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?

Neural Network 157

Neural Network Convolutional Neural Networks BERT Deep Learning 157

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.

BERT 114

BERT Automation Neural Network Machine Learning 114

Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

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. At its core, Deepnote AI aims to augment the workflow of data scientists.

Auto-complete 240

Auto-complete ChatGPT Large Language Models Neural Network 240

ODSC - Open Data Science

MARCH 10, 2025

Tools like Python , R , and SQL were mainstays, with sessions centered around data wrangling, business intelligence, and the growing role of data scientists in decision-making. By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow.

Data Science 52

Data Science Neural Network Convolutional Neural Networks Large Language Models 52

Mlearning.ai

APRIL 21, 2023

Natural Language Processing Transformers, the neural network architecture, that has taken the world of natural language processing (NLP) by storm, is a class of models that can be used for both language and image processing. We will also discuss the pros and cons of Transformers, along with practical examples in Python.

Neural Network 52

Neural Network Natural Language Processing ChatGPT NLP 52

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. Versatile Toolset Exposure : Including Python, Java, TensorFlow, and Keras.

Prompt Engineer 267

Prompt Engineer Prompt Engineering Large Language Models ChatGPT 267

Unite.AI

APRIL 15, 2024

The reranking model, often a neural network or a transformer-based architecture, is specifically trained to assess the relevance of a document to a given query. ColBERT: Efficient and Effective Late Interaction One of the standout models in the realm of reranking is ColBERT ( Contextualized Late Interaction over BERT ).

BERT 157

BERT Large Language Models Natural Language Processing NLP 157

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.

NLP 97

NLP Neural Network BERT Natural Language Processing 97

John Snow Labs

JUNE 6, 2023

Transformers are defined as a specific type of neural network architecture that have proven to be particularly effective for sequence classification tasks, thanks to their ability to capture long-term dependencies and contextual relationships in the data. The transformer architecture was introduced by Vaswani et al.

BERT 52

BERT Python NLP Neural Network 52

ODSC - Open Data Science

FEBRUARY 17, 2023

TensorFlow is desired for its flexibility for ML and neural networks, PyTorch for its ease of use and innate design for NLP, and scikit-learn for classification and clustering. BERT is still very popular over the past few years and even though the last update from Google was in late 2019 it is still widely deployed.

NLP 111

NLP Data Science Deep Learning BERT 111

Unite.AI

JUNE 21, 2024

Models such as GPT, BERT , and more recently Llama , Mistral are capable of understanding and generating human-like text with unprecedented fluency and coherence. Setup Python Virtual Environment Ubuntu 22.04 comes with Python 3.10. lib64 BNB_CUDA_VERSION=122 CUDA_VERSION=122 python setup.py import torch import torch.nn

Deep Learning 130

Deep Learning Neural Network Convolutional Neural Networks Large Language Models 130

Explosion

AUGUST 1, 2019

Huge transformer models like BERT, GPT-2 and XLNet have set a new standard for accuracy on almost every NLP leaderboard. Deep neural networks have offered a solution, by building dense representations that transfer well between tasks. In this post we introduce our new wrapping library, spacy-transformers.

BERT 52

BERT NLP Neural Network Categorization 52

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!

Neural Network 52

Neural Network BERT Large Language Models Natural Language Processing 52

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.

OpenAI 144

OpenAI BERT Neural Network AI 144

AWS Machine Learning Blog

JUNE 18, 2024

Traditional neural network models like RNNs and LSTMs and more modern transformer-based models like BERT for NER require costly fine-tuning on labeled data for every custom entity type. We extract the default generic entities through the AWS SDK for Python (Boto3) as follows: import pandas as pd comprehend_client = boto3.client("comprehend")

Large Language Models 117

Large Language Models Natural Language Processing LLM Computer Vision 117

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, Use the supplied Python scripts for quantization. Run the provided Python test scripts to invoke the SageMaker endpoint for both INT8 and FP32 versions. 2xLarge-FP32 70 110.8

BERT 88

BERT Deep Learning ML Neural Network 88

Unite.AI

SEPTEMBER 13, 2024

How It Works TensorRT-LLM speeds up inference by optimizing neural networks during deployment using techniques like: Quantization : Reduces the precision of weights and activations, shrinking model size and improving inference speed. Weight Bindings Before compiling the model, the weights (or parameters) must be bound to the network.

Large Language Models 130

Large Language Models LLM Natural Language Processing Auto-complete 130

Unite.AI

JANUARY 23, 2024

Techniques like Word2Vec and BERT create embedding models which can be reused. Word2Vec pioneered the use of shallow neural networks to learn embeddings by predicting neighboring words. BERT produces deep contextual embeddings by masking words and predicting them based on bidirectional context.

Large Language Models 100

Large Language Models Prompt Engineering Prompt Engineer LLM 100

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.

NLP 98

NLP BERT Large Language Models Natural Language Processing 98

AWS Machine Learning Blog

APRIL 25, 2024

It uses BERT, a popular NLP technique, to understand the meaning and context of words in the candidate summary and reference summary. The more similar the words and meanings captured by BERT, the higher the BERTScore. It uses neural networks like BERT to measure semantic similarity beyond just exact word or phrase matching.

BERT 125

BERT NLP Algorithm Neural Network 125

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. Editor’s note: Benjamin Batorsky, PhD is a speaker for ODSC East 2023. deep” architecture). Follow along in the notebook !

Deep Learning 98

Deep Learning Convolutional Neural Networks NLP Neural Network 98

Viso.ai

JULY 9, 2024

In this blog, we’ll explore the concept of transfer learning, how it technically works, and provide a step-by-step guide to implementing it in Python. 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.

Neural Network 52

Neural Network BERT Deep Learning Computer Vision 52

ODSC - Open Data Science

APRIL 11, 2023

This workshop will introduce you to the fundamentals of PySpark (Spark’s Python API), the Spark NLP library, and other best practices in Spark programming when working with textual or natural language data. In recent years, text data is increasingly becoming more common as new techniques to work with them become popular.

NLP 96

NLP Data Science Large Language Models Data Scientist 96

Bugra Akyildiz

DECEMBER 18, 2022

If you have been spending a lot of time with ChatGPT, you my want to take a look at the awesome prompts: [link] You can open full-fledged terminal, write Python, JS in various terminals and then debug your code further. There is a good notebook that you can take a look at the approach in Python. SELECT date.fromtimestamp(.purchase_ts)

Neural Network 52

Neural Network ChatGPT Machine Learning Deep Learning 52

Viso.ai

DECEMBER 18, 2023

TensorFlow is an open-source software library for AI and machine learning with deep neural networks. TensorFlow Lite also optimizes the trained model using quantization techniques (discussed later in this article), which consequently reduces the necessary memory usage as well as the computational cost of utilizing neural networks.

Computer Vision 98

Computer Vision Machine Learning Deep Learning Neural Network 98

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.

Large Language Models 52

Large Language Models BERT Neural Network LLM 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.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

ODSC - Open Data Science

JUNE 6, 2023

Using Embeddings to Detect Anomalies Figure 1: Using a trained deep neural network, it is possible to convert unstructured data to numeric representations, i.e., embeddings Embeddings are numerical representations generated from unstructured data like images, text, and audio, and greatly influence machine learning approaches for handling such data.

Machine Learning 90

Machine Learning ML Engineer Neural Network Data Science 90

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.

BERT 52

BERT NLP Machine Learning Deep Learning 52

Viso.ai

APRIL 26, 2024

Object detection systems typically use frameworks like Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs). Concept of Convolutional Neural Networks (CNN) However, in prompt object detection systems, users dynamically direct the model with many tasks it may not have encountered before.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

AWS Machine Learning Blog

JANUARY 17, 2023

When running this notebook on Studio, you should make sure the Python 3 (PyTorch 1.10 BERT + Random Forest. BERT + Random Forest. BERT + Random Forest with HPO. Fuse multiple neural network models directly and handle raw text (which are also capable of handling additional numerical and categorical columns).

Categorization 79

Categorization BERT Machine Learning Neural Network 79

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 ).

Machine Learning 52

Machine Learning BERT Neural Network OpenAI 52

Dlabs.ai

MAY 22, 2022

Once complete, you’ll know all about machine learning, statistics, neural networks, and data mining. Machine Learning with Python: A Practical Introduction Author: Saeed Aghabozorgi Ph.D. According to GitHub , Python is the most popular programming language used in machine learning.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Neural Network 52

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.

Natural Language Processing 40

Natural Language Processing Neural Network Deep Learning BERT 40

AWS Machine Learning Blog

AUGUST 30, 2023

Recent advancements in ML (specifically the invention of the transformer-based neural network architecture) have led to the rise of models that contain billions of parameters or variables. medium 2vCPU+4GiB notebook instance with a Python 3 kernel. We use an ml.t3.medium pip install nest-asyncio==1.5.5 --quiet !pip

Generative AI 128

Generative AI LLM Python ML 128

Towards AI

JULY 19, 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.

NLP 40

NLP Neural Network BERT Natural Language Processing 40