Pickl AI

JANUARY 26, 2025

Summary: Deep Learning models revolutionise data processing, solving complex image recognition, NLP, and analytics tasks. Introduction Deep Learning models transform how we approach complex problems, offering powerful tools to analyse and interpret vast amounts of data. With a projected market growth from USD 6.4

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Marktechpost

MAY 5, 2024

In deep learning, especially in NLP, image analysis, and biology, there is an increasing focus on developing models that offer both computational efficiency and robust expressiveness. The ever-increasing need for processing larger and more complex datasets has driven researchers to find more efficient and scalable solutions.

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Unite.AI

SEPTEMBER 20, 2023

These are deep learning models used in NLP. This discovery fueled the development of large language models like ChatGPT. Large language models or LLMs are AI systems that use transformers to understand and create human-like text.

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Towards AI

OCTOBER 19, 2024

Photo by Amr Taha™ on Unsplash In the realm of artificial intelligence, the emergence of transformer models has revolutionized natural language processing (NLP). In this guide, we will explore how to fine-tune BERT, a model with 110 million parameters, specifically for the task of phishing URL detection.

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BERT Natural Language Processing Deep Learning Machine Learning 105

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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Marktechpost

JANUARY 15, 2025

These limitations are particularly significant in fields like medical imaging, autonomous driving, and natural language processing, where understanding complex patterns is essential. 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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Analytics Vidhya

FEBRUARY 4, 2023

One of the most promising areas within AI in healthcare is Natural Language Processing (NLP), which has the potential to revolutionize patient care by facilitating more efficient and accurate data analysis and communication.

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Marktechpost

AUGUST 7, 2023

Language model pretraining has significantly advanced the field of Natural Language Processing (NLP) and Natural Language Understanding (NLU). Models like GPT, BERT, and PaLM are getting popular for all the good reasons. Models like GPT, BERT, and PaLM are getting popular for all the good reasons.

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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 201

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Unite.AI

JULY 11, 2023

Introduction To Image Generation Image Source Course difficulty: Beginner-level Completion time: ~ 1 day (Complete the quiz/lab in your own time) Prerequisites: Knowledge of ML, Deep Learning (DL), Convolutional Neural Nets (CNNs), and Python programming. What will AI enthusiasts learn? What will AI enthusiasts learn?

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AWS Machine Learning Blog

MAY 15, 2024

Bfloat16 accelerated SGEMM kernels and int8 MMLA accelerated Quantized GEMM (QGEMM) kernels in ONNX have improved inference performance by up to 65% for fp32 inference and up to 30% for int8 quantized inference for several natural language processing (NLP) models on AWS Graviton3-based Amazon Elastic Compute Cloud (Amazon EC2) instances.

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Unite.AI

SEPTEMBER 25, 2023

However, as technology advanced, so did the complexity and capabilities of AI music generators, paving the way for deep learning and Natural Language Processing (NLP) to play pivotal roles in this tech. Today platforms like Spotify are leveraging AI to fine-tune their users' listening experiences.

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Unite.AI

SEPTEMBER 18, 2023

Artificial Intelligence is a very vast branch in itself with numerous subfields including deep learning, computer vision , natural language processing , and more. Another subfield that is quite popular amongst AI developers is deep learning, an AI technique that works by imitating the structure of neurons.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

In today’s rapidly evolving landscape of artificial intelligence, deep learning models have found themselves at the forefront of innovation, with applications spanning computer vision (CV), natural language processing (NLP), and recommendation systems. device batch = [t.to(device) device batch = [t.to(device)

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Unite.AI

MAY 29, 2024

With nine times the speed of the Nvidia A100, these GPUs excel in handling deep learning workloads. This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction.

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ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! If a Natural Language Processing (NLP) system does not have that context, we’d expect it not to get the joke. In this post, I’ll be demonstrating two deep learning approaches to sentiment analysis. deep” architecture).

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IBM Journey to AI blog

MARCH 27, 2024

However, while spend-based commodity-class level data presents an opportunity to help address the difficulties associates with Scope 3 emissions accounting, manually mapping high volumes of financial ledger entries to commodity classes is an exceptionally time-consuming, error-prone process. This is where LLMs come into play.

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Towards AI

DECEMBER 28, 2023

The advent of more powerful personal computers paved the way for the gradual acceptance of deep learning-based methods. The introduction of attention mechanisms has notably altered our approach to working with deep learning algorithms, leading to a revolution in the realms of computer vision and natural language processing (NLP).

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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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Neural Network Convolutional Neural Networks BERT Deep Learning 158

Probably Approximately a Scientific Blog

JANUARY 12, 2021

In the last 5 years, popular media has made it seem that AI is nearly if not already solved by deep learning, with reports on super-human performance on speech recognition, image captioning, and object recognition. Figure 1: adversarial examples in computer vision (left) and natural language processing tasks (right).

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ODSC - Open Data Science

FEBRUARY 17, 2023

Natural language processing (NLP) has been growing in awareness over the last few years, and with the popularity of ChatGPT and GPT-3 in 2022, NLP is now on the top of peoples’ minds when it comes to AI. BERT even accounts for the context of words, allowing for more accurate results related to respective queries and tasks.

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NVIDIA Developer

JUNE 2, 2021

Applications for natural language processing (NLP) have exploded in the past decade. Modern techniques can capture the nuance, context, and sophistication of language, just as humans do. Fundamental understanding of a deep learning framework such as TensorFlow, PyTorch, or Keras.

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AssemblyAI

MAY 3, 2023

The core process is a general technique known as self-supervised learning , a learning paradigm that leverages the inherent structure of the data itself to generate labels for training. Transfer learning allows a model to leverage the knowledge gained from one task and apply it to another, often with minimal additional training.

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Marktechpost

NOVEMBER 10, 2024

TextBlob A popular Python sentiment analysis toolkit, TextBlob is praised for its ease of use and adaptability while managing natural language processing (NLP) workloads. spaCy A well-known open-source natural language processing package, spaCy is praised for its robustness and speed while processing massive amounts of text.

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Flipboard

JUNE 20, 2023

We present the results of recent performance and power draw experiments conducted by AWS that quantify the energy efficiency benefits you can expect when migrating your deep learning workloads from other inference- and training-optimized accelerated Amazon Elastic Compute Cloud (Amazon EC2) instances to AWS Inferentia and AWS Trainium.

Machine Learning 123

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Marktechpost

DECEMBER 5, 2023

Large Language Models (LLMs), like GPT, PaLM, LLaMA, etc., Their ability to utilize the strength of Natural Language Processing, Generation, and Understanding by generating content, answering questions, summarizing text, and so on have made LLMs the talk of the town in the last few months. What are Small Language Models?

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Marktechpost

APRIL 16, 2024

In recent years, there has been a great inclination toward Large Language Models (LLMs) due to their amazing text generation, analysis, and classification capabilities. These models use billions of parameters to execute a variety of Natural Language Processing (NLP) tasks.

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Unite.AI

OCTOBER 22, 2024

This term refers to how much time, memory, or processing power an algorithm requires as the size of the input grows. In AI, particularly in deep learning , this often means dealing with a rapidly increasing number of computations as models grow in size and handle larger datasets.

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Artificial Corner

JULY 30, 2023

So that’s why I tried in this article to explain LLM in simple or to say general language. Photo by Shubham Dhage on Unsplash Introduction Large language Models (LLMs) are a subset of Deep Learning. Natural Language Processing (NLP) is a subfield of artificial intelligence.

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Marktechpost

JULY 22, 2024

Authorship Verification (AV) is critical in natural language processing (NLP), determining whether two texts share the same authorship. With deep learning models like BERT and RoBERTa, the field has seen a paradigm shift. For example, InstructAV with LLaMA-2-7B achieved an accuracy of 91.4%

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Marktechpost

MARCH 20, 2025

Let’s create a small dataset of abstracts from various fields: Copy Code Copied Use a different Browser abstracts = [ { "id": 1, "title": "Deep Learning for Natural Language Processing", "abstract": "This paper explores recent advances in deep learning models for natural language processing tasks.

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Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. Deep learning-based models, especially CNNs, have revolutionized feature extraction in image captioning.

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DataRobot Blog

APRIL 21, 2022

Machine learning, and especially deep learning, has become increasingly more accurate in the past few years. In the graph below, borrowed from the same article, you can see how some of the most cutting-edge algorithms in deep learning have increased in terms of model size over time.

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Unite.AI

AUGUST 28, 2023

With advancements in deep learning, natural language processing (NLP), and AI, we are in a time period where AI agents could form a significant portion of the global workforce. Deep learning techniques further enhanced this, enabling sophisticated image and speech recognition.

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ODSC - Open Data Science

MARCH 10, 2025

By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow. The Deep Learning Boom (20182019) Between 2018 and 2019, deep learning dominated the conference landscape.

Data Science 52

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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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IBM Journey to AI blog

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” They are now capable of natural language processing ( NLP ), grasping context and exhibiting elements of creativity.

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