with deep learning algorithms. 2 (2021): 522–531.

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