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.

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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.

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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.

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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.

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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.

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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. What are Small Language Models? million parameters to Medium with 41 million.

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Mlearning.ai

JUNE 14, 2023

YouTube Introduction to Sequence Learning and Attention Mechanisms Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 — Translation, Seq2Seq, Attention — YouTube Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 7 — Translation, Seq2Seq, Attention — YouTube 2.

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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.

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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.

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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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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. They employ a technique known as unsupervised learning, where they extract knowledge from unlabelled text data, making them incredibly versatile and adaptable to various NLP tasks.

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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. Learn more about Viso Suite by booking a demo with us. Foundation models are recent developments in artificial intelligence (AI). are at the forefront of the AI revolution.

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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.

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ODSC - Open Data Science

SEPTEMBER 18, 2023

A lot goes into learning a new skill, regardless of how in-depth it is. Getting started with natural language processing (NLP) is no exception, as you need to be savvy in machine learning, deep learning, language, and more.

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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.

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Heartbeat

FEBRUARY 7, 2023

image by rawpixel.com Understanding the concept of language models in natural language processing (NLP) is very important to anyone working in the Deep learning and machine learning space. The neural network receives the embeddings and uses them to process and forecast the likelihood of the next word in the sequence.

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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.

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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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Unite.AI

JANUARY 23, 2024

LLMs are a class of deep learning models that are pretrained on massive text corpora, allowing them to generate human-like text and understand natural language at an unprecedented level. Techniques like Word2Vec and BERT create embedding models which can be reused. What are Large Language Models and Why are They Important?

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AWS Machine Learning Blog

FEBRUARY 1, 2023

Recent developments in deep learning have led to increasingly large models such as GPT-3, BLOOM, and OPT, some of which are already in excess of 100 billion parameters. Instance Size Trainium Accelerators Accelerator Memory (GB) vCPUs Instance Memory (GiB) Network Bandwidth (Gbps) trn1.2xlarge 1 32 8 32 Up to 12.5

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Bugra Akyildiz

DECEMBER 18, 2022

Brown researchers compared human visual recognition systems and Deep Neural Networks for what they believe where the important features of images are. Libraries EasyRec implements state of the art deep learning models used in common recommendation tasks: candidate generation(matching), scoring(ranking), and multi-task learning.

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NVIDIA Developer

JUNE 2, 2021

Deep learning models have gained widespread popularity for NLP because of their ability to accurately generalize over a range of contexts and languages. Fundamental understanding of a deep learning framework such as TensorFlow, PyTorch, or Keras. Basic understanding of neural networks.

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Marktechpost

JULY 4, 2023

Unigrams, N-grams, exponential, and neural networks are valid forms for the Language Model. 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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Heartbeat

JUNE 28, 2023

In this article, we will explore about ALBERT ( A lite weighted version of BERT machine learning model) What is ALBERT? ALBERT (A Lite BERT) is a language model developed by Google Research in 2019. BERT, GPT-2, and XLNet are some examples of models that can be used as teacher models for ALBERT.

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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. It addresses how input prompts function within language models like ChatGPT.

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Marktechpost

JULY 3, 2023

From deep learning, Natural Language Processing (NLP), and Natural Language Understanding (NLU) to Computer Vision, AI is propelling everyone into a future with endless innovations. These deep learning-based models demonstrate impressive accuracy and fluency while processing and comprehending natural language.

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Heartbeat

JUNE 6, 2023

Photo by GuerrillaBuzz on Unsplash Graph Convolutional Networks (GCNs) are a type of neural network that operates on graphs, which are mathematical structures consisting of nodes and edges. GCNs have been successfully applied to many domains, including computer vision and social network analysis. Richong, Z.,

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Mlearning.ai

MARCH 30, 2023

Today in this blog we will learn about transfer learning and how can you implement it using TensorFlow. To train a machine learning model or a neural network that can yield the best results requires what? The name transfer learning comes from the process of using the learning from one problem to solve another.

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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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Mlearning.ai

JUNE 22, 2023

Understanding the biggest neural network in Deep Learning Join 34K+ People and get the most important ideas in AI and Machine Learning delivered to your inbox for free here Deep learning with transformers has revolutionized the field of machine learning, offering various models with distinct features and capabilities.

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TensorFlow

MARCH 29, 2023

This plug-in enables neural network inferencing on AMD EPYC CPUs with the AMD ZenDNN library. ZenDNN ZenDNN, which is available open-source from GitHub , is a low-level AMD deep neural network library that includes basic neural network building blocks optimized for AMD EPYC CPUs. ZD-045 thru ZD-051

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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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Bugra Akyildiz

NOVEMBER 24, 2022

In the model that is backed by Bert, they train two heads tasks; disfluency head and wait head. In this work, they leverage the same scaling approach behind the success of deep learning to learn versatile optimizers. It is also important to be able to detect and remove these in order to handle speech recognition well.

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Towards AI

JULY 20, 2023

BioBERT and similar BERT-based NER models are trained and fine-tuned using a biomedical corpus (or dataset) such as NCBI Disease, BC5CDR, or Species-800. New research has also begun looking at deep learning algorithms for automatic systematic reviews, According to van Dinter et al. a text file with one word per line).

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Heartbeat

AUGUST 2, 2023

Introduction to LLMs LLM in the sphere of AI Large language models (often abbreviated as LLMs) refer to a type of artificial intelligence (AI) model typically based on deep learning architectures known as transformers. Large language models, such as GPT-3 (Generative Pre-trained Transformer 3), BERT, XLNet, and Transformer-XL, etc.,

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Heartbeat

JULY 19, 2023

Hugging F ace Transformers Recently, the field of natural language processing has paid a lot of attention to a type of machine learning architecture called transformers. They are neural networks made to analyse data sequences like sentences or paragraphs and anticipate the following word or sentence.

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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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