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.

Deep Learning 321

Deep Learning Artificial Intelligence Artificial Intelligence AI 321

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. wav2letter (now part of Flashlight) appeals to those intrigued by convolutional neural network-based architectures but comes with significant setup challenges.

Python 130

Python Convolutional Neural Networks Neural Network OpenAI 130

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

Marktechpost

APRIL 26, 2024

Graphs are important in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. Foundation Models (FMs) have revolutionized NLP and vision domains in the broader AI spectrum. Alongside topological structure, nodes often possess textual features providing context.

Large Language Models 135

Large Language Models Machine Learning Neural Network Deep Learning 135

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

Generative AI 176

Generative AI Data Scientist Machine Learning BERT 176

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

Machine Learning Algorithm Neural Network Convolutional Neural Networks 111

Unite.AI

SEPTEMBER 13, 2023

Industry Anomaly Detection and Large Vision Language Models Existing IAD frameworks can be categorized into two categories. LLMs or Large Language Models have enjoyed tremendous success in the NLP industry, and they are now being explored for their applications in visual tasks. Reconstruction-based IAD. Feature Embedding-based IAD.

Convolutional Neural Networks 288

Convolutional Neural Networks LLM Neural Network Large Language Models 288

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.

Computer Vision 128

Computer Vision Natural Language Processing Neural Network Categorization 128

Topbots

APRIL 11, 2023

BERT by Google Summary In 2018, the Google AI team introduced a new cutting-edge model for Natural Language Processing (NLP) – BERT , or B idirectional E ncoder R epresentations from T ransformers. 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 BERT Large Language Models Natural Language Processing 98

Snorkel AI

JULY 2, 2024

Extending weak supervision to non-categorical problems Our research presented in our paper “ Universalizing Weak Supervision ” aimed to extend weak supervision beyond its traditional categorical boundaries to more complex, non-categorical problems where rigid categorization isn’t practical.

Categorization 52

Categorization Neural Network Explainability Large Language Models 52

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

Neural Network Computer Vision Deep Learning Machine Learning 98

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. NLP techniques help them parse the nuances of human language, including grammar, syntax and context. Most experts categorize it as a powerful, but narrow AI model.

Neural Network 256

Neural Network LLM Algorithm NLP 256

IBM Journey to AI blog

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data. What is text mining?

Data Mining 170

Data Mining Convolutional Neural Networks Categorization Machine Learning 170

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 NLP Neural Network 98

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 19, 2024

Traditional text-to-SQL systems using deep neural networks and human engineering have succeeded. Evolutionary Process Since its inception, text-to-SQL has seen tremendous growth within the natural language processing (NLP) community, moving from rule-based to deep learning-based methodologies and, most recently, merging PLMs and LLMs.

LLM 103

LLM Neural Network Large Language Models Natural Language Processing 103

Artificial Corner

JULY 30, 2023

Working of Large Language Models (LLMs) Deep neural networks are used in Large language models to produce results based on patterns discovered from training data. Natural Language Processing (NLP) is a subfield of artificial intelligence. A transformer architecture is typically implemented as a Large language model.

Large Language Models 98

Large Language Models LLM Natural Language Processing Deep Learning 98

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 Science 52

Marktechpost

OCTOBER 18, 2023

After these accomplishments, other research has examined the advantages of pre-training large molecular graph neural networks for low-data molecular modeling. Only a small portion of the improvement made by self-supervised models in NLP and CV has yet been produced by low-data modeling attempts by fine-tuning from these models.

Machine Learning 124

Machine Learning Deep Learning Neural Network Natural Language Processing 124

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 131

Machine Learning ML Algorithm Chatbots 131

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

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.

Categorization 96

Categorization BERT Machine Learning Neural Network 96

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

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. Data is sorted and categorized based on keywords and advanced text analysis, and relevant content is highlighted and filed away accordingly.

AI Tools 131

AI Tools Data Science Machine Learning Data Analysis 131

ODSC - Open Data Science

MARCH 13, 2023

They are experts in machine learning, NLP, deep learning, data engineering, MLOps, and data visualization. Fan Staff Software Engineer | Quansight Labs As a maintainer for scikit-learn, an open-source machine learning library for Python, and skorch, a neural network library that wraps PyTorch, Thomas J.

Data Science 80

Data Science Deep Learning Machine Learning Software Engineer 80

Marktechpost

JULY 4, 2023

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

Large Language Models 103

Large Language Models BERT Natural Language Processing Categorization 103

Explosion

NOVEMBER 9, 2016

Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. Most NLP problems can be reduced to machine learning problems that take one or more texts as input. However, most NLP problems require understanding of longer spans of text, not just individual words.

Deep Learning 52

Deep Learning NLP Convolutional Neural Networks Neural Network 52

AWS Machine Learning Blog

APRIL 25, 2024

Calculate a ROUGE-N score You can use the following steps to calculate a ROUGE-N score: Tokenize the generated summary and the reference summary into individual words or tokens using basic tokenization methods like splitting by whitespace or natural language processing (NLP) libraries.

BERT 134

BERT NLP Algorithm Neural Network 134

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 76

Deep Learning NLP Convolutional Neural Networks Neural Network 76

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). Tanveer, M., & Suganthan, P.

BERT 52

BERT NLP Natural Language Processing Algorithm 52

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. Facial recognition: Face recognition uses deep neural networks trained on databases to identify faces in images or videos.

Computer Vision 98

Computer Vision Machine Learning Neural Network Algorithm 98

AWS Machine Learning Blog

APRIL 12, 2023

Our ML models include emotion detection, transcription, and NLP-powered conversational analysis that categorizes violations and provides a rank score to determine how confident it is that a violation has occurred. He is passionate about understanding and manipulating human speech using deep neural networks.

Neural Network 93

Neural Network Machine Learning ML ML Engineer 93

Viso.ai

JULY 18, 2024

LLMs apply powerful Natural Language Processing (NLP), machine translation, and Visual Question Answering (VQA). Categorization of LLMs – Source One of the most common examples of an LLM is a virtual voice assistant such as Siri or Alexa. When you ask, “What is the weather today?

Large Language Models 52

Large Language Models Neural Network Natural Language Processing BERT 52

AWS Machine Learning Blog

OCTOBER 30, 2024

These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval. Sentence transformers are powerful deep learning models that convert sentences into high-quality, fixed-length embeddings, capturing their semantic meaning.

BERT 104

BERT Categorization Data Scientist Machine Learning 104

ODSC - Open Data Science

APRIL 10, 2023

Intermediate Machine Learning with scikit-learn: Pandas Interoperability, Categorical Data, Parameter Tuning, and Model Evaluation Thomas J. Topics covered include Pandas interoperability, categorical data, parameter tuning, and model evaluation. Jon Krohn | Chief Data Scientist | Nebula.io

Software Engineer 52

Software Engineer Categorization Data Science Machine Learning 52

Pickl AI

SEPTEMBER 17, 2024

Deep Learning (DL) is a more advanced technique within Machine Learning that uses artificial neural networks with multiple layers to learn from and make predictions based on data. It involves tasks such as handling missing values, removing outliers, encoding categorical variables, and scaling numerical features.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Machine Learning 52

NVIDIA Developer

JUNE 2, 2021

Applications for natural language processing (NLP) have exploded in the past decade. With the proliferation of AI assistants and organizations infusing their businesses with more interactive human-machine experiences, understanding how NLP techniques can be used to manipulate, analyze, and generate text-based data is essential.

Natural Language Processing 40

Natural Language Processing Neural Network Deep Learning BERT 40