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. One such model that has garnered considerable attention is OpenAI's ChatGPT , a shining exemplar in the realm of Large Language Models. Imagine you have an image of a cat.

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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. This stage takes approximately 20 seconds.

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Unite.AI

DECEMBER 4, 2023

GPT 3 and similar Large Language Models (LLM) , such as BERT , famous for its bidirectional context understanding, T-5 with its text-to-text approach, and XLNet , which combines autoregressive and autoencoding models, have all played pivotal roles in transforming the Natural Language Processing (NLP) paradigm.

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Unite.AI

AUGUST 28, 2023

Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience. Systems like ChatGPT by OpenAI, BERT, and T5 have enabled breakthroughs in human-AI communication. Below is a demonstration of the BabyAGI using this link.

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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.

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Unite.AI

DECEMBER 27, 2023

These tools, such as OpenAI's DALL-E , Google's Bard chatbot , and Microsoft's Azure OpenAI Service , empower users to generate content that resembles existing data. OpenAI's GPT-4 stands as a state-of-the-art generative language model, boasting an impressive over 1.7

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Unite.AI

OCTOBER 22, 2024

AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands. Models like GPT and BERT involve millions to billions of parameters, leading to significant processing time and energy consumption during training and inference.

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Marktechpost

APRIL 25, 2024

The development of LLMs, such as OpenAI’s GPT series, Google’s Gemini, Anthropic AI’s Claude, and Meta’s Llama models, marks a significant advancement in natural language processing. Parameters (like weights in neural networks) are adjusted during training to reduce the difference between predicted and actual output.

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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. These versions offer flexibility in terms of applications, ranging from Mini with 4.4

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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 Architecture III.2

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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. Expert Creators : Developed by renowned professionals from OpenAI and DeepLearning.AI.

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Marktechpost

APRIL 6, 2024

Transformers have transformed the field of NLP over the last few years, with LLMs like OpenAI’s GPT series, BERT, and Claude Series, etc. in 2017, marking a departure from the previous reliance on recurrent neural networks (RNNs) and convolutional neural networks (CNNs) for processing sequential data.

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Unite.AI

DECEMBER 9, 2024

Early neural networks like AlexNet and ResNet demonstrated how increasing model size could improve image recognition. Then came transformers where models like GPT-3 and Googles BERT have showed that scaling could unlock entirely new capabilities, such as few-shot learning. This recipe has driven AIs evolution for over a decade.

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Marktechpost

MAY 19, 2024

Current methods in the field include keyword-based search engines and advanced neural network models like BERT and GPT. employs a variety of models, including Ollama’s Llama3, HuggingFace’s MiniLMEmbedder, Cohere’s Command R+, Google’s Gemini, and OpenAI’s GPT-4. Check out the release video!

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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.

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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).

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AWS Machine Learning Blog

FEBRUARY 6, 2024

Transformers, BERT, and GPT The transformer architecture is a neural network architecture that is used for natural language processing (NLP) tasks. BERT is trained on sequences where some of the words in a sentence are masked, and it has to fill in those words taking into account both the words before and after the masked words.

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AssemblyAI

SEPTEMBER 18, 2023

They use neural networks that are inspired by the structure and function of the human brain. GPT-4 GPT-4 is OpenAI's latest (and largest) model. BERT BERT stands for Bidirectional Encoder Representations from Transformers, and it's a large language model by Google. How Do Large Language Models Work?

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Topbots

APRIL 11, 2023

We’ll start with a seminal BERT model from 2018 and finish with this year’s latest breakthroughs like LLaMA by Meta AI and GPT-4 by OpenAI. 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.

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Marktechpost

JULY 4, 2023

Unigrams, N-grams, exponential, and neural networks are valid forms for the Language Model. Many fields have used fine-tuning, but OpenAI’s InstructGPT is a particularly impressive and up-to-date example. rely on Language Models as their foundation. It is pre-trained using a generalized autoregressive model.

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Mlearning.ai

JUNE 14, 2023

LLMs (Foundational Models) 101: Introduction to Transformer Models Transformers, explained: Understand the model behind GPT, BERT, and T5 — YouTube Illustrated Guide to Transformers Neural Network: A step by step explanation — YouTube Attention Mechanism Deep dive. Transformer Neural Networks — EXPLAINED!

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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. BERT (Bidirectional Encoder Representations from Transformers) — developed by Google. RoBERTa (Robustly Optimized BERT Approach) — developed by Facebook AI.

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John Snow Labs

JUNE 27, 2023

At their core, LLMs are built upon deep neural networks, enabling them to process vast amounts of text and learn complex patterns. In this section, we will provide an overview of two widely recognized LLMs, BERT and GPT, and introduce other notable models like T5, Pythia, Dolly, Bloom, Falcon, StarCoder, Orca, LLAMA, and Vicuna.

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Snorkel AI

MARCH 1, 2023

Model architectures that qualify as “supervised learning”—from traditional regression models to random forests to most neural networks—require labeled data for training. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

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Marktechpost

JULY 3, 2023

With the release of the latest chatbot developed by OpenAI called ChatGPT, the field of AI has taken over the world as ChatGPT, due to its GPT’s transformer architecture, is always in the headlines. The underlying architecture of LLMs typically involves a deep neural network with multiple layers.

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Mlearning.ai

JUNE 8, 2023

A few embeddings for different data type For text data, models such as Word2Vec , GLoVE , and BERT transform words, sentences, or paragraphs into vector embeddings. Images can be embedded using models such as convolutional neural networks (CNNs) , Examples of CNNs include VGG , and Inception. using its Spectrogram ).

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Mlearning.ai

JANUARY 17, 2024

Major milestones in the last few years comprised BERT (Google, 2018), GPT-3 (OpenAI, 2020), Dall-E (OpenAI, 2021), Stable Diffusion (Stability AI, LMU Munich, 2022), ChatGPT (OpenAI, 2022). Complex ML problems can only be solved in neural networks with many layers. Deep learning neural network.

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Viso.ai

JULY 9, 2024

As the name suggests, this technique involves transferring the learnings of one trained machine learning model to another, in the form of neural network weights. But, there are open source models like German-BERT that are already trained on huge data corpora, with many parameters. Book a demo to learn more.

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Snorkel AI

MAY 25, 2023

The potential of these enormous neural networks has both excited and frightened the public; the same technology that promises to help you digest long email chains also threatens to dethrone the essay as the default classroom assignment. All of this made it easy for researchers and practitioners to use BERT.

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Snorkel AI

MAY 25, 2023

The potential of these enormous neural networks has both excited and frightened the public; the same technology that promises to help you digest long email chains also threatens to dethrone the essay as the default classroom assignment. All of this made it easy for researchers and practitioners to use BERT.

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Viso.ai

JULY 18, 2024

Emergence and History of LLMs Artificial Neural Networks (ANNs) and Rule-based Models The foundation of these Computational Linguistics models (CL) dates back to the 1940s when Warren McCulloch and Walter Pitts laid the groundwork for AI. However, the first actual language model was a rule-based model developed in the 1950s.

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ODSC - Open Data Science

JUNE 9, 2023

The 1970s introduced bell bottoms, case grammars, semantic networks, and conceptual dependency theory. In the 90’s we got grunge, statistical models, recurrent neural networks and long short-term memory models (LSTM). It uses a neural network to learn the vector representations of words from a large corpus of text.

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Viso.ai

MARCH 20, 2024

Models like GPT 4, BERT, DALL-E 3, CLIP, Sora, etc., Use Cases for Foundation Models Applications in Pre-trained Language Models like GPT, BERT, Claude, etc. Foundation models are large-scale neural network architectures that undergo pre-training on vast amounts of unlabeled data through self-supervised learning.

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Bugra Akyildiz

DECEMBER 18, 2022

Before going further, a new announcement for embeddings also came from OpenAI: openai.com/blog/new-and-i… nnIt is cross-modal, and it is 1/500th of the price of the old embedding model DaVinci. ","username":"bugraa","name":"Bugra Dragon can be used as a drop-in replacement for BERT.

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Heartbeat

OCTOBER 12, 2023

These models, powered by massive neural networks, have catalyzed groundbreaking advancements in natural language processing (NLP) and have reshaped the landscape of machine learning. CLIP (Contrastive Language-Image Pre-training) : CLIP, developed by OpenAI, is a multi-modal model that can understand images and text.

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Viso.ai

JUNE 12, 2024

Large Language Models – Source In 2018, OpenAI researchers and engineers published an original work on AI-based generative large language models. GPT models are based on transformer-based deep learning neural network architecture. GPT-2 is not just a language model like BERT, it can also generate text.

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Jay Alammar

OCTOBER 3, 2022

In technical terms, this component is made up of a UNet neural network and a scheduling algorithm. With this we come to see the three main components (each with its own neural network) that make up Stable Diffusion: ClipText for text encoding. We’ll talk more about what that means later in the post. Input: text.

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