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. One of the earliest representation models used in NLP was the Bag of Words (BoW) model.

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Analytics Vidhya

JUNE 5, 2023

These gargantuan neural networks have revolutionized how machines learn and generate human language, propelling the boundaries of what was once thought possible.

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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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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

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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Dlabs.ai

MAY 22, 2022

According to BCC research, the machine learning market will grow to $90.1 Given the data, it’s little surprise that many people want to learn more about AI and ML and, in turn, develop the necessary skills to become a machine learning engineer. billion by 2026 , an almost 40% uptick in five years.

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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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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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Mlearning.ai

JUNE 14, 2023

YouTube Introduction to Sequence Learning and Attention Mechanisms Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 — Translation, Seq2Seq, Attention — YouTube Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 7 — Translation, Seq2Seq, Attention — YouTube 2. Transformer Neural Networks — EXPLAINED!

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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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The MLOps Blog

JANUARY 25, 2023

Then using Machine Learning and Deep Learning sentiment analysis techniques, these businesses analyze if a customer feels positive or negative about their product so that they can make appropriate business decisions to improve their business.

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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 6, 2024

Transformers have transformed the field of NLP over the last few years, with LLMs like OpenAI’s GPT series, BERT, and Claude Series, etc. in 2017, marking a departure from the previous reliance on recurrent neural networks (RNNs) and convolutional neural networks (CNNs) for processing sequential data.

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Marktechpost

NOVEMBER 14, 2023

The research presents a study on simplifying transformer blocks in deep neural networks, specifically focusing on the standard transformer block. The study examines the simplification of transformer blocks in deep neural networks, focusing specifically on the standard transformer block.

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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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Viso.ai

MARCH 20, 2024

Models like GPT 4, BERT, DALL-E 3, CLIP, Sora, etc., Use Cases for Foundation Models Applications in Pre-trained Language Models like GPT, BERT, Claude, etc. With Viso Suite, enterprise teams can easily integrate the full machine learning pipeline into their workflows in a matter of days.

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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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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.

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ODSC - Open Data Science

SEPTEMBER 18, 2023

A lot goes into learning a new skill, regardless of how in-depth it is. Getting started with natural language processing (NLP) is no exception, as you need to be savvy in machine learning, deep learning, language, and more.

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ODSC - Open Data Science

JUNE 9, 2023

The 1970s introduced bell bottoms, case grammars, semantic networks, and conceptual dependency theory. In the 90’s we got grunge, statistical models, recurrent neural networks and long short-term memory models (LSTM). It uses a neural network to learn the vector representations of words from a large corpus of text.

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Artificial Corner

JULY 30, 2023

Working of Large Language Models (LLMs) Deep neural networks are used in Large language models to produce results based on patterns discovered from training data. LLM Development Vs Traditional Development No Machine Learning expertise is needed in LLM Development but it’s needed in Traditional Development.

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Bugra Akyildiz

DECEMBER 18, 2022

Brown researchers compared human visual recognition systems and Deep Neural Networks for what they believe where the important features of images are. Dragon is a new foundation model (improvement of BERT) that is pre-trained jointly from text and knowledge graphs for improved language, knowledge and reasoning capabilities.

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Heartbeat

FEBRUARY 7, 2023

image by rawpixel.com Understanding the concept of language models in natural language processing (NLP) is very important to anyone working in the Deep learning and machine learning space. They are essential to a variety of NLP activities, including speech recognition, machine translation, and text summarization.

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Marktechpost

DECEMBER 13, 2023

The well-known Large Language Models (LLMs) like GPT, BERT, PaLM, and LLaMA have brought in some great advancements in Natural Language Processing (NLP) and Natural Language Generation (NLG). Three types of graph-based applications, i.e., pure graphs, text-rich graphs, and text-paired graphs, have been associated with the integration of LLMs.

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Mlearning.ai

MARCH 30, 2023

Today in this blog we will learn about transfer learning and how can you implement it using TensorFlow. To train a machine learning model or a neural network that can yield the best results requires what? We can use TRANSFER LEARNING. What is Transfer Learning?

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Heartbeat

JUNE 28, 2023

Small-size IoT (Internet of Things) devices and light machine learning models are becoming increasingly popular due to the growing demand for connected devices and intelligent automation in various industries. In this article, we will explore about ALBERT ( A lite weighted version of BERT machine learning model) What is ALBERT?

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ML Review

DECEMBER 16, 2019

Machine Learning on Graphs becomes a first-class citizen at AI conferences while being not that mysterious as you might have imagined ?. Source: Chami et al Chami et al present Hyperbolic Graph Convolutional Neural Networks (HGCN) and Liu et al propose Hyperbolic Graph Neural Networks (HGNN). Have a look ?

Machine Learning 52

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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. Transformers and transfer-learning Natural Language Processing (NLP) systems face a problem known as the “knowledge acquisition bottleneck”. In this post we introduce our new wrapping library, spacy-transformers.

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AWS Machine Learning Blog

APRIL 3, 2024

The following is a high-level overview of how it works conceptually: Separate encoders – These models have separate encoders for each modality—a text encoder for text (for example, BERT or RoBERTa), image encoder for images (for example, CNN for images), and audio encoders for audio (for example, models like Wav2Vec).

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Google Research AI blog

FEBRUARY 7, 2023

In the last 10 years, AI and ML models have become bigger and more sophisticated — they’re deeper, more complex, with more parameters, and trained on much more data, resulting in some of the most transformative outcomes in the history of machine learning.

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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. Additionally, the course delves into efficient management of prompt libraries, a crucial skill for prompt engineering roles.

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Topbots

OCTOBER 4, 2023

In this article, we will review the key machine-learning techniques driving these two major classes of AI approaches, the unique benefits and challenges associated with them, and their respective real-world business applications. It is usually based on supervised learning, which is a type of machine learning that requires labeled data.

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AWS Machine Learning Blog

MARCH 20, 2023

Customers are always looking for ways to improve the performance and response times of their machine learning (ML) inference workloads without increasing the cost per transaction and without sacrificing the accuracy of the results. In the following example figure, we show INT8 inference performance in C6i for a BERT-base model.

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Marktechpost

JULY 4, 2023

Unigrams, N-grams, exponential, and neural networks are valid forms for the Language Model. Text with similar meanings can be clustered together without the use of pre-defined classes by employing clustering, an unsupervised learning technique. rely on Language Models as their foundation.

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Marktechpost

JULY 25, 2023

Researchers at Google and Osaka University use deep neural networks to generate music from features like fMRI scans by predicting high-level, semantically structured music. The music-generating model MusicLM consists of audio-derived embeddings named MuLan and w2v-BERT- avg.

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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

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Towards AI

OCTOBER 8, 2023

The main idea of BERTScore is to use a language model that is good at understanding text, like BERT, and use it to evaluate the similarity between two sentences, a Y in your test set and a Y’ representing the model-generated text. I use a BERT WordPiece tokenizer to generate IDs for each token of each sentence, generating a [40, 30523] array.

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