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

Unite.AI

JULY 23, 2024

Once the brain signals are collected, AI algorithms process the data to identify patterns. These algorithms map the detected patterns to specific thoughts, visual perceptions, or actions. For instance, in visual reconstructions, the AI system learns to associate brain wave patterns with images a person is viewing.

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Convolutional Neural Networks Neural Network Artificial Intelligence Artificial Intelligence 317

Unite.AI

SEPTEMBER 25, 2023

Initially, the attempts were simple and intuitive, with basic algorithms creating monotonous tunes. 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.

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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 321

Unite.AI

NOVEMBER 25, 2024

BERT, GPT, or T5) based on the task. Strategy Pattern The Strategy Pattern defines a family of interchangeable algorithms, encapsulating each one and allowing the behavior to change dynamically at runtime. Switching algorithms dynamically (Strategy pattern) to balance latency and accuracy based on system load or real-time constraints.

Python 147

Python LLM AI Engineer AI 147

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.

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LLM Machine Learning Explainability Algorithm 264

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. trillion parameters, making it one of the largest language models ever created.

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Generative AI Chatbots Large Language Models OpenAI 217

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

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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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. 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.

Neural Network 147

Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 147

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

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

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

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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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

Towards AI

DECEMBER 28, 2023

Black box algorithms such as xgboost emerged as the preferred solution for a majority of classification and regression problems. The advent of more powerful personal computers paved the way for the gradual acceptance of deep learning-based methods. CS6910/CS7015: Deep Learning Mitesh M.

Large Language Models 122

Large Language Models Natural Language Processing Deep Learning Prompt Engineering 122

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 Data Science Deep Learning BERT 111

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. Traditional Computing Systems : From basic computing algorithms, the journey began.

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Auto-complete ChatGPT Large Language Models Neural Network 240

Unite.AI

MAY 8, 2024

Graph Neural Networks (GNNs) have emerged as a powerful deep learning framework for graph machine learning tasks. GLEM goes further by proposing a variational EM algorithm that alternates between updating the LLM and GNN components for mutual enhancement.

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Neural Network Large Language Models LLM BERT 182

Unite.AI

JULY 25, 2024

A significant breakthrough came with neural networks and deep learning. Transformer-based models such as BERT and GPT-3 further advanced the field, allowing AI to understand and generate human-like text across languages. IBM's Model 1 and Model 2 laid the groundwork for advanced systems.

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Neural Network AI AI Machine Learning 202

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. 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.

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Generative AI Data Scientist Machine Learning BERT 176

IBM Journey to AI blog

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” Generative AI uses advanced machine learning algorithms and techniques to analyze patterns and build statistical models.

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Generative AI AI AI Chatbots 343

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. 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.

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Large Language Models Neural Network LLM ChatGPT 363

AWS Machine Learning Blog

MARCH 20, 2023

In the following example figure, we show INT8 inference performance in C6i for a BERT-base model. The BERT-base was fine-tuned with SQuAD v1.1, In the context of deep learning, the predominant numerical format used for research and deployment has so far been 32-bit floating point, or FP32.

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BERT Deep Learning ML Neural Network 98

Mlearning.ai

FEBRUARY 15, 2023

binary_classifier_interlocutor.ipynb” file stores our binary classifier which uses ensemble learning to classify if a text was uttered by the therapist or the client while “binary_classifier_quality.ipynb” determines if the overall conversation between a therapist and client is of high quality or low quality.

BERT 52

BERT NLP Natural Language Processing Algorithm 52

Marktechpost

NOVEMBER 10, 2024

BERT (Bidirectional Encoder Representations from Transformers) Google created the deep learning model known as BERT (Bidirectional Encoder Representations from Transformers) for natural language processing (NLP).

Python 87

Python Natural Language Processing BERT NLP 87

Marktechpost

JULY 5, 2024

The related works in this paper discuss a method called Scaling Laws for deep learning, which have become popular in recent years. Pre-trained embeddings like frozen ResNet-50 and BERT, are used to speed up training and prevent underfitting for CIFAR-10 and IMDB, respectively.

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Machine Learning BERT Deep Learning Explainability 130

The MLOps Blog

JANUARY 25, 2023

Then using Machine Learning and Deep Learning sentiment analysis techniques, these businesses analyze if a customer feels positive or negative about their product so that they can make appropriate business decisions to improve their business. are used to classify the text sentiment.

BERT 52

BERT Natural Language Processing ML Deep Learning 52

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

SEPTEMBER 17, 2024

Large-scale deep learning models, especially transformer-based architectures, have grown exponentially in size and complexity, reaching billions to trillions of parameters. Recent studies have reviewed language models, optimization techniques, and acceleration methods for large-scale deep-learning models and LLMs.

Large Language Models 109

Large Language Models LLM NLP Deep Learning 109

Towards AI

JULY 21, 2023

To further comment on Fury, for those looking to intern in the short term, we have a position available to work in an NLP deep learning project in the healthcare domain. AND multilingual models) GitHub: UKPLab/sentence-transformers BERT / RoBERTa / XLM-RoBERTa produces out-of-the-box rather bad sentence embeddings.

NLP 98

NLP BERT Deep Learning Natural Language Processing 98

John Snow Labs

JUNE 6, 2023

Text classification with transformers refers to the application of deep learning models based on the transformer architecture to classify sequences of text into predefined categories or labels. BERT (Bidirectional Encoder Representations from Transformers) is a language model that was introduced by Google in 2018.

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. Key features of Shap-E: a) Implicit representation : Shap-E learns to generate implicit representations (signed distance functions) of 3D objects. Here's a simplified example of how this might look in code: class TextTo3D(nn.Module): def __init__(self): super().__init__()

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OpenAI BERT Neural Network AI 147

Marktechpost

SEPTEMBER 30, 2024

While some progress has been made in enhancing retrieval mechanisms through latent semantic analysis (LSA) and deep learning models, these methods still need to address the semantic gaps between queries and documents. This restricts their adaptability to real-world scenarios where data is constantly changing.

BERT 109

BERT Algorithm Deep Learning Data Integration 109

NVIDIA

FEBRUARY 11, 2025

He calls the blending of AI algorithms and model architectures homogenization , a trend that helped form foundation models. That work inspired researchers who created BERT and other large language models , making 2018 a watershed moment for natural language processing, a report on AI said at the end of that year. See chart below.)

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Neural Network Large Language Models Robotics BERT 55

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

Marktechpost

JULY 4, 2023

” Shaped AI also provides ranking algorithms for feeds, recommendations, and discovery sites. Top Open Source Large Language Models GPT-Neo, GPT-J, and GPT-NeoX Extremely potent artificial intelligence models, such as GPT-Neo, GPT-J, and GPT-NeoX, can be used to Few-shot learning issues.

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Large Language Models BERT Natural Language Processing Categorization 104