Marktechpost

MAY 8, 2024

Over the past decade, advancements in deep learning and artificial intelligence have driven significant strides in self-driving vehicle technology. Deep learning and AI technologies play crucial roles in both modular and End2End systems for autonomous driving. Classical methodologies for these tasks are also explored.

Deep Learning 121

Deep Learning Neural Network Data Scarcity Natural Language Processing 121

Marktechpost

MAY 14, 2024

Deep learning is crucial in today’s age as it powers advancements in artificial intelligence, enabling applications like image and speech recognition, language translation, and autonomous vehicles. Additionally, it offers insights into the diverse range of deep learning techniques applied across various industrial sectors.

Neural Network 108

Neural Network Deep Learning Computer Vision Python 108

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.

Explainability 126

Explainability Explainable AI Neural Network Algorithm 126

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.

Deep Learning 127

Deep Learning Python Neural Network Artificial Intelligence 127

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.

Deep Learning 124

Deep Learning Neural Network ML Computer Vision 124

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 129

Deep Learning Neural Network Machine Learning Explainability 129

Unite.AI

NOVEMBER 6, 2023

Traditional machine learning is a broad term that covers a wide variety of algorithms primarily driven by statistics. The two main types of traditional ML algorithms are supervised and unsupervised. These algorithms are designed to develop models from structured datasets. K-means Clustering. K-means Clustering.

Machine Learning 204

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 204

Marktechpost

APRIL 24, 2024

Explainable AI (XAI) aims to balance model explainability with high learning performance, fostering human understanding, trust, and effective management of AI partners. ELI5 is a Python package that helps debug machine learning classifiers and explain their predictions.

Explainable AI 116

Explainable AI Explainability Neural Network Data Scientist 116

Unite.AI

DECEMBER 1, 2023

Machine learning , a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). The culmination of this training is a machine-learning model.

LLM 264

LLM Machine Learning Explainability Algorithm 264

Marktechpost

OCTOBER 8, 2023

The researchers emphasize that this approach of explainability examines an AI’s full prediction process from input to output. The research group has already created techniques for using heat maps to demonstrate how AI algorithms make judgments. If you like our work, you will love our newsletter. We are also on WhatsApp.

Deep Learning 140

Deep Learning Neural Network Explainability Artificial Intelligence 140

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.

Deep Learning 126

Deep Learning Neural Network Natural Language Processing Machine Learning 126

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.

Deep Learning 111

Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111

AssemblyAI

NOVEMBER 21, 2022

At the bedrock of the Deep Learning that powers incredible technologies like text-to-image models lies matrix multiplication. Regardless of the specific architecture employed, (nearly) every Neural Network relies on efficient matrix multiplication to learn and infer.

Explainability 130

Explainability Neural Network Algorithm Deep Learning 130

PyImageSearch

OCTOBER 14, 2024

This blog post is the 1st of a 3-part series on 3D Reconstruction: Photogrammetry Explained: From Multi-View Stereo to Structure from Motion (this blog post) 3D Reconstruction: Have NeRFs Removed the Need for Photogrammetry? To learn about 3D Reconstruction, just keep reading. 3D Gaussian Splatting: The End Game of 3D Reconstruction?

Explainability 105

Explainability Computer Vision Deep Learning Algorithm 105

Marktechpost

MARCH 28, 2024

Deep Learning (Adaptive Computation and Machine Learning series) This book covers a wide range of deep learning topics along with their mathematical and conceptual background. It also provides information on the different deep learning techniques used in various industrial applications.

Artificial Intelligence 142

Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 142

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.

Composite AI 182

Composite AI Explainability Black Box AI Explainable AI 182

PyImageSearch

OCTOBER 28, 2024

Home Table of Contents NeRFs Explained: Goodbye Photogrammetry? Block #A: We Begin with a 5D Input Block #B: The Neural Network and Its Output Block #C: Volumetric Rendering The NeRF Problem and Evolutions Summary and Next Steps Next Steps Citation Information NeRFs Explained: Goodbye Photogrammetry? How Do NeRFs Work?

Explainability 86

Explainability Neural Network Computer Vision Deep Learning 86

Marktechpost

AUGUST 15, 2023

Data may be viewed as having a structure in various areas that explains how its components fit together to form a greater whole. Most current deep-learning models make no explicit attempt to represent the intermediate structure and instead seek to predict output variables straight from the input.

Deep Learning 98

Deep Learning Algorithm Explainability Machine Learning 98

AI Weekly

APRIL 11, 2024

And this is particularly true for accounts payable (AP) programs, where AI, coupled with advancements in deep learning, computer vision and natural language processing (NLP), is helping drive increased efficiency, accuracy and cost savings for businesses. Answering them, he explained, requires an interdisciplinary approach.

Robotics 221

Robotics Artificial Intelligence Artificial Intelligence Data Drift 221

Analytics Vidhya

JUNE 18, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon This article explains the problem of exploding and vanishing gradients while. The post The Challenge of Vanishing/Exploding Gradients in Deep Neural Networks appeared first on Analytics Vidhya.

Neural Network 329

Neural Network Data Science Explainability Algorithm 329

Marktechpost

NOVEMBER 20, 2024

Others have proposed a hybrid network intrusion detection system integrating convolutional neural networks (CNN), fuzzy C-means clustering, genetic algorithm, and a bagging classifier. By hybridizing optimization techniques with deep belief networks, the method aims to enhance DDoS attack detection accuracy, speed, and scalability.

Deep Learning 82

Deep Learning Convolutional Neural Networks Neural Network Algorithm 82

Unite.AI

MAY 8, 2024

My interest in machine learning (ML) was a gradual process. During my school years, I spent a lot of time studying math, probability theory, and statistics, and got an opportunity to play with classical machine learning algorithms such as linear regression and KNN.

Machine Learning 298

Machine Learning Software Engineer Large Language Models Data Analysis 298

IBM Journey to AI blog

JANUARY 10, 2024

AI operates on three fundamental components: data, algorithms and computing power. Data: AI systems learn and make decisions based on data, and they require large quantities of data to train effectively, especially in the case of machine learning (ML) models. What is artificial intelligence and how does it work?

Artificial Intelligence 229

Artificial Intelligence Artificial Intelligence Neural Network Algorithm 229

Marktechpost

JULY 22, 2023

Recent achievements in supervised tasks of deep learning can be attributed to the availability of large amounts of labeled training data. Semi-supervised learning (SSL) aims to boost model performance using labeled and unlabeled input. Yet it takes a lot of effort and money to collect accurate labels.

Machine Learning 98

Machine Learning Algorithm Deep Learning Neural Network 98

AWS Machine Learning Blog

JANUARY 25, 2023

Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. This field is often referred to as explainable artificial intelligence (XAI). In this post, we illustrate the use of Clarify for explaining NLP models.

Explainability 98

Explainability Natural Language Processing Algorithm Machine Learning 98

Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube. You can find other posts in the series here.)

Algorithm 110

Algorithm Neural Network Auto-classification ML 110

IBM Journey to AI blog

JUNE 26, 2023

Possibilities are growing that include assisting in writing articles, essays or emails; accessing summarized research; generating and brainstorming ideas; dynamic search with personalized recommendations for retail and travel; and explaining complicated topics for education and training. What is generative AI? What is watsonx.governance?

Generative AI 203

Generative AI Explainability Explainable AI Natural Language Processing 203

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. As soon as the system adapts to human wants, it automates the learning process accordingly.

Computer Vision 84

Computer Vision Deep Learning Neural Network Artificial Intelligence 84

Marktechpost

JUNE 28, 2024

Deep Learning (Adaptive Computation and Machine Learning series) This book covers a wide range of deep learning topics along with their mathematical and conceptual background. It also provides information on the different deep learning techniques used in various industrial applications.

Artificial Intelligence 115

Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 115

Towards AI

JULY 17, 2023

About this series In this series, we will learn how to code the must-to-know deep learning algorithms such as convolutions, backpropagation, activation functions, optimizers, deep neural networks, and so on using only plain and modern C++. The problem often is: how to know the F() formula?

Deep Learning 95

Deep Learning Machine Learning Neural Network Artificial Intelligence 95

Towards AI

MAY 13, 2024

This synergy enables AI supercomputers to leverage HPC capabilities, optimizing performance for demanding AI tasks like training deep learning models or image recognition algorithms. When it comes to large AI model training, supercomputers sound like an even bigger deal.

AI Modeling 109

AI Modeling Explainability Deep Learning AI 109

Analytics Vidhya

NOVEMBER 6, 2020

Today I am going to try my best in explaining. This article was published as a part of the Data Science Blogathon. Introduction Hello! The post A Short Intuitive Explanation of Convolutional Recurrent Neural Networks appeared first on Analytics Vidhya.

Neural Network 291

Neural Network Data Science Explainability Convolutional Neural Networks 291

Marktechpost

NOVEMBER 16, 2024

Deep Learning (Adaptive Computation and Machine Learning series) This book covers a wide range of deep learning topics along with their mathematical and conceptual background. It also provides information on the different deep learning techniques used in various industrial applications.

Artificial Intelligence 106

Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 106

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.

Algorithm 90

Algorithm Convolutional Neural Networks Neural Network Computer Vision 90

IBM Journey to AI blog

SEPTEMBER 27, 2023

Large language models (LLMs) are foundation models that use artificial intelligence (AI), deep learning and massive data sets, including websites, articles and books, to generate text, translate between languages and write many types of content.

Large Language Models 289

Large Language Models LLM Explainability Chatbots 289

AssemblyAI

NOVEMBER 9, 2023

The models are powered by advanced Deep Learning and Machine Learning research. In Natural Language Processing, or NLP, Text Summarization refers to the process of using Deep Learning and Machine Learning models to synthesize large bodies of texts into their most important parts.

NLP 257

NLP AI Modeling AI AI 257

Unite.AI

JUNE 22, 2023

At Digits, and with the launch of Digits AI, we’re addressing this at the foundational level, reimagining the basis of financial accounting through the lens of the latest machine learning technology and modern software architecture. Can you discuss the types of machine learning algorithms that are used?

Large Language Models 325

Large Language Models Machine Learning ChatGPT Deep Learning 325