Marktechpost

FEBRUARY 22, 2024

In computer vision, convolutional networks acquire a semantic understanding of images through extensive labeling provided by experts, such as delineating object boundaries in datasets like COCO or categorizing images in ImageNet. This approach has demonstrated effectiveness in natural language processing and reinforcement learning.

Computer Vision 128

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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

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Mlearning.ai

JANUARY 26, 2023

Introduction Natural language processing (NLP) sentiment analysis is a powerful tool for understanding people’s opinions and feelings toward specific topics. NLP sentiment analysis uses natural language processing (NLP) to identify, extract, and analyze sentiment from text data.

Natural Language Processing 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 111

Natural Language Processing Categorization NLP Neural Network 111

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

Unite.AI

AUGUST 22, 2023

We wrote developed custom rules (later more complex neural networks) to predict which customers we should approach with which products at which times to maximize the likelihood of a salesperson’s time resulting in revenue uplift. What was your favorite project and what did you learn from this experience? In September 2022, Search.io

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Unite.AI

SEPTEMBER 18, 2023

Blockchain technology can be categorized primarily on the basis of the level of accessibility and control they offer, with Public, Private, and Federated being the three main types of blockchain technologies. The neural network consists of three types of layers including the hidden layer, the input payer, and the output layer.

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NVIDIA Developer

JUNE 2, 2021

Applications for natural language processing (NLP) have exploded in the past decade. Modern techniques can capture the nuance, context, and sophistication of language, just as humans do. Basic understanding of neural networks.

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Unite.AI

AUGUST 28, 2023

With advancements in deep learning, natural language processing (NLP), and AI, we are in a time period where AI agents could form a significant portion of the global workforce. Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience.

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Marktechpost

NOVEMBER 27, 2024

Classification: Categorizing data into discrete classes (e.g., Sigmoid Kernel: Inspired by neural networks. It’s a simple yet effective algorithm, particularly well-suited for text classification problems like spam filtering, sentiment analysis, and document categorization. Document categorization.

Machine Learning 111

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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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Probably Approximately a Scientific Blog

JANUARY 12, 2021

Figure 1: adversarial examples in computer vision (left) and natural language processing tasks (right). With that said, the path to machine commonsense is unlikely to be brute force training larger neural networks with deeper layers. Is commonsense knowledge already captured by pre-trained language models?

Natural Language Processing 52

Natural Language Processing BERT NLP Neural Network 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.

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Marktechpost

NOVEMBER 11, 2024

Seaborn simplifies the process of creating complex visualizations like heatmaps, scatter plots, and time series plots, making it a popular choice for exploratory data analysis and data storytelling. TensorFlow offers a flexible and scalable platform for building and training complex neural networks.

Data Analysis 89

Data Analysis Python Natural Language Processing Computer Vision 89

IBM Journey to AI blog

AUGUST 1, 2023

A foundation model is built on a neural network model architecture to process information much like the human brain does. The term “foundation model” was coined by the Stanford Institute for Human-Centered Artificial Intelligence in 2021.

Generative AI 176

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Viso.ai

OCTOBER 10, 2024

The identification of regularities in data can then be used to make predictions, categorize information, and improve decision-making processes. While explorative pattern recognition aims to identify data patterns in general, descriptive pattern recognition starts by categorizing the detected patterns.

Neural Network 98

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Artificial Corner

JULY 30, 2023

Photo by Shubham Dhage on Unsplash Introduction Large language Models (LLMs) are a subset of Deep Learning. Image by YouTube video “Introduction to large language models” on YouTube Channel “Google Cloud Tech” What are Large Language Models? A transformer architecture is typically implemented as a Large language model.

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Marktechpost

JULY 19, 2024

Due to the complexity of interpreting user questions, database schemas, and SQL production, accurately generating SQL from natural language queries (text-to-SQL) has been a long-standing difficulty. Traditional text-to-SQL systems using deep neural networks and human engineering have succeeded.

LLM 103

LLM Neural Network Large Language Models Natural Language Processing 103

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. Some common techniques include the following: Sentiment analysis : Sentiment analysis categorizes data based on the nature of the opinions expressed in social media content (e.g., What is text mining?

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Data Mining Convolutional Neural Networks Categorization Machine Learning 170

IBM Journey to AI blog

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles.

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ODSC - Open Data Science

APRIL 28, 2023

If a Natural Language Processing (NLP) system does not have that context, we’d expect it not to get the joke. Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e. Raw text is fed into the Language object, which produces a Doc object.

Deep Learning 98

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Marktechpost

JULY 4, 2023

Natural language processing (NLP) activities, including speech-to-text, sentiment analysis, text summarization, spell-checking, token categorization, etc., rely on Language Models as their foundation. Unigrams, N-grams, exponential, and neural networks are valid forms for the Language Model.

Large Language Models 103

Large Language Models BERT Natural Language Processing Categorization 103

Towards AI

FEBRUARY 20, 2024

If you want an overview of the Machine Learning Process, it can be categorized into 3 wide buckets: Collection of Data: Collection of Relevant data is key for building a Machine learning model. Linear Regression Decision Trees Support Vector Machines Neural Networks Clustering Algorithms (e.g., How Machine Learning Works?

Machine Learning 109

Machine Learning ML Neural Network Algorithm 109

Google Research AI blog

FEBRUARY 23, 2023

Pre-training on diverse datasets has proven to enable data-efficient fine-tuning for individual downstream tasks in natural language processing (NLP) and vision problems. A few crucial design decisions made this possible: Neural network size: We found that multi-game Q-learning required large neural network architectures.

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Marktechpost

OCTOBER 18, 2023

Surprisingly, recent developments in self-supervised learning, foundation models for computer vision and natural language processing, and deep understanding have significantly increased data efficiency. However, most training datasets in the present literature on treatments have small sample sizes.

Machine Learning 124

Machine Learning Deep Learning Neural Network Natural Language Processing 124

Towards AI

AUGUST 25, 2023

Deep learning (DL) is a subset of machine learning that uses neural networks which have a structure similar to the human neural system. There is often confusion between the terms artificial intelligence and machine learning, which is discussed in The AI Process. Speech and Language Processing. Klein, and E.

Machine Learning 98

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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.

Neural Network 59

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 59

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.,

Generative AI 118

Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 118

Pickl AI

APRIL 25, 2023

There are five different subsets of Artificial Intelligence which include Machine Learning, Deep Learning, Robotics, Neural Networks, and NLP. It is important to note that Machine Learning has several subsets including neural networks, deep learning, and reinforcement learning. What is Neural Network?

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Natural Language Processing 52

Marktechpost

SEPTEMBER 8, 2023

Users can upload their data to the AI tool and then choose the variable they wish to predict to have Akkio construct a neural network specifically for that variable. MonkeyLearn’s use of machine learning to streamline business processes and analyze text eliminates the need for countless man-hours of data entry.

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AWS Machine Learning Blog

JANUARY 17, 2023

In this solution, we train and deploy a churn prediction model that uses a state-of-the-art natural language processing (NLP) model to find useful signals in text. In addition to textual inputs, this model uses traditional structured data inputs such as numerical and categorical fields.

Categorization 97

Categorization BERT Machine Learning Neural Network 97

AssemblyAI

JANUARY 23, 2025

Broadly, Python speech recognition and Speech-to-Text solutions can be categorized into two main types: open-source libraries and cloud-based services. This innovative approach spans both acoustic modeling and language modeling, making it a distinctive option in the field of speech recognition.

Python 130

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Marktechpost

OCTOBER 15, 2024

XAI, or Explainable AI, brings about a paradigm shift in neural networks that emphasizes the need to explain the decision-making processes of neural networks, which are well-known black boxes. Additionally, a metric can be categorized into three types: ground_truth, downstream_evaluation, or heuristic.

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IBM Journey to AI blog

OCTOBER 16, 2023

Voice-based queries use Natural Language Processing (NLP) and sentiment analysis for speech recognition. For instance, email management automation tools such as Levity use ML to identify and categorize emails as they come in using text classification algorithms. Companies also take advantage of ML in smartphone cameras.

Machine Learning 131

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Topbots

APRIL 11, 2023

In the rapidly evolving field of artificial intelligence, natural language processing has become a focal point for researchers and developers alike. The Most Important Large Language Models (LLMs) in 2023 1. Text classification for spam filtering, topic categorization, or document organization.

NLP 98

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Heartbeat

OCTOBER 11, 2023

Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach. Interpretability and Explainability: Deep neural networks are one machine learning model that can be very complex and difficult to assess. What is Deep Learning?

Deep Learning 52

Deep Learning Machine Learning Neural Network Natural Language Processing 52

Marktechpost

AUGUST 24, 2024

Large Language Models (LLMs) like GPT-4, Qwen2, and LLaMA have revolutionized artificial intelligence, particularly in natural language processing. It differs from other approaches like LLMs on Graphs, which primarily focus on integrating LLMs with Graph Neural Networks for graph data modeling.

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