Machine Learning Mastery

NOVEMBER 22, 2024

This well-known motto perfectly captures the essence of ensemble methods: one of the most powerful machine learning (ML) approaches -with permission from deep neural networks- to effectively address complex problems predicated on complex data, by combining multiple models for addressing one predictive task.

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Towards AI

JANUARY 20, 2025

Integrating Bayesian Theory, State-Space Dynamics, and Neural Network Structures for Enhanced Probabilistic Forecasting This member-only story is on us. Thats where the Bayesian State-Space Neural Network (BSSNN) offers a novel solution. Upgrade to access all of Medium.

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Neural Network Machine Learning Explainability AI 98

Unite.AI

MAY 8, 2024

The ability to effectively represent and reason about these intricate relational structures is crucial for enabling advancements in fields like network science, cheminformatics, and recommender systems. Graph Neural Networks (GNNs) have emerged as a powerful deep learning framework for graph machine learning tasks.

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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?

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AI News

JANUARY 28, 2025

Operating virtually rather than from a single physical base, Cognitive Labs will explore AI technologies such as Graph Neural Networks (GNNs), Active Learning, and Large-Scale Language Models (LLMs). Ericsson has launched Cognitive Labs, a research-driven initiative dedicated to advancing AI for telecoms.

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Unite.AI

JUNE 15, 2024

Graduate student Diego Aldarondo collaborated with DeepMind researchers to train an artificial neural network (ANN) , which serves as the virtual brain, using the powerful machine learning technique deep reinforcement learning.

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AI News

DECEMBER 15, 2023

In an interview at AI & Big Data Expo , Alessandro Grande, Head of Product at Edge Impulse , discussed issues around developing machine learning models for resource-constrained edge devices and how to overcome them. “A lot of the companies building edge devices are not very familiar with machine learning,” says Grande.

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Flipboard

NOVEMBER 11, 2024

This article explains, through clear guidelines, how to choose the right machine learning (ML) algorithm or model for different types of real-world and business problems.

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Marktechpost

OCTOBER 28, 2023

In the world of biological research, machine-learning models are making significant strides in advancing our understanding of complex processes, with a particular focus on RNA splicing. Machine learning models like neural networks have been instrumental in advancing scientific discovery and experimental design in biological sciences.

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Becoming Human

MARCH 16, 2023

Photo by Paulius Andriekus on Unsplash Welcome back to the next part of this Blog Series on Graph Neural Networks! The following section will provide a little introduction to PyTorch Geometric , and then we’ll use this library to construct our very own Graph Neural Network!

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Unite.AI

OCTOBER 5, 2023

I've so far read over 60 books on AI, and while some of them do get repetitive, this book managed to offer a fresh perspective, I enjoyed this book enough to add it to my personal list of the Best Machine Learning & AI Books of All Time. AI as neural networks is merely (!) Different target audience.

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Unite.AI

JUNE 25, 2023

Hybrid Approach for Physics-Aware AI Traditionally, computer vision, the field that enables AI to comprehend and infer properties of the physical world from images, has largely focused on data-based machine learning. However, assimilating the understanding of physics into the realm of neural networks has proved challenging.

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Marktechpost

MAY 5, 2024

In recent years, the demand for AI and Machine Learning has surged, making ML expertise increasingly vital for job seekers. Machine Learning with Python This course covers the fundamentals of machine learning algorithms and when to use each of them. and evaluating the same.

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Unite.AI

FEBRUARY 10, 2025

Can you explain how NeuroSplit dynamically optimizes compute resources while maintaining user privacy and performance? NeuroSplit is fundamentally device-agnostic, cloud-agnostic, and neural network-agnostic. But AI shouldn't be limited by which end-user device someone happens to use. Think about what this means for developers.

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Marktechpost

MAY 14, 2024

Understanding deep learning equips individuals to harness its potential, driving innovation and solving complex problems across various industries. This article lists the top Deep Learning and Neural Networks books to help individuals gain proficiency in this vital field and contribute to its ongoing advancements and applications.

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IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

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Unite.AI

AUGUST 28, 2024

Can you discuss the advantages of deep learning over traditional machine learning in threat prevention? However, while many cyber vendors claim to bring AI to the fight, machine learning (ML) – a less sophisticated form of AI – remains a core part of their products.

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Marktechpost

APRIL 5, 2024

In their paper, the researchers aim to propose a theory that explains how transformers work, providing a definite perspective on the difference between traditional feedforward neural networks and transformers. Despite their widespread usage, the theoretical foundations of transformers have yet to be fully explored.

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Marktechpost

JANUARY 13, 2024

The challenge of interpreting the workings of complex neural networks, particularly as they grow in size and sophistication, has been a persistent hurdle in artificial intelligence. The traditional methods of explaining neural networks often involve extensive human oversight, limiting scalability.

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Marktechpost

MARCH 23, 2024

To reduce the memory footprint and further speed up the training of FeatUp’s implicit network, the spatially varying features are compressed to their top k=128 principal components. This optimization accelerates training time by a remarkable 60× for models like ResNet-50 and facilitates larger batches without compromising feature quality.

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AI News

JULY 24, 2023

AI News spoke with Damian Bogunowicz, a machine learning engineer at Neural Magic , to shed light on the company’s innovative approach to deep learning model optimisation and inference on CPUs. One of the key challenges in developing and deploying deep learning models lies in their size and computational requirements.

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Marktechpost

APRIL 24, 2024

The increasing complexity of AI systems, particularly with the rise of opaque models like Deep Neural Networks (DNNs), has highlighted the need for transparency in decision-making processes. Moreover, it can compute these contribution scores efficiently in just one backward pass through the network.

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ODSC - Open Data Science

MARCH 11, 2025

Predictive modeling is at the heart of modern machine learning applications. But how can machine learning practitioners improve the reliability of their models, particularly when dealing with tabular data? You can listen to the full podcast on Spotify , Apple , and SoundCloud.

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IBM Journey to AI blog

AUGUST 9, 2024

Predictive AI blends statistical analysis with machine learning algorithms to find data patterns and forecast future outcomes. Generative adversarial networks (GANs) consist of two neural networks: a generator that produces new content and a discriminator that evaluates the accuracy and quality of the generated content.

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Marktechpost

JULY 7, 2024

Deep neural networks (DNNs) come in various sizes and structures. The specific architecture selected along with the dataset and learning algorithm used, is known to influence the neural patterns learned. Currently, a major challenge faced in the theory of deep learning is the issue of scalability.

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Marktechpost

OCTOBER 16, 2023

In a recent paper, “Towards Monosemanticity: Decomposing Language Models With Dictionary Learning,” researchers have addressed the challenge of understanding complex neural networks, specifically language models, which are increasingly being used in various applications.

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Towards AI

DECEMBER 12, 2024

We are diving into Mechanistic interpretability, an emerging area of research in AI focused on understanding the inner workings of neural networks. DINN extends DWLR by adding feature interaction terms, creating a neural network architecture. The author provides code and data for reproducibility.

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Towards AI

DECEMBER 12, 2024

We are diving into Mechanistic interpretability, an emerging area of research in AI focused on understanding the inner workings of neural networks. DINN extends DWLR by adding feature interaction terms, creating a neural network architecture. The author provides code and data for reproducibility.

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Towards AI

FEBRUARY 16, 2024

Image adapted from Adobe Stock Machine learning is becoming a significant tool in the field of chemistry, providing new opportunities in various areas such as drug discovery and materials science. Recently, the combination of machine learning and chemistry has made significant progress.

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Towards AI

DECEMBER 12, 2024

We are diving into Mechanistic interpretability, an emerging area of research in AI focused on understanding the inner workings of neural networks. DINN extends DWLR by adding feature interaction terms, creating a neural network architecture. The author provides code and data for reproducibility.

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Marek Rei

NOVEMBER 4, 2024

I have written short summaries of 68 different research papers published in the areas of Machine Learning and Natural Language Processing. link] Proposes an explainability method for language modelling that explains why one word was predicted instead of a specific other word. UC Berkeley, CMU. EMNLP 2022.

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Towards AI

FEBRUARY 3, 2024

(Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. A few weeks ago, I needed an explainability algorithm for a YoloV8 model. The truth is, I couldn’t find anything.

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Unite.AI

SEPTEMBER 4, 2024

Aarki allows brands to effectively engage audiences in a privacy-first world by using billions of contextual bidding signals coupled with proprietary machine learning and behavioral models. Can you elaborate on how Aarki's multi-level machine-learning infrastructure works?

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Towards AI

APRIL 8, 2024

Interconnected graphical data is all around us, ranging from molecular structures to social networks and design structures of cities. Graph Neural Networks (GNNs) are emerging as a powerful method of modeling and learning the spatial and graphical structure of such data. An illustration of GNN: Figure 1.

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Marktechpost

JULY 2, 2024

Concept-based learning (CBL) in machine learning emphasizes using high-level concepts from raw features for predictions, enhancing model interpretability and efficiency. This process enhances explainability in tasks like image and speech recognition. Check out the Paper. Also, don’t forget to follow us on Twitter.

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Unite.AI

APRIL 9, 2024

These techniques include Machine Learning (ML), deep learning , Natural Language Processing (NLP) , Computer Vision (CV) , descriptive statistics, and knowledge graphs. Explainability is essential for accountability, fairness, and user confidence. Explainability also aligns with business ethics and regulatory compliance.

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Marktechpost

JUNE 4, 2024

Modern Deep Neural Networks (DNNs) are inherently opaque; we do not know how or why these computers arrive at the predictions they do. This is a major barrier to the broader use of Machine Learning techniques in many domains. This allows one to examine how these broad ideas impact the predictions made by the network.

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