Marktechpost

SEPTEMBER 17, 2024

In deep learning, neural network optimization has long been a crucial area of focus. Training large models like transformers and convolutional networks requires significant computational resources and time. Researchers have been exploring advanced optimization techniques to make this process more efficient.

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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

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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

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Analytics Vidhya

JULY 20, 2020

Introduction With the advancement in deep learning, neural network architectures like recurrent neural networks (RNN and LSTM) and convolutional neural networks (CNN) have shown. The post Transfer Learning for NLP: Fine-Tuning BERT for Text Classification appeared first on Analytics Vidhya.

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Mlearning.ai

MAY 30, 2023

Recurrent Neural Networks (RNNs) have become a potent tool for analysing sequential data in the large subject of artificial intelligence and machine learning. As we know that Convolutional Neural Network (CNN) is used for structured arrays of data such as image data. RNN is used for sequential data.

Neural Network 52

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Becoming Human

MARCH 16, 2023

This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications. Through the next series of this post here , I will try to make an implementation of Graph Convolutional Neural Network. How do Graph Neural Networks work?

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Pickl AI

NOVEMBER 22, 2024

Summary: Long Short-Term Memory (LSTM) networks are a specialised form of Recurrent Neural Networks (RNNs) that excel in learning long-term dependencies in sequential data. Understanding Recurrent Neural Networks (RNNs) To appreciate LSTMs, it’s essential to understand RNNs.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Natural Language Processing NLP 52

Unite.AI

OCTOBER 22, 2024

AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands. Computer vision tasks rely heavily on matrix operations and have also used sub-quadratic techniques to streamline convolutional processes.

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Marktechpost

JULY 31, 2024

Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics. Natural Language Processing in TensorFlow This course focuses on building natural language processing systems using TensorFlow.

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Neural Network Auto-classification Natural Language Processing Convolutional Neural Networks 110

AI Weekly

JULY 27, 2023

techcrunch.com The Essential Artificial Intelligence Glossary for Marketers (90+ Terms) BERT - Bidirectional Encoder Representations from Transformers (BERT) is Google’s deep learning model designed explicitly for natural language processing tasks like answering questions, analyzing sentiment, and translation.

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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

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AI Weekly

OCTOBER 26, 2023

cryptopolitan.com Applied use cases Alluxio rolls out new filesystem built for deep learning Alluxio Enterprise AI is aimed at data-intensive deep learning applications such as generative AI, computer vision, natural language processing, large language models and high-performance data analytics.

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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.

Deep Learning 137

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Marktechpost

MAY 5, 2024

The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow. It covers topics like image processing, cluster analysis, gradient boosting, and popular libraries like scikit-learn, Spark, and Keras.

Machine Learning 122

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Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. These algorithms can learn and extract intricate features from input images by using convolutional layers.

Natural Language Processing 52

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Marktechpost

JUNE 3, 2024

Deep Learning This course introduces deep learning and covers its techniques, terminology, and fundamental neural network architectures. By the end, students will understand network construction, kernels, and expanding networks using transfer learning. It also explores CNNs, TFRecord, and transfer learning.

Convolutional Neural Networks 124

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 124

Viso.ai

OCTOBER 9, 2024

Emotion AI, also called Affective Computing, is a rapidly growing branch of Artificial Intelligence allowing computers to analyze and understand human language nonverbal signs such as facial expressions, body language, gestures, and voice tones to assess their emotional state. Get a demo for your organization.

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Unite.AI

MAY 24, 2024

In modern machine learning and artificial intelligence frameworks, transformers are one of the most widely used components across various domains including GPT series, and BERT in Natural Language Processing, and Vision Transformers in computer vision tasks.

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Unite.AI

SEPTEMBER 21, 2023

Organizations and practitioners build AI models that are specialized algorithms to perform real-world tasks such as image classification, object detection, and natural language processing. Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models.

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Marktechpost

MARCH 30, 2024

Vision Language Models (VLMs) emerge as a result of a unique integration of Computer Vision (CV) and Natural Language Processing (NLP). These innovations enable Mini-Gemini to process high-resolution images effectively and generate context-rich visual and textual content, setting it apart from existing models.

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Heartbeat

MAY 2, 2023

Neural Networks Neural networks are a popular deep learning algorithm that are inspired by the structure and function of the human brain. They have several use cases, from image recognition to natural language processing and self-driving vehicles.

Deep Learning 52

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Marktechpost

MARCH 26, 2024

The transformer architecture has improved natural language processing, with recent advancements achieved through scaling efforts from millions to billion-parameter models. Observations indicate diminishing returns with increased model depth, mirroring challenges in deep convolutional neural networks for computer vision.

Convolutional Neural Networks 131

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Pickl AI

NOVEMBER 20, 2024

Example In Natural Language Processing (NLP), word embeddings are often represented as vectors. Example In Deep Learning, neural networks use matrices to represent weights between layers. The forward pass through the network involves matrix multiplication to compute outputs from inputs.

Machine Learning 52

Machine Learning Neural Network Natural Language Processing Convolutional Neural Networks 52

John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. A VQA system takes free-form, text-based questions about an input image and presents answers in a natural language format.

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Natural Language Processing Deep Learning NLP Convolutional Neural Networks 40

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?

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Marktechpost

MAY 24, 2024

Transformers have greatly transformed natural language processing, delivering remarkable progress across various applications. Previous studies have explored methods like backpropagation and fine-tuning to understand sparsity in convolutional neural networks.

Convolutional Neural Networks 122

Convolutional Neural Networks Neural Network Natural Language Processing Machine Learning 122

Chatbots Life

APRIL 16, 2024

Variational Autoencoders (VAEs) : VAEs are neural networks that learn the underlying distribution of the input data and generate new data points. Generative Adversarial Networks (GANs) : GANs employ two neural networks : a generator that creates data and a discriminator that checks if it’s real.

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Pickl AI

SEPTEMBER 16, 2024

Introduction Deep Learning, a subset of Machine Learning , leverages Complex Neural Networks (CNNs) to model and solve intricate problems. Neural networks , inspired by the human brain, consist of interconnected nodes that process data and learn patterns.

Deep Learning 52

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ODSC - Open Data Science

JULY 18, 2023

Series Introduction The first session will provide you with an introduction to this series, as well as the fundamentals of machine learning and deep learning with a focus on fully connected and convolution neural networks. Sign me up! What’s more, these four sessions are only the start.

Deep Learning 52

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Explosion

JUNE 12, 2023

After training on the annotation process, the annotation tool, and the supporting neuro-ontology, three raters annotated 15 clinical notes in three rounds. A machine annotator based on a convolutional neural network had a high level of agreement with the human annotators but one that was lower than human inter-rater agreement.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Natural Language Processing Automation 52

Marktechpost

NOVEMBER 10, 2023

Traditionally, Convolutional Neural Networks (CNNs) have been the go-to models for processing image data, leveraging their ability to extract meaningful features and classify visual information.

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Marktechpost

JULY 16, 2023

The Vision Transformer (ViT) rapidly replaces convolution-based neural networks because of its simplicity, flexibility, and scalability. Feeding data into a deep neural network during training and operation in batches is common practice.

Convolutional Neural Networks 98

Convolutional Neural Networks Neural Network Computer Vision Natural Language Processing 98

Unite.AI

MARCH 26, 2024

The development of Large Language Models (LLMs) built from decoder-only transformer models has played a crucial role in transforming the Natural Language Processing (NLP) domain, as well as advancing diverse deep learning applications including reinforcement learning , time-series analysis, image processing, and much more.

Natural Language Processing 147

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Pickl AI

AUGUST 3, 2023

These algorithms have shown remarkable success in solving a wide range of complex tasks, such as image recognition, natural language processing, speech recognition, and more. At the core of Deep Learning is the artificial neural network (ANN), which is inspired by the structure and function of the human brain.

Deep Learning 52

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

NLP 98

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Viso.ai

DECEMBER 10, 2023

The most popular machine learning method is deep learning, where multiple hidden layers of a neural network are used in a model. The Process of Image Recognition Systems There are a few steps that are at the backbone of how image recognition systems work. CNNs are unmatched by traditional machine learning methods.

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

Viso.ai

OCTOBER 10, 2024

In supervised learning, images are annotated to train neural networks – Image Annotation with Viso Suite What Is the Goal of Pattern Recognition? The goal of pattern recognition is based on the idea that the decision-making process of a human being is somewhat related to the recognition of patterns.

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