Data Science Dojo

JUNE 14, 2023

You must have heard the name GPT if you are interested in text processing. GPT is one of the most popular machine-learning models used for text processing. I t belongs to a class of models called “ Transformers ” which are classified among deep learning models. And that was just one model.

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Data Science Dojo

JUNE 14, 2023

You must have heard the name GPT if you are interested in text processing. GPT is one of the most popular machine-learning models used for text processing. I t belongs to a class of models called “ Transformers ” which are classified among deep learning models. And that was just one model.

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Flipboard

MARCH 27, 2023

In this series, you will learn about Accelerating Deep Learning Models with PyTorch 2.0. This lesson is the 1st of a 2-part series on Accelerating Deep Learning Models with PyTorch 2.0 : What’s New in PyTorch 2.0? TorchDynamo and TorchInductor To learn what’s new in PyTorch 2.0, via its beta release.

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PyImageSearch

SEPTEMBER 23, 2024

Home Table of Contents Deploying a Vision Transformer Deep Learning Model with FastAPI in Python What Is FastAPI? You’ll learn how to structure your project for efficient model serving, implement robust testing strategies with PyTest, and manage dependencies to ensure a smooth deployment process. Testing main.py

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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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Dataconomy

FEBRUARY 21, 2024

Choose Your Framework & Environment Flexibility is key : Google Gemma AI works seamlessly with popular deep learning frameworks like JAX, PyTorch, and Keras 3.0 TensorFlow backend).

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PyImageSearch

JANUARY 2, 2023

This blog will cover the benefits, applications, challenges, and tradeoffs of using deep learning in healthcare. Computer Vision and Deep Learning for Healthcare Benefits Unlocking Data for Health Research The volume of healthcare-related data is increasing at an exponential rate.

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AWS Machine Learning Blog

OCTOBER 5, 2023

First, download the Llama 2 model and training datasets and preprocess them using the Llama 2 tokenizer. For detailed guidance of downloading models and the argument of the preprocessing script, refer to Download LlamaV2 dataset and tokenizer. He focuses on developing scalable machine learning algorithms.

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Applied Data Science

AUGUST 2, 2021

Download the free, unabridged version here. They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. Download the free, unabridged version here.

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Flipboard

JULY 10, 2023

Figure 5: Architecture of Convolutional Autoencoder for Image Segmentation (source: Bandyopadhyay, “Autoencoders in Deep Learning: Tutorial & Use Cases [2023],” V7Labs , 2023 ). time series or natural language processing tasks). This architecture is well-suited for handling sequential data (e.g.,

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PyImageSearch

JANUARY 1, 2024

pathlib and textwrap are for file and text manipulation, google.generativeai (aliased as genai ) is the main module for AI functionalities, and PIL.Image and urllib.request are for handling and downloading images. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

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AWS Machine Learning Blog

JUNE 13, 2023

In this post, we demonstrate how to deploy Falcon for applications like language understanding and automated writing assistance using large model inference deep learning containers on SageMaker. SageMaker large model inference (LMI) deep learning containers (DLCs) can help. amazonaws.com/djl-inference:0.22.1-deepspeed0.8.3-cu118"

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Dataconomy

JULY 12, 2023

The ChatGPT language model, which is supported by the GPT-3.5 It is able to comprehend the context and deliver responses that are human-like thanks to its natural language processing abilities. This adaptable AI program can write blog posts, articles, and social media captions, among other types of material.

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AWS Machine Learning Blog

JUNE 11, 2024

First, we started by benchmarking our workloads using the readily available Graviton Deep Learning Containers (DLCs) in a standalone environment. In our test environment, we observed 20% throughput improvement and 30% latency reduction across multiple natural language processing models.

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ODSC - Open Data Science

APRIL 3, 2023

Question Answering is the task in Natural Language Processing that involves answering questions posed in natural language. candidate in Machine Learning & Natural Language Processing at UKP Lab in TU Darmstadt, supervised by Prof. Don’t worry, you’re not alone! Iryna Gurevych.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

of Large Model Inference (LMI) Deep Learning Containers (DLCs) and adds support for NVIDIA’s TensorRT-LLM Library. This file contains the required configurations for the Deep Java Library (DJL) model server to download and host the model. The task parameter is used to define the natural language processing (NLP) task.

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Pickl AI

OCTOBER 16, 2024

Summary: TensorFlow is an open-source Deep Learning framework that facilitates creating and deploying Machine Learning models. Introduction TensorFlow supports various platforms and programming languages , making it a popular choice for developers. It’s an open-source Deep Learning framework developed by Google.

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Towards AI

JUNE 27, 2023

For instance, today’s machine learning tools are pushing the boundaries of natural language processing, allowing AI to comprehend complex patterns and languages. However, the rapid evolution of these machine learning tools also presents a challenge for developers.

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AWS Machine Learning Blog

MAY 1, 2024

AWS Trainium instances for training workloads SageMaker ml.trn1 and ml.trn1n instances, powered by Trainium accelerators, are purpose-built for high-performance deep learning training and offer up to 50% cost-to-train savings over comparable training optimized Amazon Elastic Compute Cloud (Amazon EC2) instances.

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ODSC - Open Data Science

JUNE 29, 2023

Let’s download the dataframe with: import pandas as pd df_target = pd.read_parquet("[link] /Listings/airbnb_listings_target.parquet") Let’s simulate a scenario where we want to assert the quality of a batch of production data. The used dataset was adapted from the inside Airbnb project.

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AWS Machine Learning Blog

APRIL 1, 2024

In this blog post, AWS collaborates with Meta’s PyTorch team to discuss how to use the PyTorch FSDP library to achieve linear scaling of deep learning models on AWS seamlessly using Amazon EKS and AWS Deep Learning Containers (DLCs). Alex Iankoulski is a Principal Solutions Architect, Self-managed Machine Learning at AWS.

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AWS Machine Learning Blog

JULY 2, 2024

the optimizations are available in torch Python wheels and AWS Graviton PyTorch deep learning container (DLC). Starting with PyTorch 2.3.1, Please see the Running an inference section that follows for the instructions on installation, runtime configuration, and how to run the tests.

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AWS Machine Learning Blog

MAY 4, 2023

You can use ml.trn1 and ml.inf2 compatible AWS Deep Learning Containers (DLCs) for PyTorch, TensorFlow, Hugging Face, and large model inference (LMI) to easily get started. For the full list with versions, see Available Deep Learning Containers Images. petaflops of FP16/BF16 compute power.

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AWS Machine Learning Blog

JUNE 6, 2023

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. These are basically big models based on deep learning techniques that are trained with hundreds of billions of parameters.

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Towards AI

AUGUST 28, 2023

Apply these concepts to solve real-world industry problems in deep learning Taking a step away from classical machine learning (ML), embeddings are at the core of most deep learning (DL) use cases. You can download the images here [4]. You can download the data here (product images by [5]).

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Heartbeat

APRIL 23, 2023

One of the key components of chatbot development is natural language processing (NLP), which allows the bot to understand and respond to human language. It provides a range of features for processing and analyzing text data. We can then use SpaCy to build the chatbot’s natural language processing capabilities.

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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.

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Mlearning.ai

FEBRUARY 9, 2023

AI tools, such as ChatGPT and DALL-E, are developed with deep learning techniques. Deep learning is a subfield of AI that aims to extract knowledge from data through complex neural networks. Performing deep learning projects is difficult. Building a deep learning model takes both money and time.

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AWS Machine Learning Blog

OCTOBER 19, 2023

Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII). Download the SageMaker Data Wrangler flow.

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Pickl AI

DECEMBER 9, 2024

Practical projects and hands-on learning are crucial for mastery. Key areas include NLP, computer vision, and Deep Learning. What is AI and Machine Learning? Artificial Intelligence (AI) is the simulation of human intelligence in machines programmed to think, learn, and solve problems.

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AWS Machine Learning Blog

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. With the DJL, integrating this deep learning is simple. Business requirements We are the US squad of the Sportradar AI department. The architecture of DJL is engine agnostic.

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Applied Data Science

DECEMBER 23, 2022

In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. It is not surprising that it has become a major application area for deep learning.

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AWS Machine Learning Blog

APRIL 25, 2024

AWS and Hugging Face have a partnership that allows a seamless integration through SageMaker with a set of AWS Deep Learning Containers (DLCs) for training and inference in PyTorch or TensorFlow, and Hugging Face estimators and predictors for the SageMaker Python SDK. and requirements.txt files and save it as model.tar.gz : !

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Heartbeat

JUNE 6, 2023

In recent years, researchers have also explored using GCNs for natural language processing (NLP) tasks, such as text classification , sentiment analysis , and entity recognition. Once the GCN is trained, it is easier to process new graphs and make predictions about them. Download the Cora dataset here.

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PyImageSearch

FEBRUARY 19, 2024

Downloading [link] to./data/cifar-10-python.tar.gz The logs indicate that the CIFAR-10 dataset was successfully downloaded, extracted, and verified. This process involves retrieving the dataset from an online source, saving it locally, and then extracting the contents for use in the model training and testing. 0.5), (0.5,

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AWS Machine Learning Blog

MAY 31, 2023

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. For a list of NVIDIA Triton Deep Learning Containers (DLCs) supported by SageMaker inference, refer to Available Deep Learning Containers Images.

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AWS Machine Learning Blog

JUNE 9, 2023

ONNX ( Open Neural Network Exchange ) is an open-source standard for representing deep learning models widely supported by many providers. ONNX provides tools for optimizing and quantizing models to reduce the memory and compute needed to run machine learning (ML) models. For more details, refer to CreateEndpointConfig.

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