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model-inference

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brian-kipkoech-tanui / sagemaker-ML-workflow

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Image Classifiers are used in the field of computer vision to identify the content of an image and it is used across a broad variety of industries, from advanced technologies like autonomous vehicles and augmented reality, to eCommerce platforms, and even in diagnostic medicine.

python aws json aws-lambda aws-s3 image-classification aws-ec2 endpoint model-evaluation aws-step-functions sagemaker sagemaker-deployment model-testing aws-statemachine sagemaker-studio model-inference
  • Updated Mar 1, 2023
  • HTML

ChaitanyaC22 / Udacity-AWS-MLE-ND-Project2-Build-a-ML-Workflow-For-Scones-Unlimited-On-Amazon-SageMaker

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The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.

python aws json aws-lambda deployment aws-s3 lambda-functions python3 image-classification aws-ec2 aws-iam endpoint model-evaluation aws-step-functions sagemaker-deployment aws-sagemaker model-testing aws-statemachine sagemaker-studio model-inference
  • Updated Jan 24, 2023
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SayamAlt / Global-News-Headlines-Text-Summarization

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Successfully established a text summarization model using Seq2Seq modeling with Luong Attention, which can give a short and concise summary of the global news headlines.

natural-language-processing text-generation text-summarization attention-mechanism seq2seq-model luong-attention text-tokenization model-inference model-architecture-and-implementation data-exploration-and-preprocessing
  • Updated May 6, 2024
  • Jupyter Notebook

SayamAlt / English-to-Spanish-Language-Translation-using-Seq2Seq-and-Attention

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Successfully established a Seq2Seq with attention model which can perform English to Spanish language translation up to an accuracy of almost 97%.

natural-language-processing language-translation exploratory-data-analysis text-generation neural-machine-translation attention-model attention-is-all-you-need text-preprocessing luong-attention text-tokenization seq2seq-modeling fine-tuning-bert bert-transformer hugging-face-transformers model-inference model-architecture-and-implementation model-training-and-evaluation
  • Updated May 6, 2024
  • Jupyter Notebook

GauravG-20 / Udacity-AWS-MLE-ND-Project2-Build-a-ML-Workflow-For-Scones-Unlimited-On-Amazon-SageMaker

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The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.

python aws json aws-lambda aws-s3 jupyter-notebook udacity-nanodegree udacity-scholarship-course sagemaker-deployment aws-sagemaker sagemaker-studio model-inference aws-state-machine aws-endpoint udacity-machine-learning-fundamentals
  • Updated Oct 10, 2023
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SayamAlt / Symptoms-Disease-Text-Classification

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Successfully developed a fine-tuned BERT transformer model which can accurately classify symptoms to their corresponding diseases upto an accuracy of 89%.

natural-language-processing text-classification exploratory-data-analysis multiclass-classification text-preprocessing text-tokenization bert-fine-tuning hugging-face-transformers fine-tune-bert-tensorflow model-inference model-architecture-and-implementation model-training-and-evaluation data-exploration-and-preprocessing
  • Updated May 6, 2024
  • Jupyter Notebook

SayamAlt / Financial-News-Sentiment-Analysis

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Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.

natural-language-processing sentiment-analysis multiclass-classification text-preprocessing text-tokenization distilbert-model hugging-face-transformers fine-tune-bert-tensorflow model-inference model-architecture-and-implementation model-training-and-evaluation data-exploration-and-preprocessing
  • Updated May 6, 2024
  • Jupyter Notebook

vinit714 / --Deep-Learning-for-Fashion-MNIST--Accessory-Classification-Project

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This repository contains Python code to classify fashion items using a Convolutional Neural Network (CNN) implemented with TensorFlow and Keras. It includes data preprocessing, model building, training, evaluation, and visualization of results.

visualization cnn data-preprocessing evaluation-metrics data-augmentation normalization fashion-mnist model-inference
  • Updated Feb 16, 2024
  • Jupyter Notebook

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