Becoming Human

MARCH 16, 2023

Photo by NASA on Unsplash Hello and welcome to this post, in which I will study a relatively new field in deep learning involving graphs — a very important and widely used data structure. This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Amazon Elastic Compute Cloud (Amazon EC2) DL2q instances, powered by Qualcomm AI 100 Standard accelerators, can be used to cost-efficiently deploy deep learning (DL) workloads in the cloud. This is a guest post by A.K Roy from Qualcomm AI. The cores are interconnected with a high-bandwidth low-latency network-on-chip (NoC) mesh.

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AWS Machine Learning Blog

JUNE 12, 2023

It can support a wide variety of use cases, including text classification, token classification, text generation, question and answering, entity extraction, summarization, sentiment analysis, and many more. It uses attention as the learning mechanism to achieve close to human-level performance.

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AWS Machine Learning Blog

OCTOBER 6, 2023

Background of multimodality models Machine learning (ML) models have achieved significant advancements in fields like natural language processing (NLP) and computer vision, where models can exhibit human-like performance in analyzing and generating content from a single source of data. In his spare time, he loves running and hiking.

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AWS Machine Learning Blog

APRIL 8, 2024

of Large Model Inference (LMI) Deep Learning Containers (DLCs). For the TensorRT-LLM container, we use auto. option.tensor_parallel_degree=max option.max_rolling_batch_size=32 option.rolling_batch=auto option.model_loading_timeout = 7200 We package the serving.properties configuration file in the tar.gz

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AWS Machine Learning Blog

MAY 2, 2023

With the ability to solve various problems such as classification and regression, XGBoost has become a popular option that also falls into the category of tree-based models. In this post, we dive deep to see how Amazon SageMaker can serve these models using NVIDIA Triton Inference Server. The outputs are then returned.

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Mlearning.ai

FEBRUARY 19, 2023

Along with text generation it can also be used to text classification and text summarization. Natural Language Processing (NLP) NLP is subset of Artificial Intelligence that is concerned with helping machines to understand the human language. The auto-complete feature on your smartphone is based on this principle.

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AWS Machine Learning Blog

MAY 31, 2024

It’s built on causal decoder-only architecture, making it powerful for auto-regressive tasks. The output shows the expected JSON file content, illustrating the model’s natural language processing (NLP) and code generation capabilities. trillion token dataset primarily consisting of web data from RefinedWeb with 11 billion parameters.

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AWS Machine Learning Blog

MARCH 2, 2023

Sentiment analysis and other natural language programming (NLP) tasks often start out with pre-trained NLP models and implement fine-tuning of the hyperparameters to adjust the model to changes in the environment. Hyperparameter optimization is highly computationally demanding for deep learning models. eks-create.sh

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Defined.ai blog

SEPTEMBER 21, 2023

That’s the power of Natural Language Processing (NLP) at work. In this exploration, we’ll journey deep into some Natural Language Processing examples , as well as uncover the mechanics of how machines interpret and generate human language. That’s precisely the challenge NLP tackles.

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John Snow Labs

MAY 24, 2023

Top Text Annotation Tools for NLP Each annotation tool has a specific purpose and functionality. NLP Lab is a Free End-to-End No-Code AI platform for document labeling and AI/ML model training. This is done by using state-of-the-art Spark NLP and Visual NLP pre-trained models or by tuning models to better handle specific use cases.

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AWS Machine Learning Blog

MARCH 1, 2023

Fine-tuning from pre-trained models using DreamBooth Fine-tuning is a process in deep learning where a pre-trained model is further trained on a new task using a small amount of labelled data. Hugging Face and Amazon introduced Hugging Face Deep Learning Containers (DLCs) to scale fine tuning tasks across multiple GPUs and nodes.

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Google Research AI blog

FEBRUARY 28, 2023

We also help make global conferences accessible to more researchers around the world, for example, by funding 24 students this year to attend Deep Learning Indaba in Tunisia. Dataset Description Auto-Arborist A multiview urban tree classification dataset that consists of ~2.6M Pfam-NUniProt2 A set of 6.8

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John Snow Labs

JUNE 27, 2023

Are you curious about the groundbreaking advancements in Natural Language Processing (NLP)? Prepare to be amazed as we delve into the world of Large Language Models (LLMs) – the driving force behind NLP’s remarkable progress. Ever wondered how machines can understand and generate human-like text?

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Viso.ai

DECEMBER 22, 2023

Its creators took inspiration from recent developments in natural language processing (NLP) with foundation models. This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. SAM’s game-changing impact lies in its zero-shot inference capabilities.

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Jay Alammar

DECEMBER 16, 2020

This article focuses on auto-regressive models, but these methods are applicable to other architectures and tasks as well. Multiple methods exist for assigning importance scores to the inputs of an NLP model. The literature is most often concerned with this application for classification tasks, rather than natural language generation.

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Mlearning.ai

MAY 23, 2023

Classification is very important in machine learning. Hence, we have various classification algorithms in machine learning like logistic regression, support vector machine, decision trees, Naive Bayes classifier, etc. What is deep learning? What is the difference between deep learning and machine learning?

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The MLOps Blog

FEBRUARY 27, 2023

Also, science projects around technologies like predictive modeling, computer vision, NLP, and several profiles like commercial proof of concepts and competitions workshops. When we speak about like NLP problems or classical ML problems with tabular data when the data can be spread in huge databases. With NLP, that’s not so easy.

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AWS Machine Learning Blog

JULY 16, 2024

It manages the availability and scalability of the Kubernetes control plane, and it provides compute node auto scaling and lifecycle management support to help you run highly available container applications. Amazon EKS is a managed Kubernetes service that makes it straightforward to run Kubernetes clusters on AWS.

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AWS Machine Learning Blog

JANUARY 9, 2023

For example, an image classification use case may use three different models to perform the task. The scatter-gather pattern allows you to combine results from inferences run on three different models and pick the most probable classification model. These endpoints are fully managed and support auto scaling.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

The system is further refined with DistilBERT , optimizing our dialogue-guided multi-class classification process. Utilizing the latest Hugging Face LLM modules on Amazon SageMaker, AWS customers can now tap into the power of SageMaker deep learning containers (DLCs).

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AWS Machine Learning Blog

JANUARY 17, 2024

Llama 2 is an auto-regressive generative text language model that uses an optimized transformer architecture. As a publicly available model, Llama 2 is designed for many NLP tasks such as text classification, sentiment analysis, language translation, language modeling, text generation, and dialogue systems.

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AWS Machine Learning Blog

AUGUST 8, 2023

Recent scientific breakthroughs in deep learning (DL), large language models (LLMs), and generative AI is allowing customers to use advanced state-of-the-art solutions with almost human-like performance. In this post, we show how to run multiple deep learning ensemble models on a GPU instance with a SageMaker MME.

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Unite.AI

MARCH 28, 2024

Let's start by understanding why LLM inference is so challenging compared to traditional NLP models. The Inference Challenge with Large Language Models Before the advent of LLMs, natural language processing relied on smaller models focused on specific tasks like text classification, named entity recognition, and sentiment analysis.

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