AWS Machine Learning Blog

JANUARY 19, 2024

In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model performance and reduce inference times. First, we use an Amazon SageMaker Studio notebook to fine-tune a pre-trained BERT model on a target task using a domain-specific dataset.

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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. Understanding Neural Networks At their core, neural networks are computational models inspired by the biological neural networks that constitute animal brains.

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Marktechpost

DECEMBER 12, 2024

A Deep Neural Network (DNN) is an artificial neural network that features multiple layers of interconnected nodes, also known as neurons. Each neuron processes input data by applying weights, biases, and an activation function to generate an output. pixel values of an image, numerical data).

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ODSC - Open Data Science

FEBRUARY 17, 2023

The chart below shows 20 in-demand skills that encompass both NLP fundamentals and broader data science expertise. In a change from last year, there’s also a higher demand for those with data analysis skills as well. Having mastery of these two will prove that you know data science and in turn, NLP.

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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.

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IBM Journey to AI blog

AUGUST 1, 2023

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. A foundation model is built on a neural network model architecture to process information much like the human brain does.

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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.

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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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Towards AI

JULY 21, 2023

Blog: Faster and smaller quantized NLP with Hugging Face and ONNX Runtime Popular Hugging Face Transformer models (BERT, GPT-2, etc) can be shrunk and accelerated with ONNX Runtime quantization… medium.com Talking about ONNX… Suraj Patil created this awesome repo that incorporates the ONNX script with the Hugging Face pipeline framework.

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Pickl AI

SEPTEMBER 23, 2024

Summary: The blog explores the synergy between Artificial Intelligence (AI) and Data Science, highlighting their complementary roles in Data Analysis and intelligent decision-making. Introduction Artificial Intelligence (AI) and Data Science are revolutionising how we analyse data, make decisions, and solve complex problems.

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ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e. Since 2014, he has been working in data science for government, academia, and the private sector. deep” architecture).

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AWS Machine Learning Blog

FEBRUARY 6, 2024

The other data challenge for healthcare customers are HIPAA compliance requirements. Transformers, BERT, and GPT The transformer architecture is a neural network architecture that is used for natural language processing (NLP) tasks. It was first introduced in the paper “Attention Is All You Need” by Vaswani et al.

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Large Language Models BERT NLP Data Scientist 113

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. ChatGPT for Productivity showcases the use of ChatGPT in various fields such as data science, marketing, and project management.

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ODSC - Open Data Science

APRIL 11, 2023

From Big Data to NLP insights: Getting started with PySpark and Spark NLP The amount of data being generated today is staggering and growing. Apache Spark has emerged as the de facto tool to analyze big data over the last few years and is now a critical part of the data science toolbox.

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ODSC - Open Data Science

JUNE 6, 2023

This is due to a deep disconnect between data engineering and data science practices. Historically, our space has perceived streaming as a complex technology reserved for experienced data engineers with a deep understanding of incremental event processing.

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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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Towards AI

JULY 20, 2023

This ongoing process straddles the intersection between evidence-based medicine, data science, and artificial intelligence (AI). 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. a text file with one word per line).

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Towards AI

JULY 13, 2024

Now, imagine a large, pretrained model tailored for time-series data — one that delivers accurate predictions without extensive retraining. Their decoder-only model, inspired by NLP giants like BERT, uses a patch-based approach to handle data efficiently.

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ODSC - Open Data Science

SEPTEMBER 18, 2023

Neural Network-Based Embeddings The fourth part delves into neural network-based embeddings, including word2vec and GloVe, that provide dense and compact representations of words. Large Language Models Finally, the course concludes with a look at large language models, such as BERT, ELMo, GPT, and ULMFiT.

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ODSC - Open Data Science

NOVEMBER 19, 2024

ODSC West 2024 showcased a wide range of talks and workshops from leading data science, AI, and machine learning experts. This blog highlights some of the most impactful AI slides from the world’s best data science instructors, focusing on cutting-edge advancements in AI, data modeling, and deployment strategies.

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Heartbeat

FEBRUARY 7, 2023

Formula to calculate n-gram probabilities; image by Peter Martigny on Feedly Neural Language Models Neural language models make use of artificial neural networks to discover the patterns and structures in a language. One of the most popular language models is the Recurrent Neural Network Language Model (RNNLM).

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Dlabs.ai

MAY 22, 2022

Machine Learning for Data Science and Analytics Authors: Ansaf Salleb-Aouissi, Cliff Stein, David Blei, Itsik Peer Associate, Mihalis Yannakakis, Peter Orbanz If you ever dreamt of attending classes at Columbia University but never had the chance, this artificial intelligence course focused on ML is the next best thing.

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Marktechpost

JULY 3, 2023

The underlying architecture of LLMs typically involves a deep neural network with multiple layers. Based on the discovered patterns and connections found in the training data, this network analyses the input text and produces predictions.

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AWS Machine Learning Blog

APRIL 3, 2024

The following is a high-level overview of how it works conceptually: Separate encoders – These models have separate encoders for each modality—a text encoder for text (for example, BERT or RoBERTa), image encoder for images (for example, CNN for images), and audio encoders for audio (for example, models like Wav2Vec).

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Lexalytics

APRIL 5, 2021

This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas. (I Around this time a new graduate student, Geoffrey Hinton, decided that he would study the now discredited field of neural networks.

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Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

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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Large Language Models BERT Neural Network LLM 52

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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Applied Data Science

JANUARY 4, 2022

Transformers taking the AI world by storm The family of artificial neural networks (ANNs) saw a new member being born in 2017, the Transformer. Initially introduced for Natural Language Processing (NLP) applications like translation, this type of network was used in both Google’s BERT and OpenAI’s GPT-2 and GPT-3.

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NVIDIA Developer

JUNE 2, 2021

Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized NLP by offering accuracy comparable to human baselines on benchmarks like SQuAD for question-answer, entity recognition, intent recognition, sentiment analysis, and more. Basic understanding of neural networks.

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Snorkel AI

FEBRUARY 13, 2024

While rarely an endpoint, large language model (LLM) distillation lets data science teams kickstart the data development process and get to a production-ready model faster than they could with traditional approaches. The student model could be a simple model like logistic regression or a foundation model like BERT.

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Snorkel AI

FEBRUARY 13, 2024

While rarely an endpoint, large language model (LLM) distillation lets data science teams kickstart the data development process and get to a production-ready model faster than they could with traditional approaches. The student model could be a simple model like logistic regression or a foundation model like BERT.

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AWS Machine Learning Blog

JUNE 9, 2023

By using Graph Neural Networks (GNNs), GuardDuty is able to enhance its capability to alert customers. GraphStorm enables our team to train GNN embedding in a self-supervised manner on a graph with 288 million nodes and 2 billion edges,” Says Haining Yu, Principal Applied Scientist at Amazon Measurement, Ad Tech, and Data Science.

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Heartbeat

OCTOBER 10, 2023

RoBERTa RoBERTa (Robustly Optimized BERT Approach) is a natural language processing (NLP) model based on the BERT (Bidirectional Encoder Representations from Transformers) architecture. This refers to the fact that BERT was pre-trained on one set of tasks but fine-tuned on a different set of tasks for downstream NLP applications.

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Mlearning.ai

MAY 25, 2023

Sequence models have gained traction in the past 5 years and it’s been very active research of study, even the GPT models — on which ChatGPT was implemented using the concept of Transformers and BERT , which is based on Self-Attention model — it is based on Attention models — which uses the base of Encoder-Decoder sequence models.

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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. & Nie, JY.

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Applied Data Science

DECEMBER 23, 2022

Dall-e , and pre-2022 tools in general, attributed their success either to the use of the Transformer or Generative Adversarial Networks. The former is a powerful architecture for artificial neural networks that was originally introduced for language tasks (you’ve probably heard of GPT-3 ?) Who should I follow? What happened?

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