Marktechpost

MAY 5, 2024

Existing surveys detail a range of techniques utilized in Explainable AI analyses and their applications within NLP. While earlier surveys predominantly centred on encoder-based models such as BERT, the emergence of decoder-only Transformers spurred advancements in analyzing these potent generative models.

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Unite.AI

DECEMBER 1, 2023

SHAP's strength lies in its consistency and ability to provide a global perspective – it not only explains individual predictions but also gives insights into the model as a whole. 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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Mlearning.ai

JULY 23, 2023

I’m going to explain the detail of Text Classification using Naive Bayes algorithm (Naive Bayes Classifier). Nowdays, there is lot more advanced algorithms for text classification such as BERT, CNN, LSTM, etc. They use a type of computer model called a Neural Network, which is really good at learning from and making sense of data.

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Artificial Corner

JULY 30, 2023

In this world of complex terminologies, someone who wants to explain Large Language Models (LLMs) to some non-tech guy is a difficult task. So that’s why I tried in this article to explain LLM in simple or to say general language. BERT (Bidirectional Encoder Representations from Transformers) — developed by Google.

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Mlearning.ai

MARCH 30, 2023

To train a machine learning model or a neural network that can yield the best results requires what? How can we train a neural network without having an ample amount of data, even if you have it can you afford to train a model for months? Let me explain this in simple words. This is how transfer learning works.

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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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Marktechpost

JULY 4, 2023

Unigrams, N-grams, exponential, and neural networks are valid forms for the Language Model. ” Even for seasoned programmers, the syntax of shell commands might need to be explained. DeBERTa Microsoft Research proposed decoding-enhanced BERT with disentangled attention to augment BERT and RoBERTa models.

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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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Bugra Akyildiz

DECEMBER 18, 2022

Standard researchers wrote 3 posts that explain in detail what CL is and how it is being used: [link] [link] [link] Vespa wrote about how they are using pre-trained bidirectional encoders for search functionality. Dragon can be used as a drop-in replacement for BERT. See References for details and nuances of this correspondence.

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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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Dlabs.ai

MAY 22, 2022

The instructors are very good at explaining complex topics in an easy-to-understand way. Once complete, you’ll know all about machine learning, statistics, neural networks, and data mining. This course is a great starting point if you want to use it to build and apply scalable models to real-world problems.

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Snorkel AI

OCTOBER 4, 2023

These large-scale neural networks are trained on vast amounts of data to address a wide number of tasks (i.e. Data teams can fine-tune LLMs like BERT, GPT-3.5 Improve auditing, model explainability, and iteration. natural language processing, image classification, question answering).

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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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AWS Machine Learning Blog

JANUARY 17, 2023

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). BERT + Random Forest. BERT + Random Forest with HPO. BERT + Random Forest.

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Snorkel AI

OCTOBER 4, 2023

These large-scale neural networks are trained on vast amounts of data to address a wide number of tasks (i.e. Data teams can fine-tune LLMs like BERT, GPT-3.5 Improve auditing, model explainability, and iteration. natural language processing, image classification, question answering).

AI 59

AI AI Data Scientist Neural Network 59

Viso.ai

DECEMBER 27, 2023

In a computer vision example of contrast learning, we aim to train a tool like a convolutional neural network to bring similar image representations closer and separate the dissimilar ones. It typically uses a convolutional neural network (CNN) architecture, like ResNet , for extracting image features.

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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. Solution overview In this section, we present the overall workflow and explain the approach.

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Heartbeat

JULY 19, 2023

They are neural networks made to analyse data sequences like sentences or paragraphs and anticipate the following word or sentence. Hugging Face transformer models BERT, GPT-2, RoBERTa, and T5 are included in the library. To train the transformer model BERT, a massive corpus of text was used.

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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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ODSC - Open Data Science

JANUARY 29, 2024

Prompt Engineering Platforms LLM Platforms: ChatGpt, GPT-4, LLama 2, Stable Diffusion, and BERT ChatGPT OpenAI’s ChatGPT was one of the most popular apps in history, so it’s no surprise that the suite of API models including GPT-3.5 BERT While technically not an LLM (pre-dates LLMs), due to its 360 million parameters vs the (supposed) 1.76

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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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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.

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Heartbeat

AUGUST 2, 2023

Large language models, such as GPT-3 (Generative Pre-trained Transformer 3), BERT, XLNet, and Transformer-XL, etc., Transformer model used for Language-based tasks [ Source ] The encoder consists of a heap of identical layers, containing two sub-layers: a multi-head self-attention mechanism and a position-wise feed-forward neural network.

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Jay Alammar

OCTOBER 3, 2022

Let’s start to look under the hood because that helps explain the components, how they interact, and what the image generation options/parameters mean. In technical terms, this component is made up of a UNet neural network and a scheduling algorithm. Latent Diffusion Models EXPLAINED [Video] Stable Diffusion - What, Why, How?

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Mlearning.ai

FEBRUARY 8, 2023

for example, the attention-based models (Transformer, Bert, GPT3 ….) embedding the word with different meanings into different spaces ( source ) To prevent our network from being confused by the above case, we could use another network (e.g. In fact, there are several ways to extract the word repr.; during the embedding process.

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Viso.ai

DECEMBER 18, 2023

ONNX (Open Neural Network Exchange) is an open-source format that facilitates interoperability between different deep learning algorithms for simple model sharing and deployment. ONNX (Open Neural Network Exchange) is an open-source format. A deep learning framework from Microsoft. Apache MXNet. Apple Core ML.

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ML Review

DECEMBER 16, 2019

Source: Chami et al Chami et al present Hyperbolic Graph Convolutional Neural Networks (HGCN) and Liu et al propose Hyperbolic Graph Neural Networks (HGNN). The authors also outline that brute-force BERT decoding without semantic parsing works much worse, so use language models wisely ?‍

Machine Learning 52

Machine Learning Neural Network Explainability NLP 52

Heartbeat

SEPTEMBER 20, 2023

Object Detection Image from a personal computer Convolutional neural networks (CNNs) are utilized in object detection algorithms to identify and locate objects based on their visual attributes accurately. One common practice is to use recurrent neural networks (RNNs) or transformer models as language models.

Natural Language Processing 52

Natural Language Processing Computer Vision NLP Algorithm 52

Unite.AI

AUGUST 1, 2023

Prompt 1 : “Tell me about Convolutional Neural Networks.” ” Response 1 : “Convolutional Neural Networks (CNNs) are multi-layer perceptron networks that consist of fully connected layers and pooling layers. They are commonly used in image recognition tasks. .”

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Sebastian Ruder

AUGUST 15, 2021

NLP is all about pre-trained Transformers This should come as no surprise but it's still interesting to see that among the 14 "hot" topics of 2021 (see below) were five pre-trained models (BERT, RoBERTa, BART, GPT-2, XLM-R) and one general "Language models" topic. combine neural networks with a non-parametric memory.

NLP 52

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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.

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

Viso.ai

MARCH 26, 2024

Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. Furthermore, attention mechanisms work to enhance the explainability or interpretability of AI models. Vaswani et al.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Mlearning.ai

JUNE 8, 2023

Vector Embeddings for Developers: The Basics | Pinecone Used geometry concept to explain what is vector, and how raw data is transformed to embedding using embedding model. Pinecone Used a picture of phrase vector to explain vector embedding. What are Vector Embeddings? using its Spectrogram ).

Machine Learning 52

Machine Learning BERT Neural Network OpenAI 52

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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BERT NLP Machine Learning Natural Language Processing 52

AssemblyAI

JUNE 27, 2023

In this article, we take an overview of some exciting new advances in the space of Generative AI for audio that have all happened in the past few months , explaining where the key ideas come from and how they come together to bring audio generation to a new level. At its core, it's an end-to-end neural network-based approach.

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IBM Journey to AI blog

AUGUST 1, 2023

A foundation model is built on a neural network model architecture to process information much like the human brain does. BERT (Bi-directional Encoder Representations from Transformers) is one of the earliest LLM foundation models developed. An open-source model, Google created BERT in 2018.

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