Towards AI

JANUARY 29, 2025

Generative AI is powered by advanced machine learning techniques, particularly deep learning and neural networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). GPT, BERT) Image Generation (e.g., Programming: Learn Python, as its the most widely used language in AI/ML.

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AI Engineer Generative AI Neural Network BERT 98

Pickl AI

JANUARY 26, 2025

Summary: Deep Learning models revolutionise data processing, solving complex image recognition, NLP, and analytics tasks. Introduction Deep Learning models transform how we approach complex problems, offering powerful tools to analyse and interpret vast amounts of data. With a projected market growth from USD 6.4

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Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

Marktechpost

NOVEMBER 19, 2024

However, traditional deep learning methods often struggle to interpret the semantic details in log data, typically in natural language. The study reviews approaches to log-based anomaly detection, focusing on deep learning methods, especially those using pretrained LLMs. higher than the best alternative, NeuralLog.

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Large Language Models BERT Prompt Engineer Prompt Engineering 80

Unite.AI

DECEMBER 8, 2023

These systems, typically deep learning models, are pre-trained on extensive labeled data, incorporating neural networks for self-attention. This article introduces UltraFastBERT, a BERT-based framework matching the efficacy of leading BERT models but using just 0.3%

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Marktechpost

MAY 5, 2024

In deep learning, especially in NLP, image analysis, and biology, there is an increasing focus on developing models that offer both computational efficiency and robust expressiveness. The model outperforms traditional attention-based models, such as BERT and Vision Transformers, across domains with smaller model sizes.

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Deep Learning BERT Neural Network Natural Language Processing 134

Marktechpost

NOVEMBER 17, 2024

While deep learning models have achieved state-of-the-art results in this area, they require large amounts of labeled data, which is costly and time-consuming. Active learning helps optimize this process by selecting the most informative unlabeled samples for annotation, reducing the labeling effort.

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Deep Learning BERT Automation ML 99

Unite.AI

FEBRUARY 10, 2025

AI for Context-Aware Search With the integration of AI, search engines started getting more innovative, learning to understand what users meant behind the keywords rather than just matching them. Technologies like Google's RankBrain and BERT have played a vital role in enhancing contextual understanding of search engines.

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OpenAI Generative AI Artificial Intelligence Artificial Intelligence 191

Unite.AI

SEPTEMBER 20, 2023

These are deep learning models used in NLP. Machine learning is about teaching computers to perform tasks by recognizing patterns, while deep learning, a subset of machine learning, creates a network that learns independently. We choose a BERT model fine-tuned on the SQuAD dataset.

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LLM NLP BERT Python 341

Towards AI

OCTOBER 19, 2024

In this guide, we will explore how to fine-tune BERT, a model with 110 million parameters, specifically for the task of phishing URL detection. Machine learning models, particularly those based on deep learning architectures like BERT, have shown great promise in identifying malicious URLs by analyzing their textual features.

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BERT Natural Language Processing Deep Learning Machine Learning 105

Marktechpost

JANUARY 15, 2025

This gap has led to the evolution of deep learning models, designed to learn directly from raw data. What is Deep Learning? Deep learning, a subset of machine learning, is inspired by the structure and functioning of the human brain. High Accuracy: Delivers superior performance in many tasks.

Deep Learning 87

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 87

Analytics Vidhya

MARCH 13, 2023

Introduction Large Language Models (LLMs) are foundational machine learning models that use deep learning algorithms to process and understand natural language. These models are trained on massive amounts of text data to learn patterns and entity relationships in the language.

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Large Language Models Deep Learning Chatbots Machine Learning 343

Analytics Vidhya

APRIL 16, 2024

Introduction Welcome into the world of Transformers, the deep learning model that has transformed Natural Language Processing (NLP) since its debut in 2017. These linguistic marvels, armed with self-attention mechanisms, revolutionize how machines understand language, from translating texts to analyzing sentiments.

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Marktechpost

AUGUST 7, 2023

Models like GPT, BERT, and PaLM are getting popular for all the good reasons. The well-known model BERT, which stands for Bidirectional Encoder Representations from Transformers, has a number of amazing applications. Recent research investigates the potential of BERT for text summarization.

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BERT NLP Natural Language Processing Large Language Models 98

Marktechpost

NOVEMBER 25, 2023

The practical success of deep learning in processing and modeling large amounts of high-dimensional and multi-modal data has grown exponentially in recent years. They believe the proposed computational paradigm shows tremendous promise in connecting deep learning theory and practice from a unified viewpoint of data compression.

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Deep Learning Auto-classification Auto-complete BERT 128

Marktechpost

JANUARY 20, 2025

Rather than learning to generate new data, these models aim to make accurate predictions. Notably, BERT (Bidirectional Encoder Representations from Transformers), introduced by Devlin et al. Image Source Defining Predictive AI Predictive AI is primarily concerned with forecasting or inferring outcomes based on historical data.

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Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

Machine Learning 201

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 201

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. Using this approach, for the first time, we were able to effectively train BERT using simple SGD without the need for adaptivity.

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Deep Learning Algorithm Neural Network ML 118

AWS Machine Learning Blog

NOVEMBER 29, 2023

In today’s rapidly evolving landscape of artificial intelligence, deep learning models have found themselves at the forefront of innovation, with applications spanning computer vision (CV), natural language processing (NLP), and recommendation systems. If not, refer to Using the SageMaker Python SDK before continuing.

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Deep Learning Conversational AI Natural Language Processing BERT 115

Marktechpost

NOVEMBER 25, 2024

Deep learning models have emerged as transformative tools by leveraging RNA sequence data. Recent deep learning-based methods integrate multiple sequence alignments (MSAs) and secondary structure constraints to enhance RNA 3D structure prediction. million sequences.

Deep Learning 84

Deep Learning BERT Artificial Intelligence Artificial Intelligence 84

Unite.AI

JULY 23, 2024

At its core, DeWave utilizes deep learning models trained on extensive datasets of brain activity. The encoder, a BERT (Bidirectional Encoder Representations from Transformers) model, transforms EEG waves into unique codes. DeWave decodes their brainwaves into written words as users silently read text passages.

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Convolutional Neural Networks Neural Network Artificial Intelligence Artificial Intelligence 313

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 246

Neural Network Convolutional Neural Networks Robotics Natural Language Processing 246

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.

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Deep Learning Artificial Intelligence Artificial Intelligence AI 318

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” architecture).

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Deep Learning Convolutional Neural Networks NLP Neural Network 98

AssemblyAI

JUNE 27, 2023

Today, we can train deep learning algorithms that can automatically extract and represent information contained in audio signals, if trained with enough data. Traditional machine learning feature-based pipeline vs. end-to-end deep learning approach ( source ).

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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?

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Neural Network Convolutional Neural Networks BERT Deep Learning 158

AWS Machine Learning Blog

MAY 15, 2024

Optimized GEMM kernels ONNX Runtime supports the Microsoft Linear Algebra Subroutine (MLAS) backend as the default Execution Provider (EP) for deep learning operators. AWS Graviton3-based EC2 instances (c7g, m7g, r7g, c7gn, and Hpc7g instances) support bfloat16 format and MMLA instructions for the deep learning operator acceleration.

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NLP BERT Natural Language Processing Python 134

Unite.AI

SEPTEMBER 25, 2023

However, as technology advanced, so did the complexity and capabilities of AI music generators, paving the way for deep learning and Natural Language Processing (NLP) to play pivotal roles in this tech. Today platforms like Spotify are leveraging AI to fine-tune their users' listening experiences.

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Unite.AI

MAY 29, 2024

With nine times the speed of the Nvidia A100, these GPUs excel in handling deep learning workloads. Source: A pipeline on Generative AI This figure of a generative AI pipeline illustrates the applicability of models such as BERT, GPT, and OPT in data extraction.

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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

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BERT NLP Deep Learning Neural Network 52

Unite.AI

JULY 11, 2023

Introduction To Image Generation Image Source Course difficulty: Beginner-level Completion time: ~ 1 day (Complete the quiz/lab in your own time) Prerequisites: Knowledge of ML, Deep Learning (DL), Convolutional Neural Nets (CNNs), and Python programming. What will AI enthusiasts learn? Learn how it operates and its uses.

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Generative AI BERT Natural Language Processing Large Language Models 189

Flipboard

JUNE 20, 2023

We present the results of recent performance and power draw experiments conducted by AWS that quantify the energy efficiency benefits you can expect when migrating your deep learning workloads from other inference- and training-optimized accelerated Amazon Elastic Compute Cloud (Amazon EC2) instances to AWS Inferentia and AWS Trainium.

Machine Learning 123

Machine Learning BERT Deep Learning ML Engineer 123

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?

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BERT NLP Deep Learning Neural Network 52

Heartbeat

SEPTEMBER 11, 2023

The following is a brief tutorial on how BERT and Transformers work in NLP-based analysis using the Masked Language Model (MLM). Introduction In this tutorial, we will provide a little background on the BERT model and how it works. The BERT model was pre-trained using text from Wikipedia. What is BERT? How Does BERT Work?

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BERT NLP Deep Learning Machine Learning 52

AWS Machine Learning Blog

MARCH 2, 2023

Transformer-based language models such as BERT ( Bidirectional Transformers for Language Understanding ) have the ability to capture words or sentences within a bigger context of data, and allow for the classification of the news sentiment given the current state of the world. The code can be found on the GitHub repo.

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BERT Deep Learning Metadata Auto-complete 98

Unite.AI

NOVEMBER 25, 2024

BERT, GPT, or T5) based on the task. Example : In AI, a Factory pattern might dynamically generate a deep learning model based on the task type and hardware constraints, whereas in traditional systems, it might simply generate a user interface component. tabular vs. unstructured text).

Python 144

Python LLM AI Engineer AI 144

Marktechpost

MAY 3, 2024

Traditional NLP methods like CNN, RNN, and LSTM have evolved with transformer architecture and large language models (LLMs) like GPT and BERT families, providing significant advancements in the field. Sparse retrieval employs simpler techniques like TF-IDF and BM25, while dense retrieval leverages deep learning to improve accuracy.

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Natural Language Processing Large Language Models Categorization BERT 138

AWS Machine Learning Blog

AUGUST 2, 2024

of nodes with text-features MAG 484,511,504 7,520,311,838 4/4 28,679,392 1,313,781,772 240,955,156 We benchmark two main LM-GNN methods in GraphStorm: pre-trained BERT+GNN, a baseline method that is widely adopted, and fine-tuned BERT+GNN, introduced by GraphStorm developers in 2022. Dataset Num. of nodes Num. of edges Num.

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