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 106

Computer Vision Natural Language Processing Neural Network Categorization 106

IBM Journey to AI blog

DECEMBER 20, 2023

Many retailers’ e-commerce platforms—including those of IBM, Amazon, Google, Meta and Netflix—rely on artificial neural networks (ANNs) to deliver personalized recommendations. Regression algorithms —predict output values by identifying linear relationships between real or continuous values (e.g.,

Machine Learning 187

Machine Learning Neural Network Algorithm Computer Vision 187

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. This model marked a new era in NLP with pre-training of language models becoming a new standard. What is the goal? accuracy on SQuAD 1.1

NLP 98

NLP Large Language Models BERT Natural Language Processing 98

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

Probably Approximately a Scientific Blog

JANUARY 12, 2021

In NLP, dialogue systems generate highly generic responses such as “I don’t know” even for simple questions. Figure 1: adversarial examples in computer vision (left) and natural language processing tasks (right). Is commonsense knowledge already captured by pre-trained language models?

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Natural Language Processing BERT NLP Neural Network 52

ODSC - Open Data Science

MAY 24, 2023

Generative NLP Models in Customer Service: Evaluating Them, Challenges, and Lessons Learned in Banking Editor’s note: The authors are speakers for ODSC Europe this June. Be sure to check out their talk, “ Generative NLP models in customer service. The network’s encoder and decoder were implemented using two LSTMs.

NLP 52

NLP Neural Network Natural Language Processing Data Scientist 52

Unite.AI

AUGUST 4, 2023

It enables an array of NLP applications such as virtual assistants, content generators, question-answering systems, and more, to solve a range of real-world problems. LangChain categorizes its chains into three types: Utility chains, Generic chains, and Combine Documents chains.

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Prompt Engineer Prompt Engineering Python NLP 245

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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Large Language Models LLM Natural Language Processing Deep Learning 85

ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! 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.

Deep Learning 98

Deep Learning Convolutional Neural Networks Neural Network NLP 98

Unite.AI

AUGUST 8, 2023

The Technologies Behind Generative Models Generative models owe their existence to deep neural networks, sophisticated structures designed to mimic the human brain's functionality. By capturing and processing multifaceted variations in data, these networks serve as the backbone of numerous generative models.

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Generative AI ChatGPT Neural Network Convolutional Neural Networks 243

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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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. A specific kind of foundation model known as a large language model (LLM) is trained on vast amounts of text data for NLP tasks. An open-source model, Google created BERT in 2018. All watsonx.ai

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Generative AI Data Scientist BERT Machine Learning 140

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.

Neural Network 97

Neural Network NLP Robotics Natural Language Processing 97

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.

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Large Language Models BERT Natural Language Processing Categorization 91

Heartbeat

MARCH 8, 2023

Heartbeat these past few weeks has had lots of great articles covering the latest research, NLP use-cases, and Comet tutorials. 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 Neural Networks 40

Convolutional Neural Networks Neural Network Natural Language Processing Deep Learning 40

Viso.ai

MARCH 26, 2024

This enhances the interpretability of AI systems for applications in computer vision and natural language processing (NLP). Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. Vaswani et al.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

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

JULY 26, 2023

These neural networks drive the most demanding and compute-intensive generative AI applications, including question answering, code generation, video and image generation, speech recognition and more. “We For its part, Hugging Face is on a mission to democratize good machine learning. “As

Computer Vision 98

Computer Vision Machine Learning Neural Network Natural Language Processing 98

Mlearning.ai

FEBRUARY 15, 2023

Introduction In natural language processing, text categorization tasks are common (NLP). The last Dense layer is the network’s output layer, it takes in a variable shape which can be either 4 or 3, denoting the number of classes for therapist and client. Uysal and Gunal, 2014). Dönicke, T., Manning C.

BERT 52

BERT NLP Natural Language Processing Algorithm 52

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., spam vs. not spam), while generative NLP models can create new text based on a given prompt (e.g., a social media post or product description).

Generative AI 118

Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 118

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 119

Machine Learning Deep Learning Neural Network Natural Language Processing 119

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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Convolutional Neural Networks Data Mining Categorization Machine Learning 134

IBM Journey to AI blog

OCTOBER 16, 2023

Voice-based queries use Natural Language Processing (NLP) and sentiment analysis for speech recognition. This communication can involve speech recognition, speech-to-text conversion, NLP, or text-to-speech. Google Translate uses NLP to translate words across more than 130 languages.

Machine Learning 124

Machine Learning ML Algorithm Chatbots 124

IBM Journey to AI blog

APRIL 18, 2024

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

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Heartbeat

AUGUST 29, 2023

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. Deep learning models use artificial neural networks to learn from data.

Software Development 52

Software Development Deep Learning Neural Network Convolutional Neural Networks 52

Mlearning.ai

JULY 23, 2023

Introducing Natural Language Processing (NLP) , a branch of artificial intelligence (AI) specifically designed to give computers the ability to understand text and spoken words in much the same way as human beings. Text Classification : Categorizing text into predefined categories based on its content.

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Explainability Categorization Algorithm NLP 52

NVIDIA Developer

MAY 20, 2021

Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Duration: 8 Hours Building Transformer-Based Natural Language Processing Applications By Kevin Y.

Natural Language Processing 40

Natural Language Processing Deep Learning Neural Network NLP 40

Snorkel AI

MARCH 1, 2023

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Heartbeat

FEBRUARY 15, 2023

Source: [link] Text classification is an interesting application of natural language processing. As a machine learning engineer, you start with a labeled data set that has vast amounts of text that have already been categorized. Keras provides a simple API to build neural networks and uses Tensorflow as the backend.

ML 52

ML Neural Network Deep Learning Natural Language Processing 52

Heartbeat

JANUARY 12, 2023

Other practical examples of deep learning include virtual assistants, chatbots, robotics, image restoration, NLP (Natural Language Processing), and so on. Neural networks are motivated by the human brain’s organization. Deep learning does this via a multi-layered neural network algorithmic framework.

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

Viso.ai

JANUARY 16, 2024

Artificial Neural Networks (ANNs) are the cornerstone of modern artificial intelligence (AI). They mimic the human brain’s structure, with layers of nodes or “neurons” to process data non-linearly. However, ANNs can also process other forms of content, like music , by analyzing relevant patterns.

Neural Network 52

Neural Network Computer Vision Categorization Algorithm 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.

AI Tools 121

AI Tools Machine Learning Data Science Data Analysis 121

John Snow Labs

JUNE 2, 2023

Sentiment analysis, also known as opinion mining, is the process of computationally identifying and categorizing the subjective information contained in natural language text. Spark NLP has multiple approaches for detecting the sentiment (which is actually a text classification problem) in a text.

Deep Learning 98

Deep Learning NLP Convolutional Neural Networks Neural Network 98

Viso.ai

DECEMBER 20, 2023

Common algorithms and techniques in supervised learning include Neural Networks , Support Vector Machine (SVM), Logistic Regression, Random Forest, or Decision Tree algorithms. How supervised machine learning works Supervised machine learning is the process of training a model to learn from labelled training data.

Computer Vision 98

Computer Vision Neural Network Machine Learning Algorithm 98

Heartbeat

FEBRUARY 8, 2024

Cross-modal retrieval is a branch of computer vision and natural language processing that links visual and verbal descriptions. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively.

Neural Network 52

Neural Network Deep Learning Convolutional Neural Networks Computer Vision 52

Viso.ai

DECEMBER 22, 2023

Its creators took inspiration from recent developments in natural language processing (NLP) with foundation models. This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. . SAM’s game-changing impact lies in its zero-shot inference capabilities.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

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. Solution overview.

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