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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Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

Machine Learning 204

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 204

Marktechpost

APRIL 21, 2024

Introduction to Generative AI: This course provides an introductory overview of Generative AI, explaining what it is and how it differs from traditional machine learning methods. It introduces learners to responsible AI and explains why it is crucial in developing AI systems.

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Marktechpost

MAY 30, 2024

Google plays a crucial role in advancing AI by developing cutting-edge technologies and tools like TensorFlow, Vertex AI, and BERT. It covers how to develop NLP projects using neural networks with Vertex AI and TensorFlow. It also includes guidance on using Google Tools to develop your own Generative AI applications.

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Marktechpost

SEPTEMBER 30, 2024

While the Adam optimizer has become the standard for training Transformers, stochastic gradient descent with momentum (SGD), which is highly effective for convolutional neural networks (CNNs), performs worse on Transformer models. This performance gap poses a challenge for researchers. Check out the Paper.

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Neural Network Convolutional Neural Networks BERT Explainability 59

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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Neural Network Convolutional Neural Networks BERT Deep Learning 158

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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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

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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.

Explainability 52

Explainability Neural Network Deep Learning Machine Learning 52

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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LLM Machine Learning Explainability Algorithm 264

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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Prompt Engineering Prompt Engineer ChatGPT Convolutional Neural Networks 246

Towards AI

SEPTEMBER 26, 2024

The code imports various libraries like TensorFlow, PyTorch, Transformers, Tkinter, and CLIP to handle tasks related to neural networks, text classification, and image processing. Featured Community post from the Discord Mahvin_ built a chatbot using ChatGPT. You can try it on GitHub and share your feedback in the Discord thread!

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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.

Explainability 52

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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.

Natural Language Processing 116

Natural Language Processing Categorization Neural Network NLP 116

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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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

ODSC - Open Data Science

NOVEMBER 19, 2024

His session explained how containers can simplify the deployment of data applications by bundling dependencies and configurations into portable images. By focusing on complex domains like weather forecasting, aerodynamics, and material sciences, the session highlighted how neural operators can transform scientific models and simulations.

Deep Learning 52

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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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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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Large Language Models LLM Natural Language Processing Deep Learning 85

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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Large Language Models BERT Natural Language Processing Categorization 105

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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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 Deep Learning 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

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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

Jay Alammar

DECEMBER 16, 2020

input saliency is a method that explains individual predictions. This is a method of attribution explaining the relationship between a model's output and inputs -- helping us detect errors and biases, and better understand the behavior of the system. Interfaces for Explaining Transformer Language Models [Blog post].

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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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Snorkel AI

MARCH 1, 2023

Model architectures that qualify as “supervised learning”—from traditional regression models to random forests to most neural networks—require labeled data for training. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

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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

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.

Neural Network 52

Neural Network ChatGPT Machine Learning Deep Learning 52

Marek Rei

NOVEMBER 7, 2024

A recent survey paper by Calderon and Reichart [10] found that less than 10% of NLP interpretability papers consider inherently interpretable self-explaining models, with the authors advocating for more research on causal based interpretability methods. In contrast, other interpretable methods for SNLI show considerably worse performance.

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Explainability BERT NLP Large Language Models 90

Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. Institutions widely use machine learning models like Random Forest, neural networks, and anomaly detection algorithms. 1: Fraud Detection and Prevention No.2:

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Google Research AI blog

FEBRUARY 10, 2023

We also had a number of interesting results on graph neural networks (GNN) in 2022. Furthermore, to bring some of these many advances to the broader community, we had three releases of our flagship modeling library for building graph neural networks in TensorFlow (TF-GNN).

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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

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Mlearning.ai

JANUARY 17, 2024

Major milestones in the last few years comprised BERT (Google, 2018), GPT-3 (OpenAI, 2020), Dall-E (OpenAI, 2021), Stable Diffusion (Stability AI, LMU Munich, 2022), ChatGPT (OpenAI, 2022). Complex ML problems can only be solved in neural networks with many layers. Deep learning neural network.

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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.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Neural Network 52

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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