Marktechpost

MARCH 8, 2025

Deep learning models, having revolutionized areas of computer vision and natural language processing, become less efficient as they increase in complexity and are bound more by memory bandwidth than pure processing power. A primary issue in deep learning computation is optimizing data movement within GPU architectures.

Deep Learning 53

Deep Learning Natural Language Processing Algorithm Computer Vision 53

Unite.AI

OCTOBER 27, 2024

The framework enables developers to build, train, and deploy machine learning models entirely in JavaScript, supporting everything from basic neural networks to complex deep learning architectures. Key Features: Hardware-accelerated ML operations using WebGL and Node.js What distinguishes TensorFlow.js

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Marktechpost

DECEMBER 8, 2023

Deep learning is finding its utility in all aspects of life. The data-centric nature of deep learning impedes its ability to generalize effectively in the face of continually changing surroundings.’ PyPose is a powerful example of blending age-old robotics techniques with the latest innovations in deep learning.

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Deep Learning Robotics Algorithm AI Researcher 131

Unite.AI

MARCH 20, 2025

I am lucky to run a company that gives me a deep sense of purpose and allows me to work with an incredibly talented team from diverse backgrounds and disciplines. I then worked as an algorithms engineer and moved on to product management. In the first days of Ibex, Chaim was busy winning Kaggle (ML) competitions.

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Convolutional Neural Networks Algorithm Explainability Neural Network 147

Unite.AI

NOVEMBER 6, 2023

With these advancements, it’s natural to wonder: Are we approaching the end of traditional machine learning (ML)? In this article, we’ll look at the state of the traditional machine learning landscape concerning modern generative AI innovations. What is Traditional Machine Learning? What are its Limitations?

Machine Learning 204

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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. I’ve passed many ML courses before, so that I can compare. So you definitely can trust his expertise in Machine Learning and Deep Learning.

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Deep Learning Neural Network ML Computer Vision 124

Towards AI

JANUARY 29, 2025

Generative AI is powered by advanced machine learning techniques, particularly deep learning and neural networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Programming Languages: Python (most widely used in AI/ML) R, Java, or C++ (optional but useful) 2.

AI Engineer 98

AI Engineer Generative AI Neural Network BERT 98

BAIR

MARCH 11, 2024

Their work at BAIR, ranging from deep learning, robotics, and natural language processing to computer vision, security, and much more, has contributed significantly to their fields and has had transformative impacts on society. Specifically, I work on methods that algorithmically generates diverse training environments (i.e.,

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Marktechpost

NOVEMBER 16, 2024

Over two weeks, you’ll learn to extract features from images, apply deep learning techniques for tasks like classification, and work on a real-world project to detect facial key points using a convolutional neural network (CNN).

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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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Marktechpost

JANUARY 4, 2024

Qu Kun from the University of Science and Technology of the Chinese Academy of Sciences has created a solution called Spatial Architecture Characterization by Deep Learning (SPACEL). The post Meet SPACEL: A New Deep-Learning-based Analysis Toolkit for Spatial Transcriptomics appeared first on MarkTechPost.

Deep Learning 141

Deep Learning Neural Network Algorithm Automation 141

How to Learn Machine Learning

APRIL 8, 2025

Tom Mitchell (1997) Fast forward to 1997, when Tom Mitchell offered a more formal machine learning definition: “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”

Machine Learning 76

Machine Learning Deep Learning Artificial Intelligence Artificial Intelligence 76

Bugra Akyildiz

FEBRUARY 26, 2025

Stanford CS224n: Natural Language Processing with Deep Learning Stanford’s CS224n stands as the gold standard for NLP education, offering a rigorous exploration of neural architectures, sequence modeling, and transformer-based systems. S191: Introduction to Deep Learning MIT’s 6.S191

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Deep Learning NLP Machine Learning Robotics 40

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.

Deep Learning 124

Deep Learning Machine Learning Python Computer Vision 124

Unite.AI

MARCH 15, 2024

Nevertheless, addressing the cost-effectiveness of ML models for business is something companies have to do now. For businesses beyond the realms of big tech, developing cost-efficient ML models is more than just a business process — it's a vital survival strategy. Challenging Nvidia, with its nearly $1.5

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ML Deep Learning Neural Network Machine Learning 188

Marktechpost

APRIL 24, 2024

While traditional PAM analysis is time-consuming, recent advancements in deep learning technology offer promising solutions for automating bird species identification from audio recordings. However, ensuring the understandability of complex algorithms to ornithologists and biologists is essential.

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Deep Learning Convolutional Neural Networks Neural Network Algorithm 109

Analytics Vidhya

AUGUST 10, 2023

Introduction In today’s evolving landscape, organizations are rapidly scaling their teams to harness the potential of AI, deep learning, and ML. What started as a modest concept, machine learning, has now become indispensable across industries, enabling businesses to tap into unprecedented opportunities.

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Machine Learning Deep Learning ML AI 271

Unite.AI

APRIL 6, 2025

Wendys AI-Powered Drive-Thru System (FreshAI) FreshAI uses advanced natural language processing (NLP) , machine learning (ML) , and generative AI to optimize the fast-food ordering experience. Unlike conventional voice recognition systems, FreshAI employs deep learning models trained on thousands of real-world customer interactions.

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Automation AI AI Neural Network 183

Marktechpost

MARCH 26, 2024

Deep learning models have recently gained significant popularity in the Artificial Intelligence community. In order to address these challenges, a team of researchers has introduced DomainLab, a modular Python package for domain generalization in deep learning. If you like our work, you will love our newsletter.

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Deep Learning Python Neural Network Artificial Intelligence 129

Marktechpost

JUNE 16, 2024

Topological Deep Learning (TDL) advances beyond traditional GNNs by modeling complex multi-way relationships, unlike GNNs that only capture pairwise interactions. Topological Neural Networks (TNNs), a subset of TDL, excel in handling higher-order relational data and have shown superior performance in various machine-learning tasks.

Deep Learning 131

Deep Learning Neural Network Machine Learning Explainability 131

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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Algorithm ML Deep Learning Machine Learning 105

Marktechpost

JULY 5, 2024

Despite their success, deep learning models have recently emerged, claiming superior performance on certain tabular datasets. While deep neural networks excel in fields like image, audio, and text processing, their application to tabular data presents challenges due to data sparsity, mixed feature types, and lack of transparency.

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Deep Learning Neural Network Algorithm Machine Learning 111

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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Neural Network Deep Learning Computer Vision Big Data 135

Marktechpost

JANUARY 20, 2025

AI and ML are expanding at a remarkable rate, which is marked by the evolution of numerous specialized subdomains. While they share foundational principles of machine learning, their objectives, methodologies, and outcomes differ significantly. Rather than learning to generate new data, these models aim to make accurate predictions.

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Generative AI Neural Network AI AI 119

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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AI News

AUGUST 9, 2024

To elaborate, Machine learning (ML) models – especially deep learning networks – require enormous amounts of data to train effectively, often relying on powerful GPUs or specialised hardware to process this information quickly. On the other hand, AI thrives on massive datasets and demands high-performance computing.

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Marktechpost

JANUARY 15, 2025

This gap has led to the evolution of deep learning models, designed to learn directly from raw data. What is Deep Learning? Deep learning, a subset of machine learning, is inspired by the structure and functioning of the human brain. High Accuracy: Delivers superior performance in many tasks.

Deep Learning 89

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Unite.AI

AUGUST 28, 2024

For example, an algorithm that predicts which patients need more intensive care based on healthcare costs rather than actual illness. Furthermore, while machine learning (ML) algorithms can offer personalized treatment recommendations, the lack of transparency in these algorithms complicates individual accountability.

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Marktechpost

JANUARY 10, 2024

Researchers have developed an innovative tool that utilizes deep learning technology to automate bone mineral density (DL-BMD) measurements to address these challenges. After the initial segmentation process, the tool uses a region of interest (ROI) placement algorithm to create an elliptical ROI.

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Deep Learning Algorithm Automation ML 115

Flipboard

NOVEMBER 6, 2024

We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. when cases with a small amount of free air (total volume <10 ml) are excluded. Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity.

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Deep Learning Algorithm ML Artificial Intelligence 86

Marktechpost

DECEMBER 28, 2023

Based on the mapper algorithm, this recursive splitting and merging procedure builds a discrete approximation of the Reeb graph. The analysis of a chest X-ray dataset revealed incorrect diagnostic annotations, emphasizing the potential of GTDA in identifying errors in deep learning datasets.

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Deep Learning Data Analysis Neural Network Algorithm 125

IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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Marktechpost

NOVEMBER 16, 2023

Thanks to developments in deep learning approaches, the capability of image analysis algorithms has been greatly enhanced. As a result of improvements in data storage, processing speed, and algorithm quality, larger samples have been used in radiological research. If you like our work, you will love our newsletter.

Deep Learning 140

Deep Learning Data Scientist Algorithm Python 140

Towards AI

JANUARY 9, 2024

In other words, we all want to get directly into Deep Learning. But this is really a mistake if you want to take studying Machine Learning seriously and get a job in AI. Machine Learning fundamentals are not 100% the same as Deep Learning fundamentals and are perhaps even more important.

ML 119

ML Neural Network Machine Learning Deep Learning 119

Towards AI

JANUARY 31, 2025

Explaining a black box Deep learning model is an essential but difficult task for engineers in an AI project. Explainability leverages user interfaces, charts, business intelligence tools, some explanation metrics, and other methodologies to discover how the algorithms reach their conclusions. This member-only story is on us.

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Deep Learning Computer Vision Explainability NLP 96

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.

Deep Learning 115

Deep Learning Convolutional Neural Networks Neural Network Computer Vision 115

Marktechpost

FEBRUARY 28, 2025

The integration of deep learning with sampling algorithms has shown promise in continuous domains, but there remains a significant gap in effective sampling approaches for discrete distributions – despite their prevalence in applications ranging from statistical physics to genomic data and language modeling.

Neural Network 53

Neural Network Algorithm Deep Learning AI Researcher 53