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.

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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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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

APRIL 26, 2024

The advancements in large language models have significantly accelerated the development of natural language processing , or NLP. These extend far beyond the traditional text-based processing of LLMs to include multimodal interactions.

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Chatbots Life

APRIL 16, 2024

This is useful in natural language processing tasks. By applying generative models in these areas, researchers and practitioners can unlock new possibilities in various domains, including computer vision, natural language processing, and data analysis. What are some popular discriminative models?

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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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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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Unite.AI

NOVEMBER 6, 2023

For instance, NN used for computer vision tasks (object detection and image segmentation) are called convolutional neural networks (CNNs) , such as AlexNet , ResNet , and YOLO. Today, generative AI technology is taking neural network techniques one step further, allowing it to excel in various AI domains.

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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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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.

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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.

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Unite.AI

MAY 31, 2023

Lack of Literature Liquid Neural Networks have limited literature on implementation, application, and benefits. They are less widely recognized than Convolutional Neural Networks (CNNs), RNNs, or transformer architecture. Neural networks have evolved from MLP (Multi-Layer Perceptron) to Liquid Neural Networks.

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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.

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Mlearning.ai

MAY 30, 2023

As we know that Convolutional Neural Network (CNN) is used for structured arrays of data such as image data. RNNs contain an internal memory that enables them to capture and use context from prior inputs, in contrast to standard feedforward neural networks, which process data strictly sequentially.

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Viso.ai

MARCH 20, 2024

The main goal of foundation models is to serve as a starting point or foundational building block for many AI tasks, like natural language processing , question answering, text generation, summarization, and even code generation. Each applies foundation models in different ways to handle complex tasks.

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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.

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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.

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Heartbeat

MARCH 8, 2023

Happy Reading, Emilie, Abby & the Heartbeat Team Natural Language Processing With SpaCy (A Python Library) — by Khushboo Kumari This post goes over how the most cutting-edge NLP software, SpaCy , operates. Convolutional, pooling, and fully linked layers are some of the layers that make up a CNN.

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Heartbeat

AUGUST 15, 2023

Advanced techniques have been devised to address these challenges, such as deep learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). RNNs have been extensively employed for natural language processing tasks such as speech recognition and language translation.

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NVIDIA

OCTOBER 18, 2023

Transforming the AI Landscape Generative AI dramatically improves ease of use by understanding human language prompts to make model changes. Those AI models are more flexible in detecting, segmenting, tracking, searching and even reprogramming — and help outperform traditional convolutional neural network -based models.

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Heartbeat

JUNE 6, 2023

GCNs have been successfully applied to many domains, including computer vision and social network analysis. In recent years, researchers have also explored using GCNs for natural language processing (NLP) tasks, such as text classification , sentiment analysis , and entity recognition.

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How to Learn Machine Learning

MAY 14, 2023

When selecting projects, consider tackling problems in different domains, such as natural language processing, computer vision, or recommendation systems. Some popular areas of specialization include natural language processing, computer vision, and reinforcement learning.

Machine Learning 52

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Towards AI

APRIL 27, 2023

Gradient descent is widely used in various machine learning models, such as linear regression, logistic regression, and neural networks. Neural Networks: In neural networks, gradient descent is used to train the weights and biases of the network to minimize the error between the predicted and actual outputs.

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Mlearning.ai

MAY 11, 2023

ChatGPT is an AI language model that has taken the world by storm since its release in 2020. Indeed, this AI is a powerful natural language processing tool that can be used to generate human-like language, making it an ideal tool for creating chatbots, virtual assistants, and other applications that require natural language interactions.

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Mlearning.ai

JULY 11, 2023

You can use libraries like TensorFlow or PyTorch to practice building simple neural networks. Focus on convolutional neural networks (CNNs) for working with images, recurrent neural networks (RNNs) for analyzing sequential data, and generative adversarial networks (GANs) for creating realistic content.

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Topbots

OCTOBER 4, 2023

Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g., Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g.,

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IBM Journey to AI blog

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. Machine learning algorithms like Naïve Bayes and support vector machines (SVM), and deep learning models like convolutional neural networks (CNN) are frequently used for text classification.

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Viso.ai

DECEMBER 22, 2023

Its creators took inspiration from recent developments in natural language processing (NLP) with foundation models. These deep learning models are central to the advancement of machine learning and AI, particularly in the realm of image processing.

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Heartbeat

MAY 30, 2023

Natural Language Processing : DRL has been used for enhancing chatbots, machine translations, speech recognition, etc DRL for robotics; image from TechXplore We could go on and on about Deep Reinforcement Learning’s applications, from training self-driving cars to creating game-playing agents that outperform human players.

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Mlearning.ai

MARCH 30, 2023

To understand this let’s take an example of a widely used neural network, CNN (Convolutional Neural Network) CNN is made of two parts: C — Convolution NN — Neural Network The most important part of training is feature extraction which is done by the convolutional part of the CNN.

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Mlearning.ai

MARCH 6, 2023

Some of the most common types include: Deep Convolutional GANs (DCGANs): These GANs use convolutional neural networks (CNNs) for image generation. Structure of GAN GANs have many applications in computer vision, natural language processing, and audio processing.

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Viso.ai

DECEMBER 27, 2023

In a computer vision example of contrast learning, we aim to train a tool like a convolutional neural network to bring similar image representations closer and separate the dissimilar ones. It typically uses a convolutional neural network (CNN) architecture, like ResNet , for extracting image features.

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Heartbeat

JULY 6, 2023

The process of locating and identifying objects of interest in an image or video is part of the object detection method, a popular computer vision technique. Object detection is typically achieved through the use of deep learning models, particularly Convolutional Neural Networks (CNNs).

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PyImageSearch

MARCH 30, 2023

More importantly, deep learning using neural networks can essentially train machine “brains” to process visual data and retain knowledge of patterns, strategies, and environmental changes over time.

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Pickl AI

AUGUST 1, 2023

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. Deep Learning Models: Deep learning models are a subset of neural networks with multiple layers (deep architectures).

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Viso.ai

FEBRUARY 24, 2023

Self supervised learning Examples Here are some practical examples of self-supervised learning: Example #1: Contrastive Predictive Coding (CPC): a self-supervised learning technique used in natural language processing and computer vision, where the model is trained to predict the next sequence of input tokens.

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TensorFlow

MARCH 29, 2023

ZenDNN is purpose-built to help deep learning application and framework developers improve inference performance on AMD EPYC CPUs across an array of workloads, including computer vision, natural language processing, and recommender systems. TF-ZenDNN We have integrated ZenDNN into high-level AI frameworks for ease of use.

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