Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

Neural Network 155

Neural Network Convolutional Neural Networks BERT Deep Learning 155

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.

Neural Network 217

Neural Network Convolutional Neural Networks Robotics Natural Language Processing 217

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. The basic idea of MoEs is to construct a network from a number of expert sub-networks, where each input is processed by a suitable subset of experts.

Deep Learning 118

Deep Learning Algorithm Neural Network BERT 118

AssemblyAI

MAY 3, 2023

This process of adapting pre-trained models to new tasks or domains is an example of Transfer Learning , a fundamental concept in modern deep learning. Transfer learning allows a model to leverage the knowledge gained from one task and apply it to another, often with minimal additional training.

Large Language Models 363

Large Language Models Neural Network LLM ChatGPT 363

Unite.AI

SEPTEMBER 18, 2023

Artificial Intelligence is a very vast branch in itself with numerous subfields including deep learning, computer vision , natural language processing , and more. 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.

Deep Learning 320

Deep Learning Artificial Intelligence Artificial Intelligence AI 320

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. Traditional machine learning feature-based pipeline vs. end-to-end deep learning approach ( source ).

Generative AI 298

Generative AI BERT Neural Network AI 298

ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! In this post, I’ll be demonstrating two deep learning approaches to sentiment analysis. 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

Unite.AI

OCTOBER 22, 2024

In AI, particularly in deep learning , this often means dealing with a rapidly increasing number of computations as models grow in size and handle larger datasets. 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 146

Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 146

Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. They consist of interconnected nodes that learn complex patterns in data. This architecture allows neural networks to learn complex patterns and relationships within data.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Marktechpost

NOVEMBER 14, 2023

The research presents a study on simplifying transformer blocks in deep neural networks, specifically focusing on the standard transformer block. The study examines the simplification of transformer blocks in deep neural networks, focusing specifically on the standard transformer block.

Neural Network 123

Neural Network Deep Learning BERT AI 123

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.

Auto-complete 240

Auto-complete ChatGPT Large Language Models Neural Network 240

Marktechpost

MARCH 25, 2024

Large Language Models (LLMs) like ChatGPT, Google’s Bert, Gemini, Claude Models, and others have emerged as central figures, redefining our interaction with digital interfaces. These models use deep learning techniques, particularly neural networks, to process and produce text that mimics human-like understanding and responses.

ChatGPT 127

ChatGPT Neural Network Large Language Models BERT 127

Unite.AI

DECEMBER 1, 2023

Exploring the Techniques of LIME and SHAP Interpretability in machine learning (ML) and deep learning (DL) models helps us see into opaque inner workings of these advanced models. Flawed Decision Making The opaqueness in the decision-making process of LLMs like GPT-3 or BERT can lead to undetected biases and errors.

LLM 263

LLM Machine Learning Explainability Algorithm 263

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

Unite.AI

JUNE 21, 2024

Models such as GPT, BERT , and more recently Llama , Mistral are capable of understanding and generating human-like text with unprecedented fluency and coherence. The Rise of CUDA-Accelerated AI Frameworks GPU-accelerated deep learning has been fueled by the development of popular AI frameworks that leverage CUDA for efficient computation.

Deep Learning 130

Deep Learning Neural Network Convolutional Neural Networks Large Language Models 130

AWS Machine Learning Blog

AUGUST 2, 2024

We also released a comprehensive study of co-training language models (LM) and graph neural networks (GNN) for large graphs with rich text features using the Microsoft Academic Graph (MAG) dataset from our KDD 2024 paper. GraphStorm provides different ways to fine-tune the BERT models, depending on the task types. Dataset Num.

BERT 110

BERT Neural Network Machine Learning ML 110

ODSC - Open Data Science

FEBRUARY 17, 2023

Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others. Machine & Deep Learning Machine learning is the fundamental data science skillset, and deep learning is the foundation for NLP.

NLP 111

NLP BERT Deep Learning Data Science 111

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 201

Neural Network AI AI BERT 201

Marktechpost

DECEMBER 5, 2023

Models like OpenAI’s ChatGPT and Google Bard require enormous volumes of resources, including a lot of training data, substantial amounts of storage, intricate, deep learning frameworks, and enormous amounts of electricity. What are Small Language Models? million parameters to Medium with 41 million.

BERT 132

BERT Large Language Models Neural Network Natural Language Processing 132

Marktechpost

JUNE 27, 2023

Activation functions for neural networks are an essential part of deep learning since they decide the accuracy and efficiency of the training model used to create or split a large-scale neural network and the output of deep learning models.

Neural Network 52

Neural Network Convolutional Neural Networks Deep Learning NLP 52

Pickl AI

NOVEMBER 13, 2024

Summary: Batch Normalization in Deep Learning improves training stability, reduces sensitivity to hyperparameters, and speeds up convergence by normalising layer inputs. It’s a crucial technique in modern neural networks, enhancing performance and generalisation. The global Deep Learning market, valued at $17.60

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

Mlearning.ai

APRIL 8, 2023

Over the years, we evolved that to solving NLP use cases by adopting Neural Network-based algorithms loosely based on the structure and function of a human brain. 2003) “ Support-vector networks ” by Cortes and Vapnik (1995) Significant people : David Blei Corinna Cortes Vladimir Vapnik 4.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

Unite.AI

DECEMBER 27, 2023

Another breakthrough is the rise of generative language models powered by deep learning algorithms. Facebook's RoBERTa, built on the BERT architecture, utilizes deep learning algorithms to generate text based on given prompts. trillion parameters, making it one of the largest language models ever created.

Generative AI 216

Generative AI Chatbots Large Language Models OpenAI 216

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 Impact V.2

BERT 52

BERT NLP Deep Learning Neural Network 52

IBM Journey to AI blog

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. 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 BERT Machine Learning 176

Marktechpost

APRIL 25, 2024

They use deep learning techniques to process and produce language in a contextually relevant manner. Parameter In the context of neural networks, including LLMs, a parameter is a variable part of the model’s architecture learned from the training data.

Large Language Models 129

Large Language Models LLM Neural Network Natural Language Processing 129

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

Unite.AI

APRIL 2, 2024

Many frameworks employ a generic neural network for a wide range of image restoration tasks, but these networks are each trained separately. Recent deep learning methods have displayed stronger and more consistent performance when compared to traditional image restoration methods.

Computer Vision 130

Computer Vision Neural Network Convolutional Neural Networks Deep Learning 130

Unite.AI

FEBRUARY 23, 2024

The journey continues with “NLP and Deep Learning,” diving into the essentials of Natural Language Processing , deep learning's role in NLP, and foundational concepts of neural networks. It addresses how input prompts function within language models like ChatGPT.

Prompt Engineering 270

Prompt Engineering Prompt Engineer Large Language Models ChatGPT 270

Mlearning.ai

APRIL 21, 2023

Natural Language Processing Transformers, the neural network architecture, that has taken the world of natural language processing (NLP) by storm, is a class of models that can be used for both language and image processing. One of the earliest representation models used in NLP was the Bag of Words (BoW) model.

Neural Network 52

Neural Network Natural Language Processing ChatGPT NLP 52

AssemblyAI

NOVEMBER 8, 2023

  Each stage leverages a deep neural network that operates as a sequence labeling problem but at different granularities: the first network operates at the token level and the second at the character level. We’ve used the DistilBertTokenizer , which inherits from the BERT WordPiece tokenization scheme.

Neural Network 147

Neural Network BERT Large Language Models Deep Learning 147

AssemblyAI

SEPTEMBER 18, 2023

LLMs leverage deep learning architectures to process and understand the nuances and context of human language. LLMs are built upon deep learning, a subset of machine learning. They use neural networks that are inspired by the structure and function of the human brain.

Large Language Models 130

Large Language Models BERT Auto-complete LLM 130

Viso.ai

MARCH 26, 2024

The introduction of the Transformer model was a significant leap forward for the concept of attention in deep learning. 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

Marktechpost

JULY 19, 2024

Traditional text-to-SQL systems using deep neural networks and human engineering have succeeded. Using long short-term memory (LSTM) and transformer deep neural networks, among others, enhanced the ability to generate SQL queries from plain English.

LLM 104

LLM Neural Network Large Language Models Natural Language Processing 104

AWS Machine Learning Blog

SEPTEMBER 16, 2024

The backbone is a BERT architecture made up of 12 encoding layers. DNABERT 6 Dataset For this post, we use the gRNA data released by researchers in a paper about gRNA prediction using deep learning. Otherwise, the architecture of DNABERT is similar to that of BERT. CRISPRon is a CNN based deep learning model.

Natural Language Processing 115

Natural Language Processing Large Language Models BERT Neural Network 115

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 146

OpenAI BERT AI AI 146

Artificial Corner

JULY 30, 2023

Photo by Shubham Dhage on Unsplash Introduction Large language Models (LLMs) are a subset of Deep Learning. 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.

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Large Language Models LLM Natural Language Processing Deep Learning 86