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.

Data Extraction 273

Data Extraction Neural Network Large Language Models NLP 273

Mlearning.ai

APRIL 8, 2023

Deep Learning (Late 2000s — early 2010s) With the evolution of needing to solve more complex and non-linear tasks, The human understanding of how to model for machine learning evolved. 2017) “ BERT: Pre-training of deep bidirectional transformers for language understanding ” by Devlin et al.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

AWS Machine Learning Blog

JUNE 6, 2023

This post further walks through a step-by-step implementation of fine-tuning a RoBERTa (Robustly Optimized BERT Pretraining Approach) model for sentiment analysis using AWS Deep Learning AMIs (AWS DLAMI) and AWS Deep Learning Containers (DLCs) on Amazon Elastic Compute Cloud (Amazon EC2 p4d.24xlarge) with up to 3.5

ML 91

ML Deep Learning BERT Python 91

Mlearning.ai

JUNE 14, 2023

Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1) YouTube BERT Research — Ep.

Neural Network 52

Neural Network BERT Large Language Models Natural Language Processing 52

Lexalytics

APRIL 5, 2021

They were not wrong: the results they found about the limitations of perceptrons still apply even to the more sophisticated deep-learning networks of today. And indeed we can see other machine learning topics arising to take their place, like “optimization” in the mid-’00s, with “deep learning” springing out of nowhere in 2012.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

in 2012 is now widely referred to as ML’s “Cambrian Explosion.” Together, these elements lead to the start of a period of dramatic progress in ML, with NN being redubbed deep learning. FP16 is used in deep learning where computational speed is valued, and the lower precision won’t drastically affect the model’s performance.

ML 113

ML Deep Learning Algorithm Large Language Models 113

Bugra Akyildiz

AUGUST 10, 2024

Articles Quantization in deep learning refers to the process of reducing the precision of the numbers used to represent the model's parameters and activations. Typically, deep learning models use 32-bit floating-point numbers (float32) for computations. Per-Group : Applies quantization to groups of channels or neurons.

BERT 59

BERT Large Language Models Categorization Deep Learning 59