AI Weekly

JULY 27, 2023

techcrunch.com The Essential Artificial Intelligence Glossary for Marketers (90+ Terms) BERT - Bidirectional Encoder Representations from Transformers (BERT) is Google’s deep learning model designed explicitly for natural language processing tasks like answering questions, analyzing sentiment, and translation.

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Towards AI

JUNE 12, 2024

Photo by david clarke on Unsplash The most recent breakthroughs in language models have been the use of neural network architectures to represent text. Both BERT and GPT are based on the Transformer architecture. 2013 Word2Vec is a neural network model that uses n-grams by training on context windows of words.

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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. This architecture allows neural networks to learn complex patterns and relationships within data.

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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. 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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Marek Rei

NOVEMBER 4, 2024

I have written short summaries of 68 different research papers published in the areas of Machine Learning and Natural Language Processing. Gives a summary of different stages of this skill developing in humans, along with a review of this work in the deep learning field. University of Wisconsin-Madison.

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Flipboard

NOVEMBER 24, 2023

Researchers at ETH Zurich have developed a new technique that can significantly boost the speed of neural networks. They’ve demonstrated that altering the inference process can drastically cut down the computational requirements of these networks. In experiments conducted on BERT, a transformer …

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AssemblyAI

MAY 3, 2023

We are going to explore these and other essential questions from the ground up , without assuming prior technical knowledge in AI and machine learning. Artificial neural networks consist of interconnected layers of nodes, or “neurons” which work together to process and learn from data.

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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 helps data scientists, AI developers, and ML engineers enhance their skills through engaging learning experiences and practical exercises.

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ODSC - Open Data Science

MARCH 10, 2025

Over the past decade, data science has undergone a remarkable evolution, driven by rapid advancements in machine learning, artificial intelligence, and big data technologies. By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow.

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AWS Machine Learning Blog

AUGUST 2, 2024

GraphStorm is a low-code enterprise graph machine learning (GML) framework to build, train, and deploy graph ML solutions on complex enterprise-scale graphs in days instead of months. GraphStorm provides different ways to fine-tune the BERT models, depending on the task types. Dataset Num. of nodes Num. of edges Num.

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Unite.AI

DECEMBER 1, 2023

Machine learning , a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). The culmination of this training is a machine-learning model.

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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. Participants will learn about the applications of Generative AI and explore tools developed by Google to create their own AI-driven applications.

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Marktechpost

MARCH 27, 2024

These adapters allow BERT to be fine-tuned for specific downstream tasks while retaining most of its pre-trained parameters. These adapters allow BERT to be fine-tuned for specific downstream tasks while retaining most of its pre-trained parameters. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

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Unite.AI

DECEMBER 13, 2024

Large language models (LLMs) , such as GPT-4 , BERT , Llama , etc., Simple rule-based chatbots, for example, could only provide predefined answers and could not learn or adapt. With advancements in machine learning, dynamic memory became possible.

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Towards AI

MARCH 12, 2025

Normalization Trade-off: GPT models preserve formatting & nuance (more token complexity); BERT aggressively cleans text simpler tokens, reduced nuance, ideal for structured tasks. GPT typically preserves contractions, BERT-based models may split. Tokens: Fundamental unit that neural networks process. GPT-4 and GPT-3.5

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AssemblyAI

JUNE 27, 2023

Deep Neural Networks (DNNs) have proven to be exceptionally adept at processing highly complicated modalities like these, so it is unsurprising that they have revolutionized the way we approach audio data modeling. Traditional machine learning feature-based pipeline vs. end-to-end deep learning approach ( source ).

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Marktechpost

MAY 5, 2024

By leveraging a new data-dependent convolution layer, Orchid dynamically adjusts its kernel based on the input data using a conditioning neural network, allowing it to handle sequence lengths up to 131K efficiently. Compared to the BERT-base, the Orchid-BERT-base has 30% fewer parameters yet achieves a 1.0-point

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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. The deep aspect of DNNs comes from multiple hidden layers, which allow the network to learn and model complex patterns and relationships in data.

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IBM Journey to AI blog

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. A foundation model is built on a neural network model architecture to process information much like the human brain does.

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Pickl AI

JANUARY 26, 2025

Introduction Deep Learning models transform how we approach complex problems, offering powerful tools to analyse and interpret vast amounts of data. These models mimic the human brain’s neural networks, making them highly effective for image recognition, natural language processing, and predictive analytics.

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Unite.AI

AUGUST 2, 2023

Machine learning models have heavily relied on labeled data for training, and traditionally speaking, training models on labeled data yields accurate results. To tackle the annotation issue, developers came up with the concept of SSL or Self Supervised Learning. They require a high amount of computational power.

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Unite.AI

MAY 24, 2024

In modern machine learning and artificial intelligence frameworks, transformers are one of the most widely used components across various domains including GPT series, and BERT in Natural Language Processing, and Vision Transformers in computer vision tasks. So let’s get started. MambaOut: Is Mamba Really Needed for Vision?

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Towards AI

JUNE 3, 2024

BERT is a state-of-the-art algorithm designed by Google to process text data and convert it into vectors ([link]. What makes BERT special is, apart from its good results, the fact that it is trained over billions of records and that Hugging Face provides already a good battery of pre-trained models we can use for different ML tasks.

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Unite.AI

JULY 25, 2024

A significant breakthrough came with neural networks and deep learning. Models like Google's Neural Machine Translation (GNMT) and Transformer revolutionized language processing by enabling more nuanced, context-aware translations. IBM's Model 1 and Model 2 laid the groundwork for advanced systems.

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Marktechpost

NOVEMBER 15, 2023

In machine learning, a diffusion model is a generative model commonly used for image and audio generation tasks. This model consists of two primary modules: A pre-trained BERT model is employed to extract pertinent information from the input text, and A diffusion UNet model processes the output from BERT.

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ODSC - Open Data Science

MARCH 12, 2025

The Rise of AI Engineering andMLOps 20182019: Early discussions around MLOps and AI engineering were sparse, primarily focused on general machine learning best practices. MLOps emerged as a necessary discipline to address the challenges of deploying and maintaining machine learning models in production environments.

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NVIDIA

FEBRUARY 11, 2025

They said transformer models , large language models (LLMs), vision language models (VLMs) and other neural networks still being built are part of an important new category they dubbed foundation models. Earlier neural networks were narrowly tuned for specific tasks.

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Marktechpost

NOVEMBER 19, 2024

Deploying machine learning models on edge devices poses significant challenges due to limited computational resources. These enhancements were consistent across models, ranging from transformer-based language models like BERT and GPT-NEOX to vision models like VGG. Check out the Paper.

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ODSC - Open Data Science

JUNE 6, 2023

Be sure to check out their talk, “ Getting Up to Speed on Real-Time Machine Learning ,” there! The benefits of real-time machine learning are becoming increasingly apparent. Anomaly detection, including fraud detection and network intrusion monitoring, particularly exemplifies the challenges of real-time machine learning.

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Unite.AI

DECEMBER 27, 2023

By utilizing machine learning algorithms , it produces new content, including images, text, and audio, that resembles existing data. Facebook's RoBERTa, built on the BERT architecture, utilizes deep learning algorithms to generate text based on given prompts.

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Mlearning.ai

APRIL 8, 2023

SOTA (state-of-the-art) in machine learning refers to the best performance achieved by a model or system on a given benchmark dataset or task at a specific point in time. The earlier models that were SOTA for NLP mainly fell under the traditional machine learning algorithms. Citation: Article from IBM archives 2.

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AWS Machine Learning Blog

FEBRUARY 6, 2024

To support overarching pharmacovigilance activities, our pharmaceutical customers want to use the power of machine learning (ML) to automate the adverse event detection from various data sources, such as social media feeds, phone calls, emails, and handwritten notes, and trigger appropriate actions.

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Marktechpost

JANUARY 15, 2025

Traditional machine learning models, while effective in many scenarios, often struggle to process high-dimensional and unstructured data without extensive preprocessing and feature engineering. This gap has led to the evolution of deep learning models, designed to learn directly from raw data. What is Deep Learning?

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ODSC - Open Data Science

FEBRUARY 17, 2023

Knowing how spaCy works means little if you don’t know how to apply core NLP skills like transformers, classification, linguistics, question answering, sentiment analysis, topic modeling, machine translation, speech recognition, named entity recognition, and others.

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Marktechpost

APRIL 25, 2024

Parameter In the context of neural networks, including LLMs, a parameter is a variable part of the model’s architecture learned from the training data. Parameters (like weights in neural networks) are adjusted during training to reduce the difference between predicted and actual output.

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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 ).

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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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