Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

Machine Learning 204

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 204

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

Unite.AI

JUNE 26, 2023

AI models are extremely good at solving narrow problems, such as image classification, natural language processing , speech recognition, etc., They lack subjective experience, self-consciousness, or an understanding of context beyond what they have been trained to process. but they don’t possess consciousness.

AI 348

AI AI Neural Network Natural Language Processing 348

Marktechpost

DECEMBER 5, 2023

The Hierarchically Gated Recurrent Neural Network (HGRN) technique developed by researchers from the Shanghai Artificial Intelligence Laboratory and MIT CSAI addresses the challenge of enhancing sequence modeling by incorporating forget gates in linear RNNs. Check out the Paper , Github, and Project.

Neural Network 104

Neural Network Artificial Intelligence Artificial Intelligence Natural Language Processing 104

Dlabs.ai

DECEMBER 9, 2021

Natural language processing (NLP) is a core part of artificial intelligence. Natural Language Processing Succinctly Author : Joseph D. You’ll find all you need to know to build NLP solutions, starting with an overview of natural language processes and what the technology can do.

Natural Language Processing 59

Natural Language Processing Neural Network NLP Deep Learning 59

Towards AI

SEPTEMBER 7, 2023

How is attention computed using Recurrent Neural Networks (RNNs)? Machine Translation We will look at Neural machine translation (NMT) as a running example in this article. NMT aims to build and train a single, large neural network that reads a sentence and outputs a correct translation.

Explainability 126

Explainability Neural Network Natural Language Processing NLP 126

Marktechpost

MAY 5, 2024

The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow. Students learn to implement and analyze models like linear models, kernel machines, neural networks, and graphical models.

Machine Learning 122

Machine Learning Python Neural Network Convolutional Neural Networks 122

AI Weekly

JANUARY 18, 2024

Powered by superai.com In the News Bill Gates explains how AI will change our lives in 5 years It’s no secret that Bill Gates is bullish on artificial intelligence, but he’s now predicting that the technology will be transformative for everyone within the next five years.

Robotics 217

Robotics Artificial Intelligence Artificial Intelligence Machine Learning 217

Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Marktechpost

MARCH 28, 2024

Machine Learning (in Python and R) for Dummies This book explains the fundamentals of machine learning by providing practical examples using Python and R. Neural Networks and Deep Learning This book covers the fundamental concepts of neural networks and deep learning.

Artificial Intelligence 142

Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 142

Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 52

Marktechpost

APRIL 21, 2024

Introduction to Generative AI: This course provides an introductory overview of Generative AI, explaining what it is and how it differs from traditional machine learning methods. It introduces learners to responsible AI and explains why it is crucial in developing AI systems.

Artificial Intelligence 137

Artificial Intelligence Artificial Intelligence BERT Large Language Models 137

IBM Journey to AI blog

JANUARY 10, 2024

AI can also work from deep learning algorithms, a subset of ML that uses multi-layered artificial neural networks (ANNs)—hence the “deep” descriptor—to model high-level abstractions within big data infrastructures. This is where AI programming offers a clear edge over rules-based programming methods.

Artificial Intelligence 229

Artificial Intelligence Artificial Intelligence Neural Network Algorithm 229

Unite.AI

NOVEMBER 8, 2023

Natural Language Processing (NLP) has experienced some of the most impactful breakthroughs in recent years, primarily due to the the transformer architecture. Recurrent Neural Networks (RNNs) became the cornerstone for these applications due to their ability to handle sequential data by maintaining a form of memory.

BERT 298

BERT NLP Neural Network Natural Language Processing 298

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. An emerging area of study called Explainable AI (XAI) has arisen to shed light on how DNNs make decisions in a way that humans can comprehend.

Machine Learning 119

Machine Learning Neural Network Explainability Natural Language Processing 119

Unite.AI

FEBRUARY 16, 2024

Despite achieving remarkable results in areas like computer vision and natural language processing , current AI systems are constrained by the quality and quantity of training data, predefined algorithms, and specific optimization objectives.

Neural Network 195

Neural Network Machine Learning Algorithm Natural Language Processing 195

IBM Journey to AI blog

JANUARY 15, 2024

This technology excels at applying foundation models, which are large neural networks trained on extensive unlabeled data and fine-tuned for various tasks. Last year, the US Department of State sought feedback on the challenges and security considerations of introducing generative and natural language processing AI into its network.

Generative AI 200

Generative AI Responsible AI Neural Network Natural Language Processing 200

Heartbeat

FEBRUARY 8, 2023

Source: Author The field of natural language processing (NLP), which studies how computer science and human communication interact, is rapidly growing. By enabling robots to comprehend, interpret, and produce natural language, NLP opens up a world of research and application possibilities.

Natural Language Processing 52

Natural Language Processing NLP Machine Learning Deep Learning 52

IBM Journey to AI blog

JUNE 14, 2023

If you want to start a different task or solve a new problem, you often must start the whole process over again—it’s a recurring cost. But that’s all changing thanks to pre-trained, open source foundation models. works alongside the platform’s other capabilities by clicking the buttons below.

Automation 258

Automation Neural Network AI AI 258

Pickl AI

NOVEMBER 13, 2024

Summary: Backpropagation in neural network optimises models by adjusting weights to reduce errors. Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems.

Neural Network 52

Neural Network Convolutional Neural Networks Algorithm Natural Language Processing 52

Marktechpost

JUNE 28, 2024

Machine Learning (in Python and R) for Dummies This book explains the fundamentals of machine learning by providing practical examples using Python and R. Neural Networks and Deep Learning This book covers the fundamental concepts of neural networks and deep learning.

Artificial Intelligence 119

Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 119

Marktechpost

MAY 29, 2024

This article lists the top AI courses NVIDIA provides, offering comprehensive training on advanced topics like generative AI, graph neural networks, and diffusion models, equipping learners with essential skills to excel in the field. It also covers how to set up deep learning workflows for various computer vision tasks.

Neural Network 95

Neural Network Natural Language Processing Large Language Models Prompt Engineer 95

Viso.ai

MAY 17, 2024

Neural Networks have changed the way we perform model training. Neural networks, sometimes referred to as Neural Nets, need large datasets for efficient training. So, what if we have a neural network that can adapt itself to new data and has less complexity? What is a Liquid Neural Network?

Neural Network 52

Neural Network Deep Learning Computer Vision Artificial Intelligence 52

Marktechpost

SEPTEMBER 1, 2023

LLMs have become increasingly popular in the NLP (natural language processing) community in recent years. Scaling neural network-based machine learning models has led to recent advances, resulting in models that can generate natural language nearly indistinguishable from that produced by humans.

Large Language Models 138

Large Language Models Neural Network Natural Language Processing LLM 138

Unite.AI

JANUARY 12, 2024

The rise of deep learning reignited interest in neural networks, while natural language processing surged with ChatGPT-level models. MoE architectures combine multiple specialized neural network “experts” optimized for different tasks or data types.

Generative AI 189

Generative AI Neural Network ChatGPT OpenAI 189

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. We will explain intuitively what each one means and how it contributes to the deep learning process.

Deep Learning 126

Deep Learning Neural Network Natural Language Processing Machine Learning 126

Marktechpost

JUNE 9, 2024

It explains the differences between hand-coded algorithms and trained models, the relationship between machine learning and AI, and the impact of data types on training. It also explores neural networks, their components, and the complexity of deep learning.

Artificial Intelligence 99

Artificial Intelligence Artificial Intelligence Natural Language Processing Large Language Models 99

Marktechpost

MAY 30, 2024

Natural Language Processing on Google Cloud This course introduces Google Cloud products and solutions for solving NLP problems. It covers how to develop NLP projects using neural networks with Vertex AI and TensorFlow. Learners will gain hands-on experience with image classification models using public datasets.

Artificial Intelligence 132

Artificial Intelligence Artificial Intelligence BERT Computer Vision 132

Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

Neural Network 158

Neural Network Convolutional Neural Networks BERT Deep Learning 158

IBM Journey to AI blog

AUGUST 13, 2024

Where it all started During the second half of the 20 th century, IBM researchers used popular games such as checkers and backgammon to train some of the earliest neural networks, developing technologies that would become the basis for 21 st -century AI.

Machine Learning 187

Machine Learning Robotics Neural Network Natural Language Processing 187

Marktechpost

NOVEMBER 16, 2024

Machine Learning (in Python and R) for Dummies This book explains the fundamentals of machine learning by providing practical examples using Python and R. Neural Networks and Deep Learning This book covers the fundamental concepts of neural networks and deep learning.

Artificial Intelligence 109

Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 109

Heartbeat

AUGUST 21, 2023

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs are artificial neural networks built to handle data having a grid-like architecture, such as photos or movies.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Natural Language Processing Deep Learning 52

IBM Journey to AI blog

SEPTEMBER 24, 2024

“AI could lead to more accurate and timely predictions, especially for spotting diseases early,” he explains, “and it could help cut down on carbon footprints and environmental impact by improving how we use energy and resources.” We get tired, lose our focus, or just physically can’t see all that we need to.

Explainability 175

Explainability Computer Vision Convolutional Neural Networks AI 175

Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. This process involves utilizing various NLP models and techniques to develop textual descriptions. Various algorithms are employed in image captioning, including: 1.

Natural Language Processing 52

Natural Language Processing Computer Vision NLP Algorithm 52

Viso.ai

JANUARY 2, 2024

In the following, we will explore Convolutional Neural Networks (CNNs), a key element in computer vision and image processing. Whether you’re a beginner or an experienced practitioner, this guide will provide insights into the mechanics of artificial neural networks and their applications. Howard et al.

Convolutional Neural Networks 52

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Marktechpost

MAY 5, 2024

Consequently, there’s been a notable uptick in research within the natural language processing (NLP) community, specifically targeting interpretability in language models, yielding fresh insights into their internal operations. Recent approaches automate circuit discovery, enhancing interpretability.

Natural Language Processing 116

Natural Language Processing Categorization Neural Network NLP 116

Marktechpost

MAY 8, 2024

These technologies have revolutionized computer vision, robotics, and natural language processing and played a pivotal role in the autonomous driving revolution. DRL models, such as Deep Q-Networks (DQN), estimate optimal action policies by training neural networks to approximate the maximum expected future rewards.

Deep Learning 124

Deep Learning Neural Network Data Scarcity Natural Language Processing 124