Towards AI

MARCH 12, 2025

Normalization Trade-off: GPT models preserve formatting & nuance (more token complexity); BERT aggressively cleans text simpler tokens, reduced nuance, ideal for structured tasks. GPT typically preserves contractions, BERT-based models may split. Tokens: Fundamental unit that neural networks process. GPT-4 and GPT-3.5

LLM 54

LLM BERT Neural Network Metadata 54

AWS Machine Learning Blog

JANUARY 19, 2024

In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model performance and reduce inference times. First, we use an Amazon SageMaker Studio notebook to fine-tune a pre-trained BERT model on a target task using a domain-specific dataset.

BERT 120

BERT Automation Neural Network Machine Learning 120

AssemblyAI

MAY 3, 2023

With these fairly complex algorithms often being described as “giant black boxes” in news and media, a demand for clear and accessible resources is surging. Artificial neural networks consist of interconnected layers of nodes, or “neurons” which work together to process and learn from data.

Large Language Models 363

Large Language Models Neural Network LLM ChatGPT 363

Google Research AI blog

FEBRUARY 7, 2023

The explosion in deep learning a decade ago was catapulted in part by the convergence of new algorithms and architectures, a marked increase in data, and access to greater compute. Below, we highlight a panoply of works that demonstrate Google Research’s efforts in developing new algorithms to address the above challenges.

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Deep Learning Algorithm Neural Network ML 118

Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube. You can find other posts in the series here.)

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Algorithm Neural Network Auto-classification ML 110

Unite.AI

AUGUST 2, 2023

To tackle the issue of single modality, Meta AI released the data2vec, the first of a kind, self supervised high-performance algorithm to learn patterns information from three different modalities: image, text, and speech. Why Does the AI Industry Need the Data2Vec Algorithm?

Computer Vision 246

Computer Vision Algorithm Natural Language Processing Convolutional Neural Networks 246

Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Towards AI

JUNE 3, 2024

BERT is a state-of-the-art algorithm designed by Google to process text data and convert it into vectors ([link]. What makes BERT special is, apart from its good results, the fact that it is trained over billions of records and that Hugging Face provides already a good battery of pre-trained models we can use for different ML tasks.

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BERT Neural Network Algorithm Data Analysis 84

Marktechpost

DECEMBER 12, 2024

A Deep Neural Network (DNN) is an artificial neural network that features multiple layers of interconnected nodes, also known as neurons. The deep aspect of DNNs comes from multiple hidden layers, which allow the network to learn and model complex patterns and relationships in data.

Neural Network 57

Neural Network Convolutional Neural Networks Natural Language Processing Artificial Intelligence 57

Unite.AI

OCTOBER 22, 2024

This term refers to how much time, memory, or processing power an algorithm requires as the size of the input grows. AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands.

Neural Network 147

Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 147

Marktechpost

APRIL 21, 2024

Transformer Models and BERT Model : In this course, participants delve into the specifics of Transformer models and the Bidirectional Encoder Representations from Transformers (BERT) model. These courses provide a perfect foundation in AI, from understanding basic concepts to exploring advanced algorithms and architectures.

Artificial Intelligence 137

Artificial Intelligence Artificial Intelligence BERT Large Language Models 137

Unite.AI

DECEMBER 1, 2023

Machine learning , a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). The culmination of this training is a machine-learning model. Impact of the LLM Black Box Problem 1.

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LLM Machine Learning Explainability Algorithm 264

Unite.AI

SEPTEMBER 18, 2023

NLP in particular has been a subfield that has been focussed heavily in the past few years that has resulted in the development of some top-notch LLMs like GPT and BERT. 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

JUNE 27, 2023

Deep Neural Networks (DNNs) have proven to be exceptionally adept at processing highly complicated modalities like these, so it is unsurprising that they have revolutionized the way we approach audio data modeling. At its core, it's an end-to-end neural network-based approach. The EnCodec architecture ( source ).

Generative AI 298

Generative AI BERT Neural Network AI 298

Unite.AI

AUGUST 1, 2023

OpenAI has been instrumental in developing revolutionary tools like the OpenAI Gym, designed for training reinforcement algorithms, and GPT-n models. Prompt 1 : “Tell me about Convolutional Neural Networks.” Unlike other neural networks, they leverage convolutional layers and pooling layers to process images.

Prompt Engineer 246

Prompt Engineer Prompt Engineering ChatGPT Convolutional Neural Networks 246

Pickl AI

NOVEMBER 26, 2024

Summary: Recurrent Neural Networks (RNNs) are specialised neural networks designed for processing sequential data by maintaining memory of previous inputs. Introduction Neural networks have revolutionised data processing by mimicking the human brain’s ability to recognise patterns.

Neural Network 52

Neural Network Natural Language Processing NLP Deep Learning 52

NVIDIA

FEBRUARY 11, 2025

They said transformer models , large language models (LLMs), vision language models (VLMs) and other neural networks still being built are part of an important new category they dubbed foundation models. Earlier neural networks were narrowly tuned for specific tasks. See chart below.) The field continues to move fast.

Neural Network 56

Neural Network Large Language Models Robotics BERT 56

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.

Convolutional Neural Networks 130

Convolutional Neural Networks Neural Network BERT Machine Learning 130

Marktechpost

MARCH 20, 2025

Case studies from five cities demonstrate reductions in carbon emissions and improvements in quality of life metrics." }, { "id": 6, "title": "Neural Networks for Computer Vision", "abstract": "Convolutional neural networks have revolutionized computer vision tasks.

Natural Language Processing 59

Natural Language Processing Convolutional Neural Networks Neural Network Computer Vision 59

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. NLP algorithms help computers understand, interpret, and generate natural language.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

Marktechpost

APRIL 25, 2024

Understanding the terminology, from the foundational aspects of training and fine-tuning to the cutting-edge concepts of transformers and reinforcement learning, is the first step towards demystifying the powerful algorithms that drive modern AI language systems.

Large Language Models 130

Large Language Models LLM Neural Network Natural Language Processing 130

Unite.AI

DECEMBER 27, 2023

By utilizing machine learning algorithms , it produces new content, including images, text, and audio, that resembles existing data. Another breakthrough is the rise of generative language models powered by deep learning algorithms. Generative AI is an evolving field that has experienced significant growth and progress in 2023.

Generative AI 217

Generative AI Chatbots Large Language Models OpenAI 217

Unite.AI

JULY 25, 2024

A significant breakthrough came with neural networks and deep learning. Models like Google's Neural Machine Translation (GNMT) and Transformer revolutionized language processing by enabling more nuanced, context-aware translations. IBM's Model 1 and Model 2 laid the groundwork for advanced systems.

Neural Network 202

Neural Network AI AI Machine Learning 202

Mlearning.ai

MARCH 2, 2023

Pre-training of Deep Bidirectional Transformers for Language Understanding BERT is a language model that can be fine-tuned for various NLP tasks and at the time of publication achieved several state-of-the-art results. Finally, the impact of the paper and applications of BERT are evaluated from today’s perspective. 1 Architecture III.2

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BERT NLP Deep Learning Neural Network 52

IBM Journey to AI blog

AUGUST 1, 2023

Foundation models: The driving force behind generative AI Also known as a transformer, a foundation model is an AI algorithm trained on vast amounts of broad data. A foundation model is built on a neural network model architecture to process information much like the human brain does.

Generative AI 176

Generative AI Data Scientist Machine Learning BERT 176

Unite.AI

AUGUST 28, 2023

Traditional Computing Systems : From basic computing algorithms, the journey began. Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience. These systems could solve pre-defined tasks using a fixed set of rules.

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Auto-complete ChatGPT Large Language Models Neural Network 240

Marktechpost

JANUARY 15, 2025

It employs artificial neural networks with multiple layershence the term deepto model intricate patterns in data. Each layer in a neural network extracts progressively abstract features from the data, enabling these models to understand and process complex patterns.

Deep Learning 87

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 87

Heartbeat

MARCH 15, 2023

An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019. What is RoBERTa?

BERT 52

BERT NLP Deep Learning Neural Network 52

TheSequence

NOVEMBER 24, 2024

Created Using Midjourney Next Week in The Sequence: Edge 451: Explores the ideas behind multi-teacher distillation including the MT-BERT paper. The system leverages a recurrent, transformer-based neural network architecture inspired by the successful use of Transformers in large language models (LLMs).

Neural Network 52

Neural Network BERT Large Language Models LLM 52

ODSC - Open Data Science

FEBRUARY 17, 2023

TensorFlow is desired for its flexibility for ML and neural networks, PyTorch for its ease of use and innate design for NLP, and scikit-learn for classification and clustering. NLTK is appreciated for its broader nature, as it’s able to pull the right algorithm for any job.

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NLP Data Science Deep Learning BERT 111

ODSC - Open Data Science

MARCH 12, 2025

The Boom of Generative AI and Large Language Models(LLMs) 20182020: NLP was gaining traction, with a focus on word embeddings, BERT, and sentiment analysis. The Decline of Traditional MachineLearning 20182020: Algorithms like random forests, SVMs, and gradient boosting were frequent discussion points.

Data Science 52

Data Science ETL Machine Learning AI Engineer 52

Marktechpost

JULY 22, 2024

Transformer architecture has emerged as a major leap in natural language processing, significantly outperforming earlier recurrent neural networks. Transformers consist of encoder and decoder components, each comprising multiple layers with self-attention mechanisms and feed-forward neural networks.

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Large Language Models Neural Network Natural Language Processing LLM 127

Google Research AI blog

FEBRUARY 23, 2023

Posted by Aviral Kumar, Student Researcher, and Sergey Levine, Research Scientist, Google Research Reinforcement learning (RL) algorithms can learn skills to solve decision-making tasks like playing games , enabling robots to pick up objects , or even optimizing microchip designs.

Neural Network 97

Neural Network NLP Robotics Natural Language Processing 97

John Snow Labs

JUNE 6, 2023

Transformers are defined as a specific type of neural network architecture that have proven to be particularly effective for sequence classification tasks, thanks to their ability to capture long-term dependencies and contextual relationships in the data. The transformer architecture was introduced by Vaswani et al.

BERT 52

BERT Python NLP Neural Network 52

Unite.AI

JULY 16, 2024

This is typically done using large language models like BERT or GPT. signed distance functions) Neural radiance fields (NeRFs) : Neural networks representing density and color in 3D space Each has trade-offs in terms of resolution, memory usage, and ease of generation.

OpenAI 147

OpenAI BERT Neural Network AI 147

Mlearning.ai

FEBRUARY 15, 2023

transformer.ipynb” uses the BERT architecture to classify the behaviour type for a conversation uttered by therapist and client, i.e, Figure 4 Data Cleaning Conventional algorithms are often biased towards the dominant class, ignoring the data distribution. the same result we are trying to achieve with “multi_class_classifier.ipynb”.

BERT 52

BERT NLP Natural Language Processing Algorithm 52

AWS Machine Learning Blog

APRIL 25, 2024

The most common techniques used for extractive summarization are term frequency-inverse document frequency (TF-IDF), sentence scoring, text rank algorithm, and supervised machine learning (ML). It uses BERT, a popular NLP technique, to understand the meaning and context of words in the candidate summary and reference summary.

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BERT NLP Algorithm Neural Network 130